Abstract
Following the work of Al-Riyami et al. we define the notion of key encapsulation mechanism supporting cryptographic workflow (WF-KEM) and prove a KEM-DEM composition theorem which extends the notion of hybrid encryption to cryptographic workflow. We then generically construct a WF-KEM from an identity-based encryption (IBE) scheme and a secret sharing scheme. Chosen ciphertext security is achieved using one-time signatures. Adding a public-key encryption scheme we are able to modify the construction to obtain escrow-freeness. We prove all our constructions secure in the standard model.

Abstract
The multiary version of the lambda-calculus with generalized applications integrates smoothly both a fragment of sequent calculus and the system of natural deduction of von Plato. It is equipped with reduction rules (corresponding to cut-elimination/normalisation rules) and permutation rules, typical of sequent calculus and of natural deduction with generalised elimination rules. We argue that this system is a suitable tool for doing structural proof theory as rewriting. As an illustration, we investigate combinations of reduction and permutation rules and whether these combinations induce rewriting systems which are confluent and terminating. In some cases, the combination allows the simulation of non-terminating reduction sequences known from explicit substitution calculi. In other cases, we succeed in capturing interesting classes of derivations as the normal forms w.r.t. well-behaved combinations of rules. We identify six of these "combined" normal forms, among which are two classes, due to Herbelin and Mints, in bijection with normal, ordinary natural deductions. A computational explanation for the variety of "combined" normal forms is the existence of three ways of expressing multiple application in the calculus.

Abstract
The problem of discovering previously unknown frequent patterns in time series, also called motifs, has been recently introduced. A motif is a subseries pattern that appears a significant number of times. Results demonstrate that motifs may provide valuable insights about the data and have a wide range of applications in data mining tasks. The main motivation for this study was the need to mine time series data from protein folding/unfolding simulations. We propose an algorithm that extracts approximate motifs, i.e. motifs that capture portions of time series with a similar and eventually symmetric behavior. Preliminary results on the analysis of protein unfolding data support this proposal as a valuable tool. A.dditional experiments demonstrate that the application of utility of our algorithm is not limited to this particular problem. Rather it can be an interesting tool to be applied in many real world problems.

Abstract

Abstract
We propose an approach to subgroup discovery using distribution rules (a kind of association rules with a probability distribution on the consequent) for numerical properties of interest. The objective interest of the subgroups is measured through statistical goodness of fit tests. Their subjective interest can be assessed by the data analyst through a visual interactive subgroup browsing procedure.

Abstract
In this paper we introduce distribution rules, a kind of association rules with a distribution on the consequent. Distribution rules are related to quantitative association rules but can be seen as a more fundamental concept, useful for learning distributions. We formalize the main concepts and indicate applications to tasks such as frequent pattern discovery, sub group discovery and forecasting. An efficient algorithm for the generation of distribution rules is described. We also provide interest measures, visualization techniques and evaluation.