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Typically, the two main activities in runtime verification efforts are the process of creating monitors from specifications, and the algorithms for the evaluation of traces against the generated monitors. Other activities involve the instrumentation of the system to generate the trace and the communication between the system under analysis and the monitor. Most of the applications in runtime verification have been focused on the dynamic analysis of software, even though there are many more potential applications to other computational devices and target systems. In this paper we present a collection of challenges for runtime verification extracted from concrete application domains, focusing on the difficulties that must be overcome to tackle these specific challenges. The computational models that characterize these domains require to devise new techniques beyond the current state of the art in runtime verification.<\/jats:p>","DOI":"10.1007\/s10703-019-00337-w","type":"journal-article","created":{"date-parts":[[2019,8,9]],"date-time":"2019-08-09T16:03:46Z","timestamp":1565366626000},"page":"279-335","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":90,"title":["A survey of challenges for runtime verification from advanced application domains (beyond software)"],"prefix":"10.1007","volume":"54","author":[{"given":"C\u00e9sar","family":"S\u00e1nchez","sequence":"first","affiliation":[]},{"given":"Gerardo","family":"Schneider","sequence":"additional","affiliation":[]},{"given":"Wolfgang","family":"Ahrendt","sequence":"additional","affiliation":[]},{"given":"Ezio","family":"Bartocci","sequence":"additional","affiliation":[]},{"given":"Domenico","family":"Bianculli","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Colombo","sequence":"additional","affiliation":[]},{"given":"Yli\u00e8s","family":"Falcone","sequence":"additional","affiliation":[]},{"given":"Adrian","family":"Francalanza","sequence":"additional","affiliation":[]},{"given":"Sr\u0111an","family":"Krsti\u0107","sequence":"additional","affiliation":[]},{"given":"Joa\u030bo M.","family":"Louren\u00e7o","sequence":"additional","affiliation":[]},{"given":"Dejan","family":"Nickovic","sequence":"additional","affiliation":[]},{"given":"Gordon J.","family":"Pace","sequence":"additional","affiliation":[]},{"given":"Jose","family":"Rufino","sequence":"additional","affiliation":[]},{"given":"Julien","family":"Signoles","sequence":"additional","affiliation":[]},{"given":"Dmitriy","family":"Traytel","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Weiss","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,11,11]]},"reference":[{"key":"337_CR1","unstructured":"Abadi M, Andersen DG (2016) Learning to protect communications with adversarial neural cryptography. 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