Abstract
In recent years, social curation technology has been successfully promoted as a means of addressing the problem of involving citizens with news content. Sites like Facebook, Reddit and Storify, just to name a few, allow their users to discuss, comment, share, identify and review news content from various sources. A key goal of this paper is to analyze the process of social validation in the context of Acropolis, a social computing platform that allows citizens to build and share their own narratives about complex or long-term news stories. The sense of social validation determines an increase in self-esteem, self-confidence, and instant happiness, when conveniently engendered in virtual environments.

Abstract
We present the design, fabrication and optical characterization of functional metamaterials for optical sensing of Hydrogen based on inexpensive self-assembly processes of metallic nanowires integrated in nanoporous alumina templates([37-42]). The optical properties of these materials strongly depend on the environmental concentration or partial pressure of hydrogen and can be used to develop fully optical sensors that reduce the danger of explosion. Optical metamaterials are artificial media, usually combining metallic and dielectric sub-wavelength structures, that exhibit optical properties that cannot be found in naturally occurring materials. Among these, functional metamaterials offer the added possibility of altering or controlling these properties externally after fabrication, in our case by contact with a hydrogen rich atmosphere. This dependency can be used to design([43-45]) and develop optical sensors that respond to this gas or to chemical compounds that contain or release hydrogen. In this paper we present some designs for hydrogen functional metamaterials and discuss the main parameters relevant in the optimization of their response.

Abstract
The Internet of Things (IoT) is rapidly becoming ubiquitous and applied in different domains such as human health, building automation, industrial control and environmental monitoring, introducing new security and privacy challenges. Thus, the security of data, devices and communications of IoT networks are a concern due to the sensitivity of the data used, legal and privacy issues, and the diversity of devices and protocols used. In addition, traditional security mechanisms cannot always be feasible and adequate because of the number, heterogeneity, and resource limitations of IoT devices. In this work, we are concerned with the design of an Intrusion Detection System (IDS) to protect IoT networks from external and internal threats in real time. To do this, after studying the various traditional IDS solutions, as well as new IDS proposals designed specifically for IoT networks, we conclude that there are still several improvements to be made to this type of 2nd line defense mechanism. The design proposed will consider the specific architecture of an IoT network, the scalability and heterogeneity of this type of environment, the minimization of the use of resources, and the maximization of the efficiency in the detection of intrusions. To do so, we consider the various detection methods available and the various types of attacks to which this type of network is exposed. The proposed IDS is network-based and relies on a hybrid architecture (centralized / distributed). As methods of detection, the signature / anomaly-based methods will be used simultaneously. Finally, it is emphasized that this proposal does not require the modification of the IoT software, nor does it influence the performance of the applications in the IoT devices.

Abstract
When engaged in communication, people often rely on pointing gestures to refer to out-of-reach content. However, observers frequently misinterpret the target of a pointing gesture. Previous research suggests that to perform a pointing gesture, people place the index finger on or close to a line connecting the eye to the referent, while observers interpret pointing gestures by extrapolating the referent using a vector defined by the arm and index finger. In this paper we present Warping Deixis, a novel approach to improving the perception of pointing gestures and facilitate communication in collaborative Extended Reality environments. By warping the virtual representation of the pointing individual, we are able to match the pointing expression to the observer's perception. We evaluated our approach in a co-located side by side virtual reality scenario. Results suggest that our approach is effective in improving the interpretation of pointing gestures in shared virtual environments.

Abstract
The self-localization of mobile robots in the environment is one of the most fundamental problems in the robotics navigation field. It is a complex and challenging problem due to the high requirements of autonomous mobile vehicles, particularly with regard to the algorithms accuracy, robustness and computational efficiency. In this paper, we present a comparison of three of the most used map-matching algorithms applied in localization based on natural landmarks: our implementation of the Perfect Match (PM) and the Point Cloud Library (PCL) implementation of the Iterative Closest Point (ICP) and the Normal Distribution Transform (NDT). For the purpose of this comparison we have considered a set of representative metrics, such as pose estimation accuracy, computational efficiency, convergence speed, maximum admissible initialization error and robustness to the presence of outliers in the robots sensors data. The test results were retrieved using our ROS natural landmark public dataset, containing several tests with simulated and real sensor data. The performance and robustness of the Perfect Match is highlighted throughout this article and is of paramount importance for real-time embedded systems with limited computing power that require accurate pose estimation and fast reaction times for high speed navigation. Moreover, we added to PCL a new algorithm for performing correspondence estimation using lookup tables that was inspired by the PM approach to solve this problem. This new method for computing the closest map point to a given sensor reading proved to be 40 to 60 times faster than the existing k-d tree approach in PCL and allowed the Iterative Closest Point algorithm to perform point cloud registration 5 to 9 times faster.

Abstract
The importance of planktonic microbial communities is well acknowledged, since they are fundamental for several natural processes of aquatic ecosystems. Microorganisms naturally control the flux of nutrients, and also degrade and recycle anthropogenic organic and inorganic contaminants. Nevertheless, climate change effects and/or the runoff of nutrients/pollutants can affect the equilibrium of natural microbial communities influencing the occurrence of microbial pathogens and/or microbial toxin producers, which can compromise ecosystem environmental status. Therefore, improved microbial plankton monitoring is essential to better understand how these communities respond to environmental shifts. The study of marine microbial communities typically involves highly cost and time-consuming sampling procedures, which can limit the frequency of sampling and data availability. In this context, we developed and validated an in situ autonomous biosampler (IS-ABS) able to collect/concentrate in situ planktonic communities of different size fractions (targeting prokaryotes and unicellular eukaryotes) for posterior genomic, metagenomic, and/or transcriptomic analysis at a home laboratory. The IS-ABS field prototype is a small size and compact system able to operate up to 150 m depth. Water is pumped by a micropump (TCS MG2000) through a hydraulic circuit that allows in situ filtration of environmental water in one or more Sterivex filters placed in a filter cartridge. The IS-ABS also includes an application to program sampling definitions, allowing pre-setting configuration of the sampling. The efficiency of the IS-ABS was tested against traditional laboratory filtration standardized protocols. Results showed a good performance in terms of DNA recovery, as well as prokaryotic (16S rDNA) and eukaryotic (18S rDNA) community diversity analysis, using either methodologies. The IS-ABS automates the process of collecting environmental DNA, and is suitable for integration in water observation systems, what will contribute to substantially increase biological surveillances. Also, the use of highly sensitive genomic approaches allows a further study of the diversity and functions of whole or specific microbial communities.