Abstract
Multi-stage manufacturing, typical in important industrial sectors, is inherently a complex process. The application of the zero-defect manufacturing (ZDM) philosophy, together with recent technological advances in Cyber-Physical Systems (CPS), presents significant challenges and opportunities for the implementation of new system architectures that contributes for the continuous improvement of the production. This paper describes the experience gained in the GO0D MAN project which aims at realizing a fully functional, replicable and therefore widely exploitable solution, employing multi-agent systems, smart on-line inspection tools, data analytics and knowledge management technologies. In particular, the paper presents the challenges tackled during the deployment of the GO0D MAN system architecture in three relevant industrial use cases, which represent more than 80% of the manufacturing sector.

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Providing public services through the internet is an effective approach towards an encompassing number of citizens being covered by them and for cost reduction. However, the fast development of this area has fostered discussion and legislation regarding information security and trustworthiness. In addition to security mechanisms for data processed and stored internally, service providers must ensure that data exchanged between their servers and citizens are not intercepted or modified when traversing heterogeneous and uncontrolled networks. Moreover, such institutions should provide means enabling the citizen to verify the authenticity of the services offered. In this way, the present work provides a comprehensive overview regarding the security posture of Portuguese public institutions in their online services. It consists of non-invasive robustness evaluation of the deployed solutions for end-to-end data encryption and the correct use of digital certificates. As a result, we provide some recommendations aiming to enhance the current panorama in the majority of the 111 online services considered in this study.

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Unmanned Aerial Vehicles (UAVs) acting as aerial Wi-Fi Access Points or cellular Base Stations are being considered to deploy on-demand network capacity in order to serve traffic demand surges or replace Base Stations. The ability to estimate the Quality of Service (QoS) for a given network setup may help in solving UAV placement problems. This paper proposes a Machine Learning (ML) based QoS estimator, based on convolutional neural networks, which estimates the QoS for a given network by considering the UAV positions, the user positions and their offered traffic. The ML-based QoS estimator represents a novel paradigm for estimating the QoS in aerial wireless networks. It provides fast and accurate estimations with reduced computational complexity. We demonstrate the usefulness and applicability of the proposed QoS estimator using the ideal UAV placement algorithm. Simulation results show the QoS estimator has an average prediction error lower than 5%.

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This paper describes the implementation of a modular, flexible and self-organized cyber-physical conveyor system build up with different individual modular and intelligent transfer modules. For this purpose, multi-agent systems (MAS) is used to distribute intelligence among transfer modules supporting plugability and modularity, complemented with self-organization capabilities to achieve a truly self-reconfigurable system. © 2019, Springer Nature Switzerland AG.