Since the release of the large discourse-level annotation of the Penn Discourse Treebank (PDTB), research work has been carried out on certain subtasks of this annotation, such as disambiguating discourse connectives and classifying Explicit or Implicit relations. We see a need to construct a full parser on top of these subtasks and propose a way to evaluate the parser. In this work, we have designed and developed an end-to-end discourse parser-to-parse free texts in the PDTB style in a fully data-driven approach. The parser consists of multiple components joined in a sequential pipeline architecture, which includes a connective classifier, argument labeler, explicit classifier, non-explicit classifier, and attribution span labeler. Our trained parser first identifies all discourse and non-discourse relations, locates and labels their arguments, and then classifies the sense of the relation between each pair of arguments. For the identified relations, the parser also determines the attribution spans, if any, associated with them. We introduce novel approaches to locate and label arguments, and to identify attribution spans. We also significantly improve on the current state-of-the-art connective classifier. We propose and present a comprehensive evaluation from both component-wise and error-cascading perspectives, in which we illustrate how each component performs in isolation, as well as how the pipeline performs with errors propagated forward. The parser gives an overall system F1 score of 46.80 percent for partial matching utilizing gold standard parses, and 38.18 percent with full automation.

A Message Authentication Code (MAC) is a function that takes a message and a key asparameters and outputs an authentication of the message. MAC are used to guarantee thelegitimacy of messages exchanged through a network, since generating a correctauthentication requires the knowledge of the key defined secretly by trusted parties.However, an attacker with access to a sufficiently large number of message/authenticationpairs may use a brute force algorithm to infer the secret key: from a set containinginitially all possible key candidates, subsequently remove those that yield an incorrectauthentication, proceeding this way for each intercepted message/authentication pair untila single key remains. In this paper, we determine an exact formula for the expected numberof message/authentication pairs that must be used before such form of attack issuccessful, along with an asymptotical bound that is both simple and tight. We conclude byillustrating a modern application where this bound comes in handy, namely the estimationof security levels in reflection-based verification of software integrity.

We introduce a type of isomorphism among strategic games that we call localisomorphism. Local isomorphisms is a weaker version of the notions of strongand weak game isomorphism introduced in [J. Gabarro, A. Garcia and M. Serna,Theor. Comput. Sci. 412 (2011) 6675–6695]. In a localisomorphism it is required to preserve, for any player, the player’s preferences on thesets of strategy profiles that differ only in the action selected by this player. We showthat the game isomorphism problem for local isomorphism is equivalent to the same problemfor strong or weak isomorphism for strategic games given in: general, extensive andformula general form. As a consequence of the results in [J. Gabarro, A. Garcia and M.Serna, Theor. Comput. Sci. 412 (2011) 6675–6695] thisimplies that local isomorphism problem for strategic games is equivalent to (a) thecircuit isomorphism problem for games given in general form, (b) the boolean formulaisomorphism problem for formula games in general form, and (c) the graph isomorphismproblem for games given in explicit form.