Abstract
Online tourism crowdsourcing platforms, such as AirBnB, Expedia or TripAdvisor, rely on the continuous data sharing by tourists and businesses to provide free or paid value-added services. When adequately processed, these data streams can be used to explain and support businesses in the early identification of trends as well as prospective tourists in obtaining tailored recommendations, increasing the confidence in the platform and empowering further end-users. However, existing platforms still do not embrace the desired accountability, responsibility and transparency (ART) design principles, underlying to the concept of sustainable tourism. The objective of this work is to study this problem, identify the most promising techniques which follow these principles and design a novel ART-compliant processing pipeline. To this end, this work surveys: (i) real-time data stream mining techniques for recommendation and trend identification; (ii) trust and reputation (T&R) modelling of data contributors; (iii) chained-based storage of trust models as smart contracts for traceability and authenticity; and (iv) trust- and reputation-based explanations for a transparent and satisfying user experience. The proposed pipeline redesign has implications both to digital and to sustainable tourism since it advances the current processing of tourism crowdsourcing platforms and impacts on the three pillars of sustainable tourism.

Abstract

Abstract
There is a literature gap regarding the period representativeness bias associated with sample selection in longitudinal bibliometric studies. The purpose of this paper is to analyse and compare, in terms of period representativeness, the common methods used for selecting a sample of the highly impactful papers in a field/ journal. Using 92 593 papers (Information Science & Library Science area, 1977-2016), we compared, in terms of the number of papers/year, samples of the 100 most impactful papers, obtained with different selection options. We repeated the analysis also for Top500, Top2000, and Top20000. This study shows that the frequently used metrics to compare the impact of papers and to select a sample of spacing diaeresis most impactful papers p spacing diaeresis ublished in each year and each field may privilege specific periods while neglecting others. The main result of our study is that the percentile citation-based method reduces this y spacing diaeresis ear of publicationr spacing diaeresis epresentativeness bias. This paper draws attention to the importance of the sample selection, in bibliometric studies, and to the period representativeness bias associated with different choices to select the spacing diaeresis most impactful papers. spacing diaeresis

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Abstract
São diversos os benefícios da utilização de indicadores e métricas de sustentabilidade organizacional,e referem-se à possível antecipação de condições e tendências, fornecimento de avisos de possíveis ocorrências e situações que evitem danos aos aspectos econômico, social e ambiental e auxílio nos processos de gestão. O objetivo deste estudo foi analisar as métricas de sustentabilidade organizacional, identificadas na literatura acadêmica nos últimos cinco anos. Seguindo a metodologia PRISMA, foram relacionadas sete pesquisas relevantes e enquadradas nos critérios de seleção. As principais contribuições, por meio da revisão sistemática, apontaram para estudos compostos de diferentes aspectos (empresas, cidades, regiões) ligados à sustentabilidade. Foram diversas métricas identificadas, algumas focando mais aspectos sociais da população e ou funcionários e outras mais os aspectos ambientais e seus impactos na sociedade e nas organizações. As limitações do estudo são decorrentes do método escolhido que considerou os últimos cinco anos na base Scopus e artigos completos em inglês.

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.