Abstract
Industrial Cyber-Physical Systems (CPS) are promoting the development of smart machines and products, leading to the next generation of intelligent production systems. In this context, Artificial Intelligence (AI) is posed as a key enabler for the realization of CPS requirements, supporting the data analysis and the system dynamic adaptation. However, the centralized Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitivity. Edge Computing can address the new challenges, enabling the decentralization of data analysis along the cyber-physical components. In this context, distributed AI approaches such as those based onMulti-agent Systems (MAS) are essential to handle the distribution and interaction of the components. Based on that, this work uses a MAS approach to design cyber-physical agents that can embed different data analysis capabilities, supporting the decentralization of intelligence. These concepts were applied to an industrial automobile multi-stage production system, where different kinds of data analysis were performed in autonomous and cooperative agents disposed along Edge, Fog and Cloud computing layers.

Abstract

Abstract
Cascaded H-bridge (CHB) inverters may have some of its cells with a bidirectional power flow between the dc source and the load. Considering this, switching strategies for multilevel CHB inverters are proposed for applications in which the regenerative mode is not desired, forcing all the cells to have either null or positive power flow toward the load. In order to validate the theoretical considerations, simulation and experimental analysis are made, where it is possible to maintain the high voltage quality and avoid the regenerative mode at any operating point.

Abstract
The authors present a framework for developing intercultural competence (IC) and use tridimensional digital virtual worlds (3DVW) as environments for developing IC. They developed an artifact, via design research, constituted by an educational method using the 3DVW Second Life® as the place for a virtual exchange program between 92 Brazilian and Portuguese master students. The results of the study indicate that the 3DVW can be used for the development of IC because it allows rich experiential and relational/conversational learning opportunities, especially due to the affordances of immersion/sense of presence, social interaction, content production, and knowledge sharing. The students involved in the virtual exchange inside Second Life® had to practice a set of attitudes and skills such as communication skills; culture-specific knowledge; understanding others' worldviews; skills to analyze, evaluate, and relate; skills to listen, observe, and interpret; respect, openness; tolerance for ambiguity, among other, that are all attributes of IC.

Abstract
Proliferative diabetic retinopathy (PDR) is an advanced diabetic retinopathy stage, characterized by neovascularization, which leads to ocular complications and severe vision loss. However, the available DR-labeled retinal image datasets have a small representation of images of the severest DR grades, and thus there is lack of PDR cases for training DR grading models. Additionally, the criteria for labelling these images in the publicly available datasets is not always clear, with some images which do not show typical PDR lesions being labeled as PDR due to the presence of photo-coagulation treatment and laser marks. This problem, together with the datasets' high class imbalance, leads to a limited variability of the samples, which the typical data augmentation and class balancing cannot fully mitigate. We propose a heuristic-based data augmentation scheme based on the synthesis of neovessel (NV)-like structures that compensates for the lack of PDR cases in DR-labeled datasets. The proposed neovessel generation algorithm relies on the general knowledge of common location and shape of these structures. NVs are generated and introduced in pre-existent retinal images which can then be used for enlarging deep neural networks' training sets. The data augmentation scheme was tested on multiple datasets, and allows to improve the model's capacity to detect NVs.

Abstract
The exchange of academic marks between HEIs (Higher Education Institutions) is mandatory in every student mobility programs (i.e. the EU Erasmus Program) but that process remains to present date with insufficient technological support and the absence of a comprehensive reference model that allows the integration of potential technological solutions for the exchange of academic data with existing Academic Information Systems seems to limit greatly the possibility of adopting solutions of this type referred to in the existing literature. This work addresses this issue, conducting an initial bibliographic review aimed at the identification of the fundamental requirements of such an architecture as well as explores some of the technologies that are showing potential for usage in the safe exchange of academic results between partner HEIs, with particular interest in blockchain technology applied in an educational context.