Abstract
This work has developed a hybrid framework that combines machine learning and control approaches for legged robots to achieve new capabilities of balancing against external perturbations. The framework embeds a kernel which is a model-based, full parametric closed-loop and analytical controller as the gait pattern generator. On top of that, a neural network with symmetric partial data augmentation learns to automatically adjust the parameters for the gait kernel, and also generate compensatory actions for all joints, thus significantly augmenting the stability under unexpected perturbations. Seven Neural Network policies with different configurations were optimized to validate the effectiveness and the combined use of the modulation of the kernel parameters and the compensation for the arms and legs using residual actions. The results validated that modulating kernel parameters alongside the residual actions have improved the stability significantly. Furthermore, The performance of the proposed framework was evaluated across a set of challenging simulated scenarios, and demonstrated considerable improvements compared to the baseline in recovering from large external forces (up to 118%). Besides, regarding measurement noise and model inaccuracies, the robustness of the proposed framework has been assessed through simulations, which demonstrated the robustness in the presence of these uncertainties. Furthermore, the trained policies were validated across a set of unseen scenarios and showed the generalization to dynamic walking.

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 paper explores the problem of sectorization of a parcel delivery service that wants to assign an action region to each of its teams, regarding the number of deliveries scheduled for each zone, so that there is a balanced service amongst sectors, covering contiguous zones, and considering limited capacities for the teams. Besides being relatively easy to model, the available optimization tools and software provide poor results when dimension increases in these types of problems, with computational capacity exceeding. In this paper an integer programming model, combined with an heuristic to return a faster solution, was implemented to solve a sectorization problem in two different situations. The main advantage of the strategy proposed, compared to previous ones, is its simplicity and easy implementation while still returning an optimal solution. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Robotic Process Automation (RPA) is a rules-based system for automating business processes by software bots that mimic human interactions to relieve employees from tedious work. It was verified in the literature that there are few works related to RPA decision support models. This technology is in great growth and, therefore, it becomes important to study the evaluation of the implementation of RPA. The objective of this work is focused on a literature review for the identification and analysis of Robotic Process Automation implementation models. This work analyses some models or studies available in the literature and, in addition, analyses it from a perspective relating to the Triple Bottom Line (TBL) related to environmental, social and economic effects. Regarding the results obtained, it appears that there is still a lot of room to improve research in this field, for example, with regard to the development of an evaluation model for the implementation of the RPA, taking into account the TBL of the sustainability concept.

Abstract
Cities worldwide have agreed on ambitious goals regarding carbon neutrality; thus, smart cities face challenges regarding active and shared mobility due to public transportation's low attractiveness and lack of real-time multimodal information. These issues have led to a lack of data on the community's mobility choices, traffic commuters' carbon footprint and corresponding low motivation to change habits. Besides, many consumers are reluctant to use some software tools due to the lack of data privacy guarantee. This paper presents a methodology developed in the FranchetAI project that addrebes these issues by providing distributed privacy-preserving machine learning models that identify travel behaviour patterns and respective GHG emissions to recommend alternative options. Also, the paper presents the developed FranchetAI mobile prototype. © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

Abstract
The full realization of the potential of a technology requires good understanding of its imple-mentation. During implementations, lack of compatibility between technology and its adopters require dynamic sequences of alignment. This process is understood to be central to the success in technology assimilation. This paper proposes a configurational model to explain and predict the alignment process during technology implementations, derived from a multiple case research of the implementation of a retinopathy screening program in networks of healthcare providers. It builds on and expands previous research capturing in a holistic way the alignment process and its nature of adaptation over time.