Abstract
Currently the breakthroughs in most computer vision problems have been achieved by applying deep learning methods. The traditional methodologies that used to successfully discriminate the data features appear to be overwhelmed by the capabilities of learning of the deep network architectures. Nevertheless, many recent works choose to integrate the old handcrafted features into the deep convolutional networks to increase even more their impressive performance. In fingerprint recognition, the minutiae are specific points used to identify individuals and their extraction is a crucial module in a fingerprint recognition system. This can only be emphasized by the fact that the US Federal Bureau of Investigation (FBI) sets as a threshold for a positive identification a number of 8 common minutiae. Deep neural networks have been used to learn possible representations of fingerprint minutiae but, however surprisingly, in this paper it is shown that for now the best choice for an automatic minutiae extraction system is still the traditional road map. A comparison study was conducted with state-of-the-art methods and the best results were achieved by handcraft features. © Springer Nature Switzerland AG 2019.

Abstract
Electronic identity (eID) schemes are key enablers of secure digital services. eIDs have been adopted in several European countries using smart-cards for secure authentication and authorization. Towards achieving a European digital single market where European citizens can seamlessly access cross-border public services using their national eIDs, the European Union (EU) developed the electronic IDentification, Authentication and trust Services (eIDAS) regulation. eIDAS creates an interoperable framework that integrates the eIDs adopted in the EU Member States (MS). It is also an enabler of a cross-border operation, harmonized with the General Data Protection Regulation (GDPR) regulation by protecting the privacy of personal data. If one can use the same procedure for authentication and authorization abroad, one can better understand new services that use eIDs. This paper provides a comparative analysis of eID cards adopted in EU MS and their privacy features in preparedness for eIDs cross-border interoperation. © Springer Nature Switzerland AG 2019.

Abstract

Abstract
In this paper, we introduce the streaming graph canonization problem. Its goal is finding a canonical representation of a sequence of graphs in a stream. Our model of a stream fixes the graph's vertices and allows for fully dynamic edge changes, meaning it permits both addition and removal of edges. Our focus is on small graphs, since small graph isomorphism is an important primitive of many subgraph-based metrics, like motif analysis or frequent subgraph mining. We present an efficient data structure to approach this problem, namely a graph isomorphism discrete finite automaton and showcase its efficiency when compared to a non-streaming-aware method that simply recomputes the isomorphism information from scratch in each iteration.

Abstract
Habitat mapping is an important task to manage ecosystems. This task becomes most challenging when it comes to marine habitats as it is hard to get good images in underwater conditions and to precisely locate them. In this paper we present a novel technique for performing habitat mapping automating all phases, from data collection to classification, lowering costs and increasing efficiency throughout the process. For mapping habitats in a vast coastal region, we use visible light cameras mounted on autonomous underwater vehicles, capable of collecting and geo-locating all acquired data. The optic images are enhanced using Computer Vision techniques, to help specialists identify the habitats they contain (during training phase). In a later stage, we employ convolutional neural networks to automatically identify habitats in all imagery. Habitats are classified according to the European Nature Information System, an European classification standard for habitats.

Abstract
Can we forecast future connections in a social network? Can we predict who will start using a given hashtag in Twitter, leveraging contextual information such as who follows or retweets whom to improve our predictions? In this paper we present an abridged report of TENSORCAST, a method for forecasting time-evolving networks, that uses coupled tensors to incorporate multiple information sources. TENSORCAST is scalable (linearithmic on the number of connections), effective (more precise than competing methods) and general (applicable to any data source representable by a tensor). We also showcase our method when applied to forecast two large scale heterogeneous real world temporal networks, namely Twitter and DBLP.