Meaningfully automating sociotechnical business collaboration promises efficiency-, effectiveness-, and quality increases for realizing next-generation decentralized autonomous organizations. For automating business-process aware cross-organizational operations, the development of existing choreography languages is technology driven and focuses less on sociotechnical suitability and expressiveness concepts and properties that recognize the interaction between people in organizations and technology in workplaces. This gap our suitability-and expressiveness exploration fills by means of a cross-organizational collaboration ontology that we map as a proof-of-concept evaluation to the eSourcing Markup Language (eSML). The latter we test in a feasibility case study to meaningfully support the automation of business collaboration. The developed eSourcing ontology and eSML is replicable for exploring strengths and weaknesses of other choreography languages.

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Big data analytics and knowledge management is becoming a hot topic with the emerging techniques of cloud computing and big data computing model such as MapReduce. However, large-scale adoption of MapReduce applications on public clouds is hindered by the lack of trust on the participating virtual machines deployed on the public cloud. In this paper, we extend the existing hybrid cloud MapReduce architecture to multiple public clouds. Based on such architecture, we propose IntegrityMR, an integrity assurance framework for big data analytics and management applications. We explore the result integrity check techniques at two alternative software layers: the MapReduce task layer and the applications layer. We design and implement the system at both layers based on Apache Hadoop MapReduce and Pig Latin, and perform a series of experiments with popular big data analytics and management applications such as Apache Mahout and Pig on commercial public clouds (Amazon EC2 and Microsoft Azure) and local cluster environment. The experimental result of the task layer approach shows high integrity (98% with a credit threshold of 5) with non-negligible performance overhead (18% to 82% extra running time compared to original MapReduce). The experimental result of the application layer approach shows better performance compared with the task layer approach (less than 35% of extra running time compared with the original MapReduce).

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The unprecedented outbreak of the Corona Virus Disease 2019 (COVID-19) pandemic has seriously affected numerous countries in the world from various aspects such as education, economy, social security, public health, etc. Most governments have made great efforts to control the spread of COVID-19, e.g., locking down hard-hit cities and advocating masks for the population. However, some countries and regions have relatively poor medical conditions in terms of insufficient medical equipment, hospital capacity overload, personnel shortage, and other problems, resulting in the large-scale spread of the epidemic. With the unique advantages of Artificial Intelligence (AI), it plays an extremely important role in medical imaging, clinical data, drug development, epidemic prediction, and telemedicine. Therefore, AI is a powerful tool that can help humans solve complex problems, especially in the fight against COVID-19. This study aims to analyze past research results and interpret the role of Artificial Intelligence in the prevention and treatment of COVID-19 from five aspects. In this paper, we also discuss the future development directions in different fields and prove the validity of the models through experiments, which will help researchers develop more efficient models to control the spread of COVID-19.

Based on attribution theory, this paper explores the mechanism behind the influence of hotel electronic word-of-mouth dispersion on consumer order decision. Results show that (1) discrete electronic word-of-mouth negatively impact the order decision; (2) attribution selection could mediate the effect of electronic word-of-mouth dispersion on order decision of consumers; (3) independent self-construal weakens the negative effect of electronic word-of-mouth dispersion on order decision of consumers; and (4) consumers with high endowment reduce the tendency of electronic word-of-mouth dispersion due to online supporter reviews. Findings not only contribute to electronic word-of-mouth dispersion studies in the field of consumer behaviour, but also provide theoretical guidance and reference for hotel order management based on electronic word-of-mouth.