Abstract
The need for faster and energy-efficient computing technologies has recently pushed for major developments on alternative computing paradigms to the common von Neumann architecture. Amongst those, reservoir computing framework is an emerging concept that leverages a simple training process and eases transference to hardware implementations, allowing any given nonlinear physical system to act as a computing platform. In this work, we explore how we can make use of a discrete chain of solitons to create an effective reservoir computing framework, investigating not only the ability to learn data but also to predict models depending on the strength of the nonlinear interaction of the media. Probing the role of the nonlinear separation for tasks involving nonlinear separable data, these results open new possibilities for a multitude of physical implementations in the context of optical sciences, from optical fibers to nonlinear crystals.

Abstract
As AI algorithms thrive on data, SCADA would be considered a natural ground for Artificial Intelligence (AI) applications to be developed, translating that avalanche of information into meaningful and fast insights to human operators. However, presently, the high complexity of the events, the data semantics, the large variety of equipment and technologies translate into very few AI applications developed in SCADA. Aware of the enormous potential yet to be explored, E-REDES partnered with INESC TEC to experiment on the development of two novel AI applications based on SCADA data. The first tool, called Alarm2Insights, identifies anomalous behaviours regarding the performance of the protection functions associated with HV and MV line panels. The second tool, called EventProfiler, uses unsupervised learning to identify similar events (i.e., with similar log messages) in HV line panels, and supervised learning to classify new events into previously defined clusters and detect unique or rare events. Aspects associated to data handling and pre-processing are also discussed. The project's results show a very promising potential of applying AI to SCADA data, enhancing the role of the operator and support him in doing better and more informed decisions. © 2021 The Institution of Engineering and Technology.

Abstract
Due to added mobility and the increase in international students worldwide, as well as the current problem regarding the counterfeiting of diplomas and the selling of fraudulent certificates, we propose a technological solution. Namely, to ally blockchain technology to higher education certificates and diplomas, to make the process of checking for academic qualifications more facilitated and transparent. Employers of graduates, as well as higher education institutions which evaluate course applicants, would benefit. Perhaps equally as important, students applying for international degree programs would have their lives simplified. There is an increased pressure to ensure the legitimacy and authenticity of certifications and diplomas-and preferably without the current "hassle" of getting diplomas recognized by official entities. New technological advances, with the development of blockchain and smart contracts, with their characteristics of immutability, decentralization, security, traceability, and consensus, may be considered an excellent match to implement a robust and reliable anti-fraud solution to issue digital diplomas. Radical innovations, such as linking blockchain and higher education diplomas, involve significant change and novelty. Linking blockchain and higher education diplomas could potentially positively impact and benefit millions of people worldwide, especially the younger generations. This study involved a literature review and the searching of the Scopus database (refereed publications) for the following concepts: blockchain and diploma. Existing literature is recent, with most articles (25) published between 2019 and 2020, with 4 in 2018 and only 1 in 2017. This was aligned with our expectations since the development of blockchain utilization outside financial and crypto-assets industries is recent, and it is known as "Blockchain 3.0". We can additionally affirm that the topic is attracting attention and efforts from researchers worldwide and that some higher education institutions have already implemented ad hoc solutions. As it is, the sector lacks a unified response to the problem of automatic and reliable higher education diploma certification.

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Abstract
Modern web applications replicate their data across the globe and require strong consistency guarantees for their most critical data. These guarantees are usually provided via state-machine replication (SMR). Recent advances in SMR have focused on leaderless protocols, which improve the availability and performance of traditional Paxos-based solutions. We propose Tempo - a leaderless SMR protocol that, in comparison to prior solutions, achieves superior throughput and offers predictable performance even in contended workloads. To achieve these benefits, Tempo timestamps each application command and executes it only after the timestamp becomes stable, i.e., all commands with a lower timestamp are known. Both the timestamping and stability detection mechanisms are fully decentralized, thus obviating the need for a leader replica. Our protocol furthermore generalizes to partial replication settings, enabling scalability in highly parallel workloads. We evaluate the protocol in both real and simulated geo-distributed environments and demonstrate that it outperforms state-of-the-art alternatives.

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