Abstract
The General Data Protection Regulation (GDPR) is the regulation that determines the directives inherent to the collection, processing, and protection of personal data in European Union (EU) countries. It was implemented in May 2018 and over the past few years, several public and private companies have been affected by serious penalties. With more than 1500 fines already registered, it is important to have an analysis and insights about them. This paper proposes a detailed analysis of the public records of fines under GDPR, understanding the average fines imposed, the main causes for their application and how they have evolved over time. It is also intended to understand the most affected sectors and point ways to mitigate these penalties. It is concluded that fines under GDPR have an increasing trend over time, both in number of fines and in value, with Industry and Commerce & Media, Telecoms and Broadcasting being the most affected sectors.

Abstract
This article addresses the Production Routing Problem (PRP), which consists of determining, in an integrated way, production and inventory planning, and vehicle routing to minimize the costs involved. In the problem, a plant is responsible for producing several types of products to meet the known demand of a set of customers using a homogeneous fleet of vehicles over the planning horizon. In the literature, evolutionary approaches have not been explored in depth for the PRP, specifically for the problem with multiple products. Thus, this work mitigates this gap, presenting a novel Memetic Algorithm and testing its effectiveness on randomly generated sets of instances, comparing the results obtained with a commercial optimization solver. In our solution approach, several classic operators from the literature were implemented. Furthermore, we propose four novel genetic operators. In addition, we evaluated the proposed method's performance in classical instances of literature considering a single item. The computational experiments were carried out to assess the impact of the numerous parameter combinations involving the metaheuristic, and, from statistical analyses, we evidence the proposed technique's robustness. Computational experiments showed that our proposed method outperforms the commercial solver Gurobi in determining feasibly high-quality solutions, mainly on large instances for the PRP with multiple items.

Abstract
Clinical in-bed video-based human motion analysis is a very relevant computer vision topic for several relevant biomedical applications. Nevertheless, the main public large datasets (e.g. ImageNet or 3DPW) used for deep learning approaches lack annotated examples for these clinical scenarios. To address this issue, we introduce BlanketSet, an RGB-IRD action recognition dataset of sequences performed in a hospital bed. This dataset has the potential to help bridge the improvements attained in more general large datasets to these clinical scenarios. Information on how to access the dataset is available at rdm.inesctec.pt/dataset/nis-2022-004.

Abstract
The reading of the T650 glyph has been a puzzle for decades. Here, we analyze the semantic contexts in which the glyph appears together with available phonetic evidence to arrive at a phonetic reading of JOM. We provide grammatical reconstructions of the lexical contexts and discuss the rebuses involved in non semantic contexts.

Abstract
Automatic energy management systems allow users' active participation in flexibility management while assuring their energy demands. We propose a transparent framework for automated energy management to increase trust and improve the learning process, combining machine learning, experts' knowledge, and semantic reasoning. A practical example of thermal comfort shows the advantages of the framework.

Abstract