The COVID-19 pandemic is an example of a temporary situation when critical infrastructure (CI) operators had to operate with continuously changing conditions. The role of cyber infrastructure during pandemics, for example for the remote work or access to critical systems, has also changed.

The feature extraction capabilities of Machine Learning (ML) models have led to their wide adoption in a large variety of sectors: from anomaly detection for machinery, to user clustering and behavioral prediction, market trends predictions, or the analysis of text, sound, and image data.

In the world of finance, where every decision can have significant ramifications, the possibility of predicting market movements is invaluable. Traditionally, analysts have relied on a combination of data analysis, market trends, and expert insights to make informed predictions.

Due to its multivariate and multipurpose use and reuse, data’s worth is dramatically increasing, leading to an era characterized by the generation of data marketplaces towards accessing, selling, sharing, and trading data and data assets.

ZONESEC: built-in cyber-security for wide area surveillance system. Aljosa Pasic, Jose-Ramon Martinez-Salio, Susana Gonzalez Zarzosa, Rodrigo Diaz. Reggio Calabria, Italy, August 29 – September 1, 2017 (ARES 2017)

Paper named “ZONESEC: built-in cyber-security for wide area surveillance system” has been accepted for publication in S-CI 2017 Workshop, which will take place in conjunction with ARES 2017 to be held 29 august-1 September in Reggio Calabria, Italy.

University of Southampton IT Innovation Centre prepared paper named “Large Scale Surveillance, Detection and Alerts Information Management System for Critical Infrastructure”.

Technical university of Dresden published three papers: “A Fully Balanced Ultra-Wide Band Mixer MMIC with Multi-Tanh Triplet Input for High Dynamic Range Radar Receiver Systems” was accepted for ICNF conference in Vilnius, from June 20-23 (http://www.icnf2017.ff.vu.lt/) and another paper, named “