# 現代の圧縮 ( 美 学 A E S T H E T I C S )

### Modern compression for the modern aesthete. (現代壓縮 4 2 0 為現代 э с т е т а .)

Using advanced algorithms technology, we have created a compression algorithm that has a local compression ratio of over 700 000. The future is here, Web 3.0 enabled, Python 2.7 running, Big Data storing, Analytics enhancing, cutting edge technology.

#### [# S A D B O Y S](http://instagram.com/ineverwin)

## Inspiration

[WPEG Compression Algorithm](http://www.dangermouse.net/esoteric/wpeg.html) is a theoretical compression algorithm that can get infinite local compression ratio. **現代の圧縮 ( 美 学 A E S T H E T I C S )** is a practical implementation of the WPEG algorithm that uses advanced Web3.0 technology and post-現代ism 美学 to create the most advanced 420 圧縮 (compression) algorithm in the world.

#### [# E L E V A T O R C O R E](http://instagram.com/ineverwin)

## What it does

**現代の圧縮 ( 美 学 A E S T H E T I C S )** turns any file under 5MB into a file of just 7 bytes. This compression ratio is well over 700 000, which is significantly higher than any other popular compression algorithm available today. Using **現代の圧縮 ( 美 学 A E S T H E T I C S )**, we can expect an increased efficiency in Big Data storage as well as new, cutting-edge techniques in Data Analytics.

**現代の圧縮 ( 美 学 A E S T H E T I C S )** uses the ポストアルゴリズムのプログラミングプラグマ (post-algorithmic programming pragma), the deconstruction of the use of complex algorithms to increase the compression ratios of large files. Instead, we took an 盒子外面 (out-of-the-box) solution that incorporates the cutting-edge technology of the Imgur API and the losslessness property of files under 5MB uploaded to Imgur. Thus, we simply upload *any* file in 5MB chunks encoded as PNG files *( 美 学 A E S T H E T I C S )* and store these as 7 bit characters which are the URLs of these images as Imgur gives them. Treating Imgur as a 無限のストレージボックス (infinity storage box), we can compress any large file with a compression ratio of nearly one million.

#### [# V A P O R W A V E](http://instagram.com/ineverwin)

## How I built it

Using the newest version of Python2 (the one that doesn't suck) and Anaconda, we created the most up-to-date and versatile CaaS (サービスとしての圧縮/Compression-as-a-Service) platform available. **現代の圧縮 ( 美 学 A E S T H E T I C S )** takes the concept of SaaS and further applies it to be the most relevant towards Cloud Computing/Big Data Analytics as possible. With the state-of-the-art Imgur API (アプリケーション・プログラマーのインターフェース), we created the fastest, most efficient, and bleeding-edgiest compression software available; all in under 200 lines of code.

#### [# 4 2 0](http://instagram.com/ineverwin)

## Challenges I ran into

Learning to program was pretty hard, the Imgur API had really convenient calls but I was too lazy to read the API documentation, I had to finish some homework so I was a bit pressed on time, this documentation took *wayyyyy* too long.

Also I needed to figure out how this relates to the sponsors to pretend like I'm qualified for prizes (I'm not really, but this worked on my Windows ~~95~~ 10 machine so I applied for the Microsoft マイクロソフト prize).

#### [# W I N D O W S 9 5](http://instagram.com/ineverwin)

## Accomplishments that I'm proud of

Incorporated the elements of V A P O R W A V E completely unnecessarily into a Hackathon project. Made a **圧縮アルゴリズム ( C O M P R E S S I O N A L G O R I T H M )** that is one of the most consistent and efficient. One more project to my resume I guess.

#### [# P O S T S T A R T U P C U L T ( U R E )](http://instagram.com/ineverwin)

## What I learned

Python, coding, Imgur API, V A P O R W A V E, **( 美 学 A E S T H E T I C S )**, the post-programming pragma.

#### [# A E S T H E T I C S](http://instagram.com/ineverwin)

## What's next for 現代の圧縮 ( 美 学 A E S T H E T I C S )

**現代の圧縮 ( 美 学 A E S T H E T I C S )** is taking CaaS Big Data Analytics/SaaS/Cloud Computing to exciting new directions. What the future holds for us is not simply 現代の圧縮, but 現代のコンピュー (modern computing) in general. With the invention of the Turing Complete Machine, many more problems will become feasible and we may one day even find a solution to the

```
H A L T I N G P R O B L E M 停 機 問 題 。

```

#### [# 現 代 の 圧 縮 ( M O D E R N C O M P R E S S I O N )](http://instagram.com/ineverwin)

## [W W W . I N S T A G R A M . C O M / I N E V E R W I N](http://instagram.com/ineverwin)
## 💖Inspiration💝

The inspiration for CiphyrAI originated from University of Chicago's Nightshade and Glaze image processing algorithms, which were adversarial programs designed to attempt to control generative AI and limit its propagation into social media and the modern zeitgeist as a whole. We wanted to create something that would be effective even after breakthroughs with image generation. CiphyrAI accommodates this by acting as an invisible watermark, which allows for the democratization of personal art protection and copyright.

## ⚙️What it does⚙️

CiphyrAI provides a way for digital photo attribution via watermarking images and our watermark reading algorithm using Luhn error correction codes, stenography, and AES encryption through TypeScript. Created a system wherein changes to pixels can be detected down to a 2x2 pixel grid across any image with a .1% false negative rate per grid square. This is unnoticeable to the human eye, but recognizable by our app to ensure attribution.

## 🧑‍💻How we built it🧑‍💻

We built it with React and Typescript deployed in Vercel.

## 🎢Challenges we ran into🎢

Typescript documentation is quite lacking and outdated with no publication dates for reference... hence our 6 hour detour to fix issues related to Typescript discrepancies around imports and modules 😅

## 🌐Accomplishments that we're proud of🌐

We're proud of our beautiful front-end and local visualizer for detected image changes. We're also proud and amazed at how well the algorithm to encrypt the images worked for that use case.

## 🧑‍🎓What we learned🧑‍🎓

We learned the troubles and tribulations of implementing a web-app with Typescript and know not to fully trust it's documentation without ample research to validate it's for the current documentation version since there's no publication dates on Microsoft's Typescript articles.

## 🗣️What's next for CiphyrAI🗣️

Given the immense learning we've had during the development of this app, we plan to re-develop this web-app utilizing Firebase for user account authentication, encoded image storage, and other useful functions. We truly see a use case in this idea and think it'd be very cool to see through our implementation as originally envisioned without compromising to time constraints!
## Inspiration

Due to COVID-19, millions of students across the world have been forced to quickly adapt to video lectures and online education. To ease this transition and make studying more efficient, we wanted to help students by summarizing their lecture transcripts while also capturing the full lecture transcript so students can pay full attention to the lecture instead of being distracted with taking notes.

## What it does

A website that summarizes video lectures for efficient studying. Users can upload their video lectures to receive a full text transcript and timestamps of the most important sentences. The video timestamps have not been formatted to "minutes : seconds" yet.

## How We built it

We made made a microservice out of a pre-trained BERT model to summarize text, and an Express web server that works with Vue for the UI to make a web app. The web app accepts video uploads, sends the video to Azure's Speech-to-Text API to get a full transcript, sends the transcript to the microservice to get a summary with timestamps, and sends the summary and timestamps to Vue for display and video playback.

## Challenges we ran into

Managing cloud platform credentials in a team of 4 was difficult. Coordinating ourselves to avoid duplicating work. Managing packages and dependencies. Scope creep. Timestamps need to formatted to "minutes : seconds".

## Accomplishments that we're proud of

Most technically sophisticated hackathon project so far, as the project has many moving parts like Azure Media Services (for video playback), Azure Cognitive Services (for the Speech-to-Text API), and BERT (for text summarization).
Fun name ;)

## What we learned

CORS, JavaScript Promises, Microservice Architecture

## What's next for AweSummarizer

Adding video lecture subtitles automatically generated with Azure Media Services. Adding clickable timestamps to play the video at that timestamp for more convenient navigation.