In this paper, we propose three protocols to share, among a set of$N$competing entities, the responsibility to grant anonymous access to a resource. The protocols we propose vary in their settings to take into account central or distributed registration. We prove that any subset of guardian authorities can neither tamper with, nor forge, new access-key tokens. Besides, two of the methods we propose are resistant to the eventual appearance of quantum computers. The protocols we propose permit new approaches for cryptographic applications such as electronic voting or blockchain access.

BACKGROUND: Due to their role of receptors or transporters, membrane proteins play a key role in many important biological functions. In our work we used Grammatical Inference (GI) to localize transmembrane segments. Our GI process is based specifically on the inference of Even Linear Languages. RESULTS: We obtained values close to 80% in both specificity and sensitivity. Six datasets have been used for the experiments, considering different encodings for the input sequences. An encoding that includes the topology changes in the sequence (from inside and outside the membrane to it and vice versa) allowed us to obtain the best results. This software is publicly available at: http://www.dsic.upv.es/users/tlcc/bio/bio.html CONCLUSION: We compared our results with other well-known methods, that obtain a slightly better precision. However, this work shows that it is possible to apply Grammatical Inference techniques in an effective way to bioinformatics problems.

In this paper, we study the notion of k-reversibility and k-testability when regular tree languages are involved. We present an inference algorithm for learning a k-testable tree language that runs in polynomial time with respect to the size of the sample used. We also study the tree language classes in relation to other well known ones, and some properties of these languages are proven.

To undertake a syntactic approach to a pattern recognition problem, it is necessary to have good grammatical models as well as good parsing algorithms that allow distorted samples to be classified. There are several methods that obtain, by taking two trees as input, the editing distance between them. In the following work, a polynomial time algorithm which processes the distance between a tree and a tree automaton is presented. This measure can be used in pattern recognition problems as an error model inside a syntactic classifier.