Abstract

Abstract
The burst of social networks and the possibility of being continuously connected has provided a fast way for information diffusion. More specifically, real-time posting allowed news and events to be reported quicker through social networks than traditional news media. However, the massive data that is daily available makes newsworthy information a needle in a haystack. Therefore, our goal is to build models that can detect journalistic relevance automatically in social networks. In order to do it, it is essential to establish a ground truth with a large number of entries that can provide a suitable basis for the learning algorithms due to the difficulty inherent to the ambiguity and wide scope associated with the concept of relevance. In this paper, we propose and compare two different methodologies to annotate posts regarding their relevance: automatic and human annotation. Preliminary results show that supervised models trained with the automatic annotation methodology tend to perform better than using human annotation in a test dataset labeled by experts.

Abstract
The ambition to create start-ups with exponential valuations is the goal of many entrepreneurs. How may one create the so-called unicorns (start-ups with a valuation in excess of one billion US dollars)? Our study involved interviews and a focus group and a theoretical model was created. Business plans are surprisingly seen to be less important than is normally perceived, as is a higher education degree. The research also points to variables such as one’s DNA, experience, implementation capacity, intrinsic motivation, vision and timing, and the ability to seek feedback and learn from mistakes and not just from success. © 2018, Springer International Publishing AG, part of Springer Nature.

Abstract

Abstract
Designing and optimizing applications for energy-efficient High Performance Computing systems up to the Exascale era is an extremely challenging problem. This paper presents the toolbox developed in the ANTAREX European project for autotuning and adaptivity in energy efficient HPC systems. In particular, the modules of the ANTAREX toolbox are described as well as some preliminary results of the application to two target use cases.(1)

Abstract
Dams require continuous security and monitoring programs, integrated with visual inspection and testing in dam surveillance programs. New approaches for dam monitoring focus on multi-sensor integration, taking into account emerging technologies such as GNSS, optic fiber, TLS, InSAR techniques, GBInSAR, GPR, that can be used as complementary data in dam monitoring, eliciting causes of dam deformation that cannot be assessed with traditional techniques. This paper presents a Multi-temporal InSAR (MT-InSAR) monitoring of La Viñuela dam (Málaga, Spain), a 96 m height earth-fill dam built from 1982 to 1989. The presented MT-InSAR monitoring system comprises three C-band radar (~5,7 cm wavelength) datasets from the European satellites ERS-1/2 (1992-2000), Envisat (2003-2008), and Sentinel-1A/B (2014-2018). ERS-1/2 and Envisat datasets were processed using StaMPS. In the case of Sentinel-1A/B, two different algorithms were applied, SARPROZ and ISCE-SALSIT, allowing the comparison of the estimated LOS velocity pattern. The obtained results confirm that LaViñuela dam is deforming since its construction, as an earth-fill dam. Maximum deformation rates were measured in the initial period (1992-2000), being around -7 mm/yr (LOS direction) on the coronation of the dam. In the period covered by the Envisat dataset (2003-2008), the average deforming pattern was lower, of the order of -4 mm/yr. Sentinel-1A/B monitoring confirms that the deformation is still active in the period 2014-2018 in the central-upper part of the dam, with maximums of velocity reaching -6 mm/yr. SARPROZ and ISCE-SALSIT algorithms provide similar results. It was concluded that MT-InSAR techniques can support the development of new and more effective means of monitoring and analyzing the health of dams complementing actual dam surveillance systems. © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.