Abstract
Mammoclass is a web tool that allows users to enter a small set of variable values that describe a finding in a mammography, and produces a probability of this finding being malignant or benign. The tool requires that the user types in every variable a value in order to perform a prediction. In this work, we present a speech-to-text interface integrated to MammoClass that allows radiologists to speak up a mammography report instead of typing it in. This new MammoClass module can take audio content, transcribe it into written words, and automatically extract the variable values by applying a parser to the recognized text. Results of spoken mammography reports show that the same variables are extracted for both types of input: typed in or dictated text.

Abstract
Several works in the literature use propositional ("black box") approaches to generate prediction models. In this work we employ the Inductive Logic Programming technique, whose prediction model is based on first order rules, to the domain of breast cancer. These rules have the advantage of being interpretable and convenient to be used as a common language between the computer scientists and the medical experts. We also explore the relevance of some of variables usually collected to predict breast cancer. We compare our results with a propositional classifier that was considered best for the same dataset studied in this paper.

Abstract
There are some obstacles, towards a paperless office. One of them is the collection of signatures, since nearly half of all documents are printed for the sole purpose of collecting them. Digital signatures can have the same legal evidential validity as handwritten signatures, provided they are based on certificates issued by accredited certification authorities and the associated private keys are stored on tamper proof token security devices like smart cards. In this article, we propose a platform for secure digital signature workflow management that integrates secure token based digital signatures with the Enterprise Content Management Alfresco, where each user can associate a set of smart cards to his account. The documents can then be signed with the citizen card or other smart card that has digital signatures capabilities. We have implemented an Alfresco module that allows us to explore several workflow techniques to implement real task secure digital signatures workflows, as people for example do when they pass a paper document between various departments to be signed. Since all users can see the current state of the documents being signed during the entire signage process, important security properties like system trust are preserved. We also describe an external validation web service, that provides a way for users to validate signed documents. The validation service then shows to the user important document security properties like timestamps, certificates attributes and highlights the document integrity in face of the digital signatures that have been collected in the workflows defined by our module in Alfresco. © Springer International Publishing Switzerland 2016.

Abstract
The massive growth of the Internet and its services is currently being sustained by the mercantilization of users' identities and private data. Traditional services on the Web require the user to disclose many unnecessary sensitive identity attributes like bankcards, geographic position, or even personal health records in order to provide a service. In essence, the services are presented as free and constitute a means by which the user is mercantilized, often without realizing the real value of its data to the market. In this chapter the auhors describe OFELIA (Open Federated Environment for Leveraging of Identity and Authorization), a digital identity architecture designed from the ground up to be user centric. OFELIA is an identity/authorization versatile infrastructure that does not depend upon the massive aggregation of users' identity attributes to offer a highly versatile set of identity services but relies instead on having those attributes distributed among and protected by several otherwise unrelated Attribute Authorities. Only the end user, with his smartphone, knows how to aggregate these scattered Attribute Authorities' identity attributes back into some useful identifiable and authenticated entity identity that can then be used by Internet services in a secure and interoperable way.

Abstract
Matrix Decomposition methods are applied to a wide range of tasks, such as data denoising, dimensionality reduction, co-clustering and community detection. However, in the presence of boolean inputs, common methods either do not scale or do not provide a boolean reconstruction, which results in high reconstruction error and low interpretability of the decomposition. We propose a novel step decomposition of boolean matrices in non-negative factors with boolean reconstruction. By formulating the problem using threshold operators and through suitable relaxation of this problem, we provide a scalable algorithm that can be applied to boolean matrices with millions of non-zero entries. We show that our method achieves significantly lower reconstruction error when compared to standard state of the art algorithms. We also show that the decomposition keeps its interpretability by analyzing communities in a flights dataset (where the matrix is interpreted as a graph in which nodes are airports) and in a movie-ratings dataset with 10 million non-zeros.

Abstract
A Subgraph Census (determining the frequency of smaller subgraphs in a network) is an important computational task at the heart of several graph mining algorithms. Here we focus on the g-tries, an efficient state-of-the art data structure. Its algorithm makes extensive use of the graph primitive that checks if a certain edge exists. The original implementation used adjacency matrices in order to make this operation as fast as possible, as is the case with most past approaches. This representation is very expensive in memory usage, limiting the applicability. In this paper we study a number of possible approaches that scale linearly with the number of edges. We make an extensive empirical study of these alternatives in order to find an efficient hybrid approach that combines the best representations. We achieve a performance that is less than 50% slower than the adjacency matrix on average (almost 3 times more efficient than a naive binary search implementation), while being memory efficient and tunable for different memory restrictions. © Springer-Verlag Berlin Heidelberg 2016.