Abstract
This paper uses non-traditional data, from a MicroFinance Institution (MFI), in a Credit Scoring loan classification problem and addresses a common problem in emerging markets of the lack of a verifiable customers' credit history. We perform a set of experiments to define a baseline model and prove the relevance of node embedding features, in credit scoring models, using a real world dataset.

Abstract
HARMONI is the adaptive optics assisted, near-infrared and visible light integral field spectrograph for the Extremely Large Telescope (ELT). A first light instrument, it provides the work-horse spectroscopic capability for the ELT. As the project approaches its Final Design Review milestone, the design of the instrument is being finalized, and the plans for assembly, integration and testing are being detailed. We present an overview of the instrument's capabilities from a user perspective, provide a summary of the instrument's design, including plans for operations and calibrations, and provide a brief glimpse of the predicted performance for a specific observing scenario. The paper also provides some details of the consortium composition and its evolution since the project commenced in 2015.

Abstract

Abstract
While the objectives of forest management vary widely and include the protection of resources in protected forests and nature reserves, the primary objective has often been the production of wood products. However, even in this case, forests play a key role in the conservation of living resources. Constraining the areas of clearcuts contributes to this conservation, but if it is too restrictive, a dispersion of small clearcuts across the forest might occur, and forest fragmentation might be a serious ecological problem. Forest fragmentation leads to habitat loss, not only because the forest area is reduced, but also because the core area of the habitats and the connectivity between them decreases. This study presents a Monte Carlo tree search method to solve a bi-objective harvest scheduling problem with constraints on the clearcut area, total habitat area and total core area inside habitats. The two objectives are the maximization of both the net present value and the probability of connectivity index. The method is presented as an approach to assist the decision maker in estimating efficient alternative solutions and the corresponding trade-offs. This approach was tested with instances for forests ranging from some dozens to over a thousand stands and temporal horizons from three to eight periods. In general, multi-objective Monte Carlo tree search was able to find several efficient alternative solutions in a reasonable time, even for medium and large instances.

Abstract
Software requirements engineers and testers generally define technical documents in natural languages, but this practice can lead to inconsistencies between the documentation and the consequent system implementation. Previous research has shown that writing requirements and tests in a structured way, with controlled natural languages like RSL, can help mitigate these problems. This study goes further, discussing new experiments carried out to validate that RSL (with its complementary tools, called “ITLingo Studio”) can be applied in different systems and technologies, namely the possibility of applying the approach to integrate test automation capabilities in security testing. The preliminary conclusion indicates that, by combining tools such as ITLingo Studio and the Robot Framework, it is possible to integrate requirements and test specifications with test automation, and that would bring benefits in the testing process’ productivity.

Abstract
The modern digital era is characterized by a plethora of emerging technologies, methodologies and techniques that are employed in the manufacturing industries with intent to improve productivity, to optimize processes and to reduce operational costs. Yet, algorithms and methodological approaches for improvement of energy consumption and environmental impact are not integrated with the current operational and planning tools used by manufacturing companies. One possible reason for this is the difficulty in bridging the gap between the most advanced energy related ICT tools, developed within the scope of the industry 4.0 era, and the legacy systems that support most manufacturing operational and planning processes. Consequently, this paper proposes a conceptual architecture model for a digital energy management platform, which is comprised of an IIoT-based platform, strongly supported by energy digital twin for interoperability and integrated with AI-based energy data-driven services. This conceptual architecture model enables companies to analyse their energy consumption behaviour, which allows for the understanding of the synergies among the variables that affect the energy demand, and to integrate this energy intelligence with their legacy systems in order to achieve a more sustainable energy demand. © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the FAIM 2021.