Abstract
This paper aims to describe the main outcomes of the ADMS4LV project which stands for Advanced Distribution Management System for Active Management of LV Grids. ADMS4LV targets the development and demonstration of a framework with adequate tools to optimize the management and operation of Low Voltage (LV) networks towards the effective implementation of Smart Grids. This work details the main functionalities of ADMS4LV and discusses their implementation. The validation of the functionalities is presented from demonstrations in a laboratorial setup, namely regarding the algorithms which using advanced data analytics, accomplish to operate LV networks with low observability, (i.e., with few real-time measurements) and without having full knowledge of the networks' technical characteristics, such as the consumers' phase connection to the grid. The assessment of the results shows the adequacy of the ADMS4LV solutions for deployment in distribution networks with current infrastructures, differing unnecessary investments in sensory devices. © 2019 IEEE.

Abstract
Data stream mining seeks to extract useful information from quickly-arriving, infinitely-sized and evolving data streams. Although these challenges have been addressed throughout the literature, none of them can be considered "solved." We contribute to closing this gap for the task of data stream clustering by proposing two modifications to the well-known ClusTree data stream clustering algorithm: pruning unused branches and detecting concept drift. Our experimental results show the difficulty in tackling these aspects of data stream mining and the sensitivity of stream mining algorithms to parameter values. We conclude that further research is required to better equip stream learners for the data stream clustering task.

Abstract

Abstract
Abstract The world population is facing several difficulties related to an aging society. In particular, the widespread increase of chronic and incapacitating diseases is overwhelming for traditional healthcare services. Ambient assisted living (AAL) systems can greatly improve healthcare scalability and scope while keeping people in the comfort of their home environments. This chapter focuses precisely on presenting the fundamental key aspects (cf. processing and sensing, integration and management, communication and coordination, intelligence and reasoning) to promote safety and support for outpatients living autonomously in AAL settings. Furthermore, for each key issue, a set of practical technological solutions are reported and detailed, showing real applicability of ubicomp technology to the integration and management of AAL systems specially designed for supporting daily living activities of people with progressive loss of capacities. © 2019 John Wiley & Sons, Ltd.

Abstract
The Apriori algorithm is considered a classic in the association rules extraction field. This algorithm makes recursive searches in a dataset looking for frequent sets that satisfy given minimum support. Apriori has several properties to optimize its performance, such as reducing the number of generated itemsets and its parallelization by multiple processors. These features have led to the emergence of several studies that present parallel versions of Apriori. However, these proposals do not explore the heterogeneous capabilities of each machine, which causes a significant part of the algorithm's processing time to be spent on I/O processes and not exactly on the execution of the algorithm. In this sense, this study proposes a mathematical modeling of the Apriori algorithm in which heterogeneous machines are considered. The findings identified a better performance of this algorithm when compared to the original and parallel versions of Apriori, but in which all processors are considered homogeneous. The findings reveal the time reducing rate increases with the growth in the number of itemsets and the number of considered processors.

Abstract
Human integration in cyber-physical systems (CPS) is playing a crucial role in the era of the digital transformation, notably because humans are seen as the most flexible driver in an automated system. Two main reference models for human activities in production systems are usually considered, namely Human-in-the-Loop (HitL) and Human-in-the-Mesh (HitM), which present different requirements and challenges. This paper aims to overview the different activities related to the human integration in CPS, particularly discussing the requirements that can be found in HitL and HitM models for the different phases of the decision-making process, namely detect, determine, develop and describe; and analyzing the technologies and computational tools to support these human activities. The human integration in CPS is illustrated through three examples, where humans playing the operator and manager roles are integrated in the PERFoRM and FAR-EDGE ecosystems, covering different phases of the decision-making process. Copyright