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The present Special Issue of the Journal of Logical and Algebraic Methods in Programming was planned as a tribute to Jose Manuel Esgalhado Valenca on the occasion of his Jubilation. A tribute to a professor, in the deepest sense of the word, a colleague and a friend, but above all to a long and inspiring academic journey that has so profoundly shaped the development of Informatics as a scientific area in Portugal. A scientific area that, as he taught us, needs to be understood broadly: not only as an independent research domain, but also as an educational pillar, a strategy for social and economic development, a foundation for a multifaceted professional career. This preface introduces some steps of such a journey. The Special Issue features a selection of scientific papers written by his collaborators, colleagues and friends, covering the different areas Jose Valenca helped to launch and consolidate in Portugal, namely computational logic, verification and mechanized reasoning, and information security. (c) 2022 Published by Elsevier Inc.

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The Communications website, http://cacm.acm.org, features more than a dozen bloggers in the BLOG@CACM community. In each issue of Communications , we'll publish selected posts or excerpts. twitter Follow us on Twitter at http://twitter.com/blogCACM http://cacm.acm.org/blogs/blog-cacm Carlos Baquero offers guidance on how to decide where to publish one's paper.

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Carlos Baquero on whether using artificial intelligence provides an unfair advantage to writers.

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Abstract Multiple COVID-19 diagnosis methods based on information collected from patients have been proposed during the global pandemic crisis, with the aim of providing medical staff with quick diagnosis tools to efficiently plan and manage the limited healthcare resources. In general, these methods have been developed to detect COVID-19 positive cases from a particular combination of reported symptoms, and have been evaluated using datasets extracted from different studies with different characteristics. On the other hand, the University of Maryland, in partnership with Facebook, launched the Global COVID-19 Trends and Impact Survey (UMD-CTIS), the largest health surveillance tool to date that has collected information from 114 countries/territories since April 2020. This survey captured various individual features including gender, age groups, self-reported symptoms, isolation measures, and mental health status, among others. In this paper, we compare the performance of different proposed COVID-19 diagnosis methods using the information collected by UMD-CTIS, for the years 2020 and 2021, in five countries: Brazil, Canada, Germany, Japan, and South Africa. The evaluation of these methods with homogeneous data across countries and years provides a solid and consistent comparison among them.