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From cybersecurity to life sciences, anomaly detection is considered crucial as it often enables the identification of relevant semantic information that can help to prevent and detect events such as cyber attacks or patients heart-attacks. Although anomaly detection is a prominent research area it still encompasses several challenges, namely regarding results evaluation in real-world unlabelled and imbalanced datasets. This work contributes to understand and compare the behaviour of different evaluation metrics, namely classic metrics based on positive and negative rates, and density based metrics without classes information. We experiment five state-of-art anomaly detection approaches over two datasets with contrasting characteristics regarding dimensionality or contamination. Each metrics’ ability to give trustful results is analysed regarding different datasets or approaches properties focusing on the possibility of evaluating real-world unsupervised learning models using density metrics.

The increasing complexity of social-economical activities in the maritime domain is raising new challenges to maritime administrations and agencies from coastal states. These challenges require innovative approaches to support maritime safety and security and, in particular, to promote the sustainable use of the oceans. One solution is to enhance Maritime Situational Awareness (MSA) using systems with high integration and efficient analysis capability. With this problem in mind, the European Commission awarded a grant to a project which aims to develop a framework that will enable a better decision process, by the operators and decision makers, at the maritime operations centers and allowing seamless cooperation between operating authorities and on-site / at sea / in air intervention forces. The project focuses on taking benefits from the data exchange to the next level, while remaining completely integrated in the current European policy. Securing shipping and human lives is of fundamental importance for our continued well-being. The most important way to cost-effectively enhance security and safety at sea is to cooperate at national and international levels. This paper focuses on the specific contribution from some partners of the European Project Consortium - MARISA.

City parking is increasingly complex and available parking spaces are scarce. Being able to identify a space to park their cars can lead many drivers to drive around the intended parking area several times, increasing traffic density and pollution. In this research we propose a collaborative blockchain solution with gamification for parking. Users collaborate to report free spaces and receive free parking minutes for their service to the community. In parallel, this approach can be used to collect beacon information from the parked vehicles and create a low-cost collaborative approach for managing a parking control process platform Blockchain that can handle this distributed process and the gamification platform increases users’ participation. info:eu-repo/semantics/acceptedVersion

Medicine is a knowledge area continuously experiencing changes. Every day, discoveries and procedures are tested with the goal of providing improved service and quality of life to patients. With the evolution of computer science, multiple areas experienced an increase in productivity with the implementation of new technical solutions. Medicine is no exception. Providing healthcare services in the future will involve the storage and manipulation of large volumes of data (big data) from medical records, requiring the integration of different data sources, for a multitude of purposes, such as prediction, prevention, personalization, participation, and becoming digital. Data integration and data sharing will be essential to achieve these goals. Our work focuses on the development of a framework process for the integration of data from different sources to increase its usability potential. We integrated data from an internal hospital database, external data, and also structured data resulting from natural language processing (NPL) applied to electronic medical records. An extract-transform and load (ETL) process was used to merge different data sources into a single one, allowing more effective use of these data and, eventually, contributing to more efficient use of the available resources. info:eu-repo/semantics/publishedVersion

The process of protecting sensitive data is continually growing and becoming increasingly important, especially as a result of the directives and laws imposed by the European Union. The effort to create automatic systems is continuous, but, in most cases, the processes behind them are still manual or semi-automatic. In this work, we have developed a component that can extract and classify sensitive data, from unstructured text information in European Portuguese. The objective was to create a system that allows organizations to understand their data and comply with legal and security purposes. We studied a hybrid approach to the problem of Named Entity Recognition for the Portuguese language. This approach combines several techniques such as rule-based/lexical-based models, machine learning algorithms, and neural networks. The rule-based and lexical-based approaches were used only for a set of specific classes. For the remaining classes of entities, two statistical models were tested&mdash, Conditional Random Fields and Random Forest and, finally, a Bidirectional-LSTM approach as experimented. Regarding the statistical models, we realized that Conditional Random Fields is the one that can obtain the best results, with a f1-score of 65.50%. With the Bi-LSTM approach, we have achieved a result of 83.01%. The corpora used for training and testing were HAREM Golden Collection, SIGARRA News Corpus, and DataSense NER Corpus.

In this work we describe our first steps towards our H2020 project MARISA participation, where we intent to develop a tool-kit towards the identification of outliers in Vessel trajectories based on electronic data regarding position and time. These outliers can correspond to illegal activities that could be related with illegal immigration, drugs transshipment among others. We developed process tools that based on any electronic Vessel position systems, like Automatic Identification System (AIS) data, it is possible to extract routes in an unsupervised approach. At the same time identify non-conformities based on AIS data signal lost and to identify situation when two or more Vessels are approaching close to each other, called the rendezvous.

In this work we describe our first steps towards our H2020 project MARISA [https://www.marisaproject.eu] participation, where we intent to develop a toolkit towards the identification of outliers in vessel trajectories based on electronic data regarding position and time. These outliers can correspond to illegal activities that could be related with illegal immigration, drugs trans-shipments among others. We developed process tools that based on any elec-tronic vessel position systems, like Automatic Identification System (AIS) data, it is possible to extract routes in an unsupervised approach. At the same time identify non-conformities based on AIS data signal lost and to identify situation when two or more vessels are approaching close to each other, called the rendezvous.