# Dataless: No Data? No Problem.

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We started Cal Hacks tossing around ideas, came to the idea of providing smartphones with internet access through a solely SMS-based platform, and never turned back. To start, we quickly tested out some ideas (Android SMS send/receive, Twilio messaging, Amazon EC2) and found out they all individually worked. Initiate #DanceParty. It was time to get to work.

So here's the Android app: An internet portal using **no** cellular data, only SMS text messages. Access convenient services such as Wikipedia, Bing Search, USDA Nutritional Information, Weather, etc... using only SMS.

## Motivation

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You own a smartphone with an SMS plan, but no data plan. How can you stay connected to the information of the modern world?

4.5 billion people lack internet access worldwide, yet smartphone ownership is growing faster than ever throughout even the most destitute of geographic locations. A vast number of these new smartphone users, however, lack access to data either because of financial constraints or a lack of geographically-available, data-enabling infrastructure. With an awareness of the power that connectivity and information has on empowering all realms of life, we wanted to create a platform that could provide data-less smartphone users with the power of information. Once we realized what that platform was, the inevitable dance party commenced before jumping into the world of hacking.
## Inspiration

With the excitement of blockchain and the ever growing concerns regarding privacy, we wanted to disrupt one of the largest technology standards yet: Email. Email accounts are mostly centralized and contain highly valuable data, making one small breach, or corrupt act can serious jeopardize millions of people. The solution, lies with the blockchain. Providing encryption and anonymity, with no chance of anyone but you reading your email.

Our technology is named after Soteria, the goddess of safety and salvation, deliverance, and preservation from harm, which we believe perfectly represents our goals and aspirations with this project.

## What it does

First off, is the blockchain and message protocol. Similar to PGP protocol it offers \_ security \_, and \_ anonymity \_, while also **ensuring that messages can never be lost**. On top of that, we built a messenger application loaded with security features, such as our facial recognition access option. The only way to communicate with others is by sharing your 'address' with each other through a convenient QRCode system. This prevents anyone from obtaining a way to contact you without your **full discretion**, goodbye spam/scam email.

## How we built it

First, we built the block chain with a simple Python Flask API interface. The overall protocol is simple and can be built upon by many applications. Next, and all the remained was making an application to take advantage of the block chain. To do so, we built a React-Native mobile messenger app, with quick testing though Expo. The app features key and address generation, which then can be shared through QR codes so we implemented a scan and be scanned flow for engaging in communications, a fully consensual agreement, so that not anyone can message anyone. We then added an extra layer of security by harnessing Microsoft Azures Face API cognitive services with facial recognition. So every time the user opens the app they must scan their face for access, ensuring only the owner can view his messages, if they so desire.

## Challenges we ran into

Our biggest challenge came from the encryption/decryption process that we had to integrate into our mobile application. Since our platform was react native, running testing instances through Expo, we ran into many specific libraries which were not yet supported by the combination of Expo and React. Learning about cryptography and standard practices also played a major role and challenge as total security is hard to find.

## Accomplishments that we're proud of

We are really proud of our blockchain for its simplicity, while taking on a huge challenge. We also really like all the features we managed to pack into our app. None of us had too much React experience but we think we managed to accomplish a lot given the time. We also all came out as good friends still, which is a big plus when we all really like to be right :)

## What we learned

Some of us learned our appreciation for React Native, while some learned the opposite. On top of that we learned so much about security, and cryptography, and furthered our beliefs in the power of decentralization.

## What's next for The Soteria Network

Once we have our main application built we plan to start working on the tokens and distribution. With a bit more work and adoption we will find ourselves in a very possible position to pursue an ICO. This would then enable us to further develop and enhance our protocol and messaging app. We see lots of potential in our creation and believe privacy and consensual communication is an essential factor in our ever increasingly social networking world.
## Inspiration

The inspiration behind GeneLevel sprang from the realization that the one-size-fits-all dietary guidelines fail to account for individual genetic variations affecting nutrient metabolism. This gap in personalized nutrition sparked our ambition to tailor dietary plans right down to the genetic level, ensuring everyone can eat precisely what their body needs for optimal health.

## What it does

GeneLevel revolutionizes dietary planning by integrating advanced genomic data analysis with machine learning to identify upregulated or downregulated marker genes indicative of an individual's unique nutritional requirements. Our platform categorizes users into optimal dietary plans, such as high-fat or low-fat diets, based on their blood mRNA expression data and questionnaire responses, providing personalized menu plans that promote physical and cognitive well-being.

## How we built it

We harnessed a combination of Gene Set Enrichment Analysis (GSEA) and machine learning algorithms to sift through genomic data, identifying key nutritional markers. The frontend was crafted with modern web technologies to make genomic data accessible, while the backend ML models were trained on rich datasets to ensure accurate dietary classifications.

## Challenges we ran into

1. **Data Collection**: Amassing a comprehensive and varied dataset of blood mRNA expression data was a significant hurdle, critical for the model's training and validation phases.
2. **Accuracy of the ML Model**: Achieving high accuracy and reliability in our machine learning predictions was challenging, necessitating continuous iterations and model optimizations.

## Accomplishments that we're proud of

1. **FrontEnd Programming**: We're particularly proud of developing a user-friendly interface that demystifies genomic data, making personalized nutrition accessible to everyone.

## What we learned

Throughout this project, we delved deep into the intricacies of genomic data and its impact on nutrition. We learned the importance of data quality, the challenges of interpreting complex biological information, and the potential of machine learning to bridge the gap between genetics and dietetics.

## What's next for GeneLevel - the Next-Generation of Personalized Nutrition

1. **Fine-tuning the ML model**: We aim to enhance the model's predictive accuracy by incorporating more diverse mRNA expression data, especially from studies focusing on obesity.
2. **Expanding Dietary Categories**: Beyond high-fat and low-fat diets, we plan to introduce more nuanced dietary categories, allowing for even more personalized nutrition plans tailored to individual genetic profiles and health goals.