Difference between revisions of "Cracking the Voynich code 2014"

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* Final seminar ('''16 October''')
 
* Final seminar ('''16 October''')
 
** [[File:Final Seminar, Group 44, 2014.pdf]]
 
** [[File:Final Seminar, Group 44, 2014.pdf]]
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** [https://drive.google.com/file/d/0B_-a4W0rL5Asa21WTnFnZXRrQ1E/view?usp=sharing Final Seminar Video, G44]
 
* Final report - only one report needed in wiki format ('''24 October''')
 
* Final report - only one report needed in wiki format ('''24 October''')
 
** [[Semester B Final Report 2014 - Cracking the Voynich code|Final Report, Group 44, 2014]]
 
** [[Semester B Final Report 2014 - Cracking the Voynich code|Final Report, Group 44, 2014]]
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* Labelled CD or USB stick containing your whole project directories. Only one is needed but it should contain two project directories, ie. one for each group member ('''30 October''')
 
* Labelled CD or USB stick containing your whole project directories. Only one is needed but it should contain two project directories, ie. one for each group member ('''30 October''')
 
* YouTube video summarizing project in exciting way - add the URL to this wiki - only one needed ('''30 October''')
 
* YouTube video summarizing project in exciting way - add the URL to this wiki - only one needed ('''30 October''')
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** [http://youtu.be/NOnotKy9ONA The YouTube Cinematic Project Experience]
 
* Optional: any number of instructional how-to YouTube videos on running your software etc.
 
* Optional: any number of instructional how-to YouTube videos on running your software etc.
  
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* [[Semester A Progress Report 2014 - Cracking the Voynich code|Voynich 2014 (Peter and Bryce) Progress Report]]
 
* [[Semester A Progress Report 2014 - Cracking the Voynich code|Voynich 2014 (Peter and Bryce) Progress Report]]
 
* [[Semester B Final Report 2014 - Cracking the Voynich code|Voynich 2014 (Peter and Bryce) Final Report]]
 
* [[Semester B Final Report 2014 - Cracking the Voynich code|Voynich 2014 (Peter and Bryce) Final Report]]
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* [https://www.eleceng.adelaide.edu.au/students/wiki/projects/index.php/Projects:2014S1-44_Cracking_the_Voynich_Manuscript_Code Elec Eng 2014 Project Wiki]
  
 
==Useful papers we wrote==
 
==Useful papers we wrote==

Latest revision as of 18:07, 3 November 2014

Supervisors

Honours students

Project guidelines

General project description

The Voynich Manuscript is a mysterious 15th century book that no one today know what it says or who wrote it. The book is in a strange alphabet. See details here.

Fortunately the whole book has been converted into an electronic format with each character changed to a convenient ascii character. We want you to write software that will search the text and perform statistical tests to get clues as to the nature of the writing. Does the document bear the statistics of a natural language or is it a fake?

We already have Support Vector Machine (SVM) and Multiple Discriminant Analysis (MDA) software that you can adapt for your purposes. This software is set up to test if two texts are written by the same author or not. The great thing about our software is that it is independent of language. So you could compare it against the existing writings of Roger Bacon, who is a suspected author

Useful notes

  • Download the digital Voynich from here.
  • The UN Declaration of Human Rights is translated into every language in the world and in principle you can compare the Voynich to all the existing languages for statistical proximity. Electronic access is here.

Specific tasks

  • Phase 1: Characterize the text. Write scripts that count its features. How many words? How long is the alphabet? Word frequencies? Probability of one letter following another. Probability of two letter pairs (2-grams) and n-letter group (n-grams). Compare these in a table with known languages obtained by running your same code on the Declaration of Human Rights. Don't forget to get a short paragraph of English and manually count everything and then run it on your code to cross check it is counting correctly. You must always validate your code or you will lose marks.
  • Phase 2: Write a general descriptor for each picture in the book, eg. water, woman, tree, flower, vegetable, leaf, dancing etc. Associate each descriptor with the appropriate paper. Write some code to find which words on a page are unique to those pages with those descriptors. Which words also suddenly increase in frequency on those pages with shared descriptors? Tabulate the results.
  • Phase 3: Investigate the use of Word Recurrence Interval (WRI) versus rank plots. Plot WRI curves of the Voynich versus other languages from the Declaration of Human Rights.
  • Phase 4: Think up some of your own ideas to try out.
  • Phase 5: As WRI is a language-independent metric, you can select classification features based on WRI. Then you can run an SVM and an MDA classifier to compare the Voynich against other languages in the Declaration of Human Rights. Then you can run it against the works of specific authors of interest such as Roger Bacon, John Dee, and Edward Kelley.

Deliverables

Semester A

Semester B

Weekly progress and questions

This is where you record your progress and ask questions. Make sure you update this every week.

Approach and methodology

We expect you to take a structured approach to both the validation and the writing of the software. You should carefully design the big-picture high-level view of the software modules, and the relationships and interfaces between them. Think also about the data transformations needed.

Expectations

  • We don't really expect you to crack the Voynich, though that would be cool if you do and you'll become very famous overnight.
  • To get good marks we expect you to show a logical approach to decisively eliminating some languages and authors, and finding some hints about the statistical nature of the words.
  • In your conclusion, you need to come up with a short list of possible hypotheses and a list of things you can definitely eliminate.
  • We expect you to critically look at the conclusions of the previous work and highlight to what extent your conclusions agree and where you disagree.
  • It is important to regularly see your main supervisors. Don't let more than 2 week go by without them seeing your face briefly.
  • You should be making at least one formal progress meeting with supervisors per month. It does not strictly have to be exactly a month, but roughly each month you should be in a position to show some progress and have some problems and difficulties to discuss. On the other hand the meetings can be very frequent in periods when you have a lot of activity and progress to show.
  • The onus is on you to drive the meetings, make the appointments, and set them up.

Relationship to possible career path

Whilst the project is fascinating as you'll learn about a specific high-profile mystery—and we do want you to have a lot of fun with it—the project does have a hard-core serious engineering side. It will familiarize you with techniques in information theory, probability, statistics, encryption, decryption, signal classification, and datamining. It will also improve your software skills. The new software tools you develop may lead to new IP in the areas of datamining, automatic text language identification, and also make you rich/famous. The types of jobs out there where these skills are useful are in computer security, comms, digital forensics, internet search companies, and language processing software companies. The types of industries that will need you are: the software industry, e-finance industry, e-security, IT industry, Google, telecoms industry, ASIO, ASIS, defence industry (e.g. DSD), etc. So go ahead and have fun with this, but keep your eye on the bigger engineering picture and try to build up an appreciation of why these techniques are useful to our industry. Now go crack the Voynich...this message will self-destruct in five seconds :-)

See also

Useful papers we wrote

[1] M. Ebrahimpour, T. J. Putniņš, M. J. Berryman, A. Allison, B. W.-H.-Ng, and D. Abbott, "Automated authorship attribution using advanced signal classification techniques," PLoS ONE, Vol. 8, No. 2, Art. No. e54998, 2013, http://dx.doi.org/10.1371/journal.pone.0054998

[2] M. J. Berryman, A. Allison, and D. Abbott, "Statistical techniques for text classification based on word recurrence intervals," Fluctuation and Noise Letters, Vol. 3, No. 1, pp. L1–L12, 2003.

References and useful resources

If you find any useful external links, list them here:

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