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

Abstract

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
Precise machine learning models for the early identification of anomalies based on biosignal data retrieved from bedside monitors could improve intensive care, by helping clinicians make decisions in advance and produce on-time responses. However, traditional models show limitations when dealing with the high complexity of this task. Layered Learning (LL) emerges as a solution, as it consists of the hierarchical decomposition of the problem into simpler tasks. This paper explores the uncovered potential of LL in the early detection of Acute Hypotensive Episodes (AHEs). We leverage information from the MIMIC-III Database to test different subdivisions of the main task and study how to combine the outcomes from distinct layers. In addition to this, we also test a novel approach to reduce false positives in AHE predictions.