Abstract
At the time of more and more devices being connected to the internet, personal and sensitive information is going around the network more than ever. Thus, security and privacy regarding IoT communications, devices, and data are a concern due to the diversity of the devices and protocols used. Since traditional security mechanisms cannot always be adequate due to the heterogeneity and resource limitations of IoT devices, we conclude that there are still several improvements to be made to the 2nd line of defense mechanisms like Intrusion Detection Systems. Using a collection of IP flows, we can monitor the network and identify properties of the data that goes in and out. Since network flows collection have a smaller footprint than packet capturing, it makes it a better choice towards the Internet of Things networks. This paper aims to study IP flow properties of certain network attacks, with the goal of identifying an attack signature only by observing those properties.

Abstract
In the era of the 4th industrial revolution, manufacturing companies are digitally transforming their businesses, processes and factories to address the current market demands. Among several opportunities, the dynamic monitoring of strategical indicators assume a crucial importance for the rapid awareness of the adaptation and optimization of a manufacturing process. In particular, the analysis of the effect that the variation of business factors will provoke in the associated performance indicators will allow to take strategical decisions to mitigate the occurrence of condition changes, as well as to optimize the system operation to face business opportunities. This paper describes the deployment of a KPI (Key Performance Indicators) monitoring tool in an industrial factory producing micro-waves, allowing the fast production reconfiguration facing the continuously and rapidly changing market conditions.

Abstract
The human gait shows the single efficiency and functionality and can be considered the most complex functional activity of the human being. Foot drop can be defined as a significant weakness in the dorsal flexion of the foot. The functional electrical orthosis (FEO) are devices that use electrical stimulation to promote functional movements. Besides the FEO promotes the improvement of muscular tropism, this mechanism can be a source of relearning, because all events are interpreted by the body as his owns. This research sought to analyze the impact of the electrostimulation through the Walkaide (R) system in the angular movement of individuals with foot drop. The data were collected from seven patients and the focus of the evaluation of this research was the subtalar joint angle. After analyze the results, it could be seen that the Walkaide (R) promoted a significant gait improvement, meanly about the compensatory movements.

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.

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.