Abstract
Some governments do not consider metadata as personal data, and so not in the scope of privacy regulations. However, often, metadata gives more relevant information than the actual content itself. Metadata can be very useful to identify, locate, understand and manage personal data, i.e., information that is eminently private in nature and under most privacy regulation should be anonymized or deleted if users have not give their consent. In voice calls, we are facing a critical situation in terms of privacy, as metadata can identify who calls to whom and the duration of the call, for example. In this work, we investigate privacy properties of voice calls metadata, in particular when using secure VoIP, giving evidence of the ability to extract sensitive information from its ("secure") metadata. We find that ZRTP metadata is freely available to any client on the network, and that users can be re-identified by any user with access to the network. Also, we propose a solution for this problem, suitable for all the ZRTP-based implementations.

Abstract
With the constant ICT evolution, the way users interact with e-commerce platforms (EC) is changing and there are constantly emerging new needs both from customers, such as the possibility of customization and individual personalization, and from business operators. As argued by existing literature, EC is a very relevant tool for the overall development of territories, especially those with inherent constraints, such as low-density regions. This work seeks to propose, through a functional and technical analysis, an electronic commerce platform of tourism products and services that allows both the traditional commercialization of products and, in parallel, the negotiation in (almost) real time of tourism products and services, idealized by customers themselves, to which operators can reply via the platform itself.

Abstract
Multi-Agent Epistemic Logic has been investigated in Computer Science [6] to represent and reason about agents or groups of agents knowledge and beliefs. Some extensions aimed to reasoning about knowledge and probabilities and also with a fuzzy semantics have been proposed [7,14]. This paper introduces a parametric method to build graded epistemic logics inspired in the systematic method to build Multi-valued Dynamic Logics introduced in [12,13]. The parameter in both methods is the same: an action lattice [10]. This algebraic structure supports a generic space of agent knowledge operators, as choice, composition and closure (as a Kleene algebra), but also a proper truth space for possible non bivalent interpretation of the assertions (as a residuated lattice).

Abstract

Abstract
In data imputation problems, researchers typically use several techniques, individually or in combination, in order to find the one that presents the best performance over all the features comprised in the dataset. This strategy, however, neglects the nature of data (data distribution) and makes impractical the generalisation of the findings, since for new datasets, a huge number of new, time consuming experiments need to be performed. To overcome this issue, this work aims to understand the relationship between data distribution and the performance of standard imputation techniques, providing a heuristic on the choice of proper imputation methods and avoiding the needs to test a large set of methods. To this end, several datasets were selected considering different sample sizes, number of features, distributions and contexts and missing values were inserted at different percentages and scenarios. Then, different imputation methods were evaluated in terms of predictive and distributional accuracy. Our findings show that there is a relationship between features’ distribution and algorithms’ performance, and that their performance seems to be affected by the combination of missing rate and scenario at state and also other less obvious factors such as sample size, goodness-of-fit of features and the ratio between the number of features and the different distributions comprised in the dataset. © Springer Nature Switzerland AG 2018.

Abstract
Lung cancer is the world's most lethal type of cancer, being crucial that an early diagnosis is made in order to achieve successful treatments. Computer-aided diagnosis can play an important role in lung nodule detection and on establishing the nodule malignancy likelihood. This paper is a contribution in the design of a learning approach, using computed tomography images. Our methodology involves the measurement of a set of features in the nodular image region, and train classifiers, as K-nearest neighbor or support vector machine (SVM), to compute the malignancy likelihood of lung nodules. For this purpose, the Lung Image Database Consortium and image database resource initiative database is used due to its size and nodule variability, as well as for being publicly available. For training we used both radiologist's labels and annotations and diagnosis data, as biopsy, surgery and follow-up results. We obtained promising results, as an Area Under the Receiver operating characteristic curve value of 0.962 +/- 0.005 and 0.905 +/- 0.04 was achieved for the Radiologists' data and for the Diagnosis data, respectively, using an SVM with an exponential kernel combined with a correlation-based feature selection method.