Daniel Arndt Alves
Daniel Arndt Alves is a master's student in electrical engineering and has a bachelor's in computer science from Mackenzie University in Brazil. Since 2004, he has been responsible for installation, configuration, and maintenance of a cluster of evolutionary computation and cellular automata at Mackenzie University. Throughout his time at the university he has kept in touch with cellular automata, grammar, formal languages, Turing machines, and with NKS.
Currently a systems analyst at Mackenzie, he is one of the administrators of the university website's server and administrator of the Moodle (learning management system for teachers and students) server. He has experience in the field of computer science with emphasis in programming languages, working mainly on management of electronic documents, digital libraries, artificial intelligence, and computer systems.
Generating Fingerprints of Electronic Files--An NKS Way
Information stored in digital documents can be lost during transmission or migration, or when media breaks down or is corrupted. To ensure that data is not and has not changed, one should utilize a digital fingerprint procedure such as digital certificates, cyclic redundancy checks (CRC), or a cryptographic hashing algorithm, such as a secure hashing algorithm (SHA) or message-digest algorithm 5 (MD5). However, keep in mind that a CRC verifies the transmission of the document but not the document itself. SHA and MD5 verify both the transmission and the information in the document itself. A digital fingerprint is unique to each document and verifies the document's integrity (unaltered state). When auditing the information or storage media, reproducing the digital fingerprint can determine whether data has been lost. If employing digital fingerprinting, retain the method by which it was applied so it can be recreated and compared to the original fingerprint.
The proposal for this NKS Summer School 2008 project is to make an NKS way to create fingerprints on digital documents, based on their contents, using their binary data with an initial state to produce a computation result to serve as a fingerprint.
Rule chosen: 1754
At the set of 224 rules, my favorite rule is 1754 because it has an interesting behavior: if it starts with simple initial conditions (a central cell with a state equal to 1 and the others with the state equal to 0) then the pattern produced shows a complex pattern combined with a fixed structure, reflected also on the borders of the automata evolution (left border is a fixed structure and right border is a complex structure).