Automated Service Composition Using AI Planning and Beyond

Automated Service Composition is one of the ``grand challenges'' in the area of Service-Oriented Computing. Mike Papazoglou was not only one of the first researchers who identified the importance of the problem, but was also one of the first proposers of formulating it as an AI planning problem. Unfortunately, classical planning algorithms were not sufficient and a number of extensions were needed, e.g., to support extended (rich) goal languages to capture the user intentions, to plan under uncertainty caused by the non-deterministic nature of services; issues that where formulated (and, partially addressed) by Mike, being one of his key contributions to the service community

Employing Crowdsourcing for Enriching a Music Knowledge Base in Higher Education

This paper describes the methodology followed and the lessons learned from employing crowdsourcing techniques as part of a homework assignment involving higher education students of computer science. Making use of a platform that supports crowdsourcing in the cultural heritage domain students were solicited to enrich the metadata associated with a selection of music tracks. The results of the campaign were further analyzed and exploited by students through the use of semantic web technologies. In total, 98 students participated in the campaign, contributing more than 6400 annotations concerning 854 tracks. The process also led to the creation of an openly available annotated dataset, which can be useful for machine learning models for music tagging. The campaign's results and the comments gathered through an online survey enable us to draw some useful insights about the benefits and challenges of integrating crowdsourcing into computer science curricula and how this can enhance students' engagement in the learning process.Comment: To be published in The 4th International Conference on Artificial Intelligence in Education Technology (AIET 2023), Berlin, Germany, 31 June-2 July 2023. For The GitHub code for the created music dataset, see https://github.com/vaslyb/MusicCro