## CAMEL Poster

The poster I’m using to present my research creating CAMEL is finally finished (full-size version: CAMEL_poster).

In order to condense the entirety of my paper into a viewer-friendly poster, I decided to make diagrams to describe the program’s inner workings in layman’s terms.
If you like the format of this poster: all code used in this project is on github.

#### Introduction to CAMEL

• machine learning program that uses Braille as a language platform. CAMEL is an acronym of ContextuAl MachinE Learning.
• uses context of unknown symbols to deduce meaning and compress information.
• provided the meaning of an initial set of symbols (a dictionary, or dict). CAMEL deduces meanings of unknowns and adds these meanings to the dict.
• grows more acurrate as the dict increases in size andoptions. Some symbols differ in meaning depending on their context. These translation options are stored in thedict in the form of Map[String, TranslationOptions].

#### What is Grade 2 Braille

• As English words are composed of letters, Braille words are composed of Braille cells.
• Contractions are special characters used to reduce the length of words.
• Some contractions stand for a whole word.  For example: ‘for’ = \braille{{for}}; ‘and’ = \braille{{and}}; ‘the’ = \braille{{the}}.
• Other contractions stand for a group of letters within a word. In the example below, the contraction ‘ing’ is used in the word ‘sing’ and as an ending in the word ‘playing.’ {ing} ; ‘s’ + {ing} = s{ing}; ‘play’ + {ing} = play{ing}
• Grade 1 Braille is uncontracted Braille.

#### Binary Braille

• The Braille alphabet is depicted by a cell that contains six raised/flat dots, numbered one through six beginning with the dot in the upper left-hand corner with the number descending the columns (see figure below).
• To simplify the calculation, I let “0” = flat, “1” = raised.
• The 3×2 matrix (Braille cell) is represented as a 1×6 bitstring (Binary Braille).

• Thus, the letter “c”

#### String Processing Method

CAMEL deduces the complex grammar rules of Grade 2 Braille given partially translated text.

CAMEL learns new symbols by taking 2 input text files (Braille text and corresponding English text), and analyzing them until all unknowns are identified, their meanings are found, and said symbols and their meanings are added to the dictionary.

#### Methods of Tagging and Text Extraction

CAMEL must Tag Unknowns & Compare to English(Extract Chunks) to infer symbol meaning. Four different tag types were used: end, front, mid, and full-word.

Below are examples of how these different types of tags were each used to extract meaning.

#### Using Contracted Braille as a Platform

An example of this process infers the symbols that represent en and in using the word penguin (contracted to p{en}gu{in} in Grade 2 Braille).

### Safety of Community

• commercial application in development that will prevent future mislabeling, such as this sign: labeled “Electrical Room” the Braille translates to “stairwell”

### Proof of Concept

• 1st successful automated program that learns compressed Braille
• translation system is effective for arbitrary symbol systems
• language platform easily changed

#### Acknowledgements

The most difficult part of the poster was creating a mature acknowledgements section; I was very tempted to thank…

• insomnia for allowing me to code at 2 am
• coffee for powering me through the day after coding at 2am
• my friend for introducing me to the instant protein-rich “meal” that is trail mix
• my research partner that refused to code in C++, which forced me to learn Python
• Guido van Rossum for inventing said language
• my parents for putting up with me when I immerse myself in research
• my friends for putting up with me when I stop in the middle of a conversation to write down ideas and/or zone-out thinking

Although these were essential to my completion of this project, I think it’s best to not include these points in the poster.

## CAMEL paper

I began working on CAMEL (Contextual Machine Learning Through the Analysis and Chunking of Partially Translated Grade 2 Braille) when I saw this atrocity: the Braille below translates to “STAIRWELL” Most sighted people don’t know Braille (to my dismay- the grammar is beautiful), this gave me the idea to write an optical Braille reader app for the sighted: the user holds their Android camera up to a sign and it automatically translates the Braille into English.

As I sat down to code this, I realized that I’d have to hard code a dictionary of Grade 2 Braille (a grammatically complex language). I have a deep disgust for hard coding $\Rightarrow$ CAMEL is the program I wrote to automate the creation of a Grade 2 Braille dictionary. All code used in this project is on github.

This program is based on contextual machine learning, so I named my project CAMEL (ContextuAl MachinE Learning). The title of my paper is a mouthful, because I’m unsure of how to shorten it while maintaining clarity: CAMEL

## Introduction to Braille

I’m currently researching machine learning using Braille as my language platform. There is a Research Symposium for Mason’s College of Science at the end of April.

Below is a sneak peak of my paper. I’ve just finished my “Introduction to Braille” section: