Abstract
The tradition of wine production and consumption in Portugal is widely spread since the country presents climatic and territorial characteristics which have made wine-making an important strategic sector. In addition, the essence of the wine industry has led to greater tourism, thus enhancing the growth of enotourism. Given the importance of the wine production sector in the national context, as well as the potential of Industry 4.0 to stimulate improvements both in efficiency and competitiveness, the objective of this work is to achieve a better understanding of how Industry 4.0 and its key features, namely simulation, can influence wine production and enotourism.

Abstract
Machine Learning is one of the most trending topics nowadays. The reason is of course for being more and more present in our everyday life, even if we do not notice it. What goes even more unnoticed is the fact that every Machine Learning model needs computational power. And of course, it also needs data. But how many data are necessary to build the best Machine Learning model possible, and how many times do we need to retrain a model so that it does not become obsolete as data change? That kind of questions are the ones that can reduce unnecessary costs to a company. In this paper we propose a novel approach to predict the performance of a model given some characteristics of the data, that are called meta-features. The goal is, indeed, to only train a new model when some error metric (e.g., RMSE) is expected to decrease substantially compared with a previously trained model. This approach is best applied in scenarios of data streaming or in Big Data, as well on Interactive Machine Learning scenarios. We validate it on a real Fraud Detection case and this scenario is also briefly described.

Abstract
Planning the expansion and the new topology of distribution networks requires knowing the location and characterization of the load as well as its future growth. Spatial load forecasting is a key tool in this task, providing high spatial resolution and adequate temporal granularity. Nowadays, with the penetration of distributed energy resources, multiple microgrid connection strategies, and implementation of self-healing and protection schemes, it is necessary to identify load blocks to plan the new active network architecture. Based on spatial load forecasting information, this paper proposes a graph partitioning technique to create load clusters in the distribution feeders. A weighted graph is constructed by means of a minimum spanning tree that allows to consider adjacency relations. The results of the simulation, carried out in a real distribution network, have demonstrated the effectiveness of the proposed method.

Abstract
Nowadays, with the exponential growth of data stream sources (e.g., Internet of Things [IoT], social networks, crowdsourcing platforms, and personal mobile devices), data stream processing has become indispensable for online classification, recommendation, and evaluation. Its main goal is to maintain dynamic models updated, holding the captured patterns, to make accurate predictions. The foundations of data streams algorithms are incremental processing, in order to reduce the computational resources required to process large quantities of data, and relevance model updating. This article addresses data stream knowledge processing, covering classification, recommendation, and evaluation; describing existing algorithms/techniques; and identifying open challenges.

Abstract
We present SyVMO, an algorithmic extension of the Variable Markov Oracle algorithm, to model and predict multi-part dependencies from symbolic music manifestations. Our model has been implemented as a software application named INCITe for computer-assisted algorithmic composition. It learns variable amounts of musical data from style-agnostic music represented as multiple viewpoints. To evaluate the SyVMO model within INCITe, we adopted the Creative Support Index survey and semi-structured interviews. Four expert composers participated in the evaluation using both personal and exogenous music corpus of variable size. The results suggest that INCITe shows great potential to support creative music tasks, namely in assisting the composition process. The use of SyVMO allowed the creation of polyphonic music suggestions from style-agnostic sources while maintaining a coherent melodic structure. © 2021, Springer Nature Switzerland AG.

Abstract