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Front Matter - SEAMS 2017 Artifacts, Table of Contents, Preface, Artifact Evaluation Committee., , and . Dagstuhl Artifacts Ser., 3 (1): 00:-1-00:-12 (2017)CaRE: Finding Root Causes of Configuration Issues in Highly-Configurable Robots., , , , , , , , and . CoRR, (2023)An Aspect-Oriented Adaptation Framework for Dynamic Component Evolution., , , and . WCAT@ECOOP, volume 189 of Electronic Notes in Theoretical Computer Science, page 21-34. Elsevier, (2006)Explaining Architectural Design Tradeoff Spaces: A Machine Learning Approach., , , and . ECSA, volume 12857 of Lecture Notes in Computer Science, page 49-65. Springer, (2021)Behavioural self-adaptation of services in ubiquitous computing environments., , and . SEAMS, page 28-37. IEEE Computer Society, (2009)Generality vs. reusability in architecture-based self-adaptation: the case for self-adaptive microservices., , , and . ECSA (Companion), page 18:1-18:6. ACM, (2018)Machine learning meets quantitative planning: enabling self-adaptation in autonomous robots., , , , and . SEAMS@ICSE, page 39-50. ACM, (2019)Multiscale time abstractions for long-range planning under uncertainty., , , and . SEsCPS@ICSE, page 15-21. ACM, (2016)Towards a Formal Framework for Hybrid Planning in Self-Adaptation., , , and . SEAMS@ICSE, page 109-115. IEEE Computer Society, (2017)Towards Proactive Decentralized Adaptation of Unmanned Aerial Vehicles for Wildfire Tracking., , and . SEAMS@ICSE, page 56-62. ACM, (2024)