Abstract
Deterministic Synchronous Multichannel Extension (DSME) is a prominentMAC behavior first introduced in IEEE 802.15.4e supporting deterministic guarantees using its multisuperframe structure. DSME also facilitates techniques like multi-channel and Contention Access Period (CAP) reduction to increase the number of available guaranteed timeslots in a network. However, any tuning of these functionalities in dynamic scenarios is not explored in the standard. In this paper, we present a multisuperframe tuning technique called DynaMO which tunes the CAP reduction and Multisuperframe Order in an effective manner to improve flexibility and scalability, while guaranteeing bounded delay. We also provide simulations to prove that DynaMO with its dynamic tuning feature can offer up to 15-30% reduction in terms of latency in a large DSME network. © Copyright held by the authors.

Abstract
Never The Less is a live audio-visual (A/V) networked performance, where participants are able to interact remotely and collaboratively. It adopts the newly-proposed web-based A/V Akson system, designed for an internet infrastructure, which allows both musical and visual content generation and interaction across multiple devices in remote locations. The system was built with great emphasis on live-performance and human collaboration, where experts and non-experts (i.e., artists and public) exist at the same level.

Abstract
We have sought to understand the current state of the art on smart tourism and on smart cities. Furthermore, we have sought to understand community awareness and the will to embrace innovation, as they are decisive factors to acquire base knowledge and overcome barriers in (soon to be) overpopulated cities and for those who are looking for a limited time culture experience - known as tourists. We live in an age where technology is increasingly present in our lives and provides us solutions to societal problems. Problems such as traffic, infrastructure and natural resources management, or even increasing citizens’ participation in governance, bringing them closer to decision-making. The objective is to understand the current level of people’s knowledge about the impact that technologies have on the society in which we live and their perception of the usefulness in solving these same problems. Therefore, an anonymous questionnaire was carried out (176 valid answers were received), as well as a focus group with two experts on the Smart Cities subject. What future is brought by those who live and breathe technology? Are people willing to accept a paradigm shift?. © Springer Nature Switzerland AG 2019.

Abstract
Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology.

Abstract

Abstract
Rheumatic heart disease can result from repeated episodes of acute rheumatic fever, which damages the heart valves and reduces their functionality. Early manifestations of heart valve damage are visible in echocardiography in the form of valve thickening, shape changing and mobility reduction. The quantification of these features is important for a precise diagnosis and it is the main motivation for this work. The first step to make this quantification is to accurately identify and track the anterior mitral leaflet throughout the cardiac cycle. An accurate segmentation and tracking with minimum user interaction is still an open problem in literature due to low image quality, speckle noise, signal dropout and nonrigid deformations. In this work, we propose a novel approach for the identification of the anterior mitral valve leaflet in all frames. The method requires a single user-specified point on the posterior wall of the aorta as input, in the first frame. The echocardiography videos are converted into a new image space, the Virtual M-mode, which samples the original echocardiography image over automatically estimated scanning lines. This new image space not only provides the motion pattern of the posterior wall of the aorta, the anterior wall of the aorta and the posterior wall of the left atrium, but also provides the location of the structures in each frame. The location information is then used to initialize the localized active contours, followed by segmenting the anterior mitral leaflet. Results shown that the new image space has robustly identified the anterior mitral valve leaflet, without any failure. The median modified Hausdorff distance error of the proposed method was 2.3 mm, with a recall of 0.94.