Abstract
A sensor based on 2 hollow core microspheres is proposed. Each microsphere was produced separately through fusion splicing and then joined. The resultant structure is a Fabry-Perot interferometer with multiple interferences that can be approximated to a 4-wave interferometer. Strain characterization was attained for a maximum of 1350 mu epsilon, achieving a linear response with a sensitivity of 3.39 +/- 0.04 pm/mu epsilon. The fabrication technique, fast and with no chemical hazards, as opposed to other fabrication techniques, makes the proposed sensor a compelling solution for strain measurements in hash environments.

Abstract
Introduction. Online health forums help to surface and organize patients' knowledge and make it useful for many. They are used by many to seek for advice or to share what they know about health subjects. Being an important communication medium, it's important to understand why and how it is used. Method. In this work we examine and categorize messages of an online health forum, with the purpose of providing a classification scheme that can be used by the research community in future analyses. The definition of the classification scheme was iterative and its inter-rater reliability was assessed twice using Cohen's Kappa statistic. Analysis. The classification scheme arose from a content analysis of 3,399 messages from several communities of an online health forum. Findings. The scheme is divided into four sections of categories and each section has several subcategories, in total there are 23 subcategories. The inter-rater agreement assessment of the scheme showed a good consistency between coders. The majority of the categories has a Cohen's Kappa agreement above 0.4. Conclusion. The proposed classification scheme facilitates the analysis of messages exchanged in online health forums for several purposes, including studies of information seeking.

Abstract
There are still open questions about the effectiveness of Virtual Reality (VR) in Education when compared to conventional learning methods. This paper studies the feasibility of a VR-based learning tool and the possible differences in knowledge retention across a VR learning method and a conventional audio method, when it comes to learning a foreign language. Also, the students’ sense of presence and satisfaction were studied. For such purpose, a user study was conducted and results revealed that while presence and satisfaction were higher in Virtual Reality, the knowledge retention score remains the same across both experimental conditions. © Springer Nature Switzerland AG 2019.

Abstract
The Classifier kNN is largely used in Human Activity Recognition systems. Research efforts have proposed methods to decrease the high computational costs of the original kNN by focusing, e.g., on approximate kNN solutions such as the ones relying on Locality-sensitive Hashing (LSH). However, embedded kNN implementations need to address the target device memory constraints and power/energy consumption savings. One of the important aspects is the constraint regarding the maximum number of instances stored in the kNN learning process (being it offline or online and incremental). This paper presents simple, energy/computationally efficient and real-time feasible schemes to maintain a maximum number of learning instances stored by kNN. Experiments in the context of HAR show the efficiency of our best approaches, and their capability to avoid the kNN storage runs out of training instances for a given activity, a situation not prevented by typical default schemes.

Abstract
Despite the high performance of current presentation attack detection (PAD) methods, the robustness to unseen attacks is still an under addressed challenge. This work approaches the problem by enforcing the learning of the bona fide presentations while making the model less dependent on the presentation attack instrument species (PAIS). The proposed model comprises an encoder, mapping from input features to latent representations, and two classifiers operating on these underlying representations: (i) the task-classifier, for predicting the class labels (as bona fide or attack); and (ii) the species-classifier, for predicting the PAIS. In the learning stage, the encoder is trained to help the task-classifier while trying to fool the species-classifier. Plus, an additional training objective enforcing the similarity of the latent distributions of different species is added leading to a 'PAIspecies'- independent model. The experimental results demonstrated that the proposed regularisation strategies equipped the neural network with increased PAD robustness. The adversarial model obtained better loss and accuracy as well as improved error rates in the detection of attack and bona fide presentations. © 2019 Gesellschaft fur Informatik (GI). All rights reserved.

Abstract
The tourism sector has become one of the most important engines of economic growth for many countries. With the evolution of Information and Communication Technologies, new opportunities have arisen for a reengineering in the way the various tourism and hospitality industries interact with tourists. Equally, new possibilities open to the entities that vision to promote the patrimony and endogenous products of their regions. Information and Communication Technologies are today one of the most critical areas for the success of tourism and how tourist destinations can be promoted. Tourists, increasingly a digital generation, expect that the access to information and services be done in an innovative, immersive and contextualized way, through the use of technologies that are embedded in their daily lives. This article discusses the touristic routes creation process and the role of Information and Communication Technologies in the creation and support of touristic routes. Also, a technological model is presented in its conceptual perspective, as well the functional modelling of its main components and features.