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Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems., , , and . CoRR, (2022)Data Analytics for Health and Connected Care: Ontology, Knowledge Graph and Applications., , , , , , , , , and 3 other author(s). PervasiveHealth, volume 488 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, page 344-360. Springer, (2022)Do Not Sleep on Linear Models: Simple and Interpretable Techniques Outperform Deep Learning for Sleep Scoring., , , , , , and . CoRR, (2022)Plotly-Resampler: Effective Visual Analytics for Large Time Series., , , and . CoRR, (2022)tsdownsample: high-performance time series downsampling for scalable visualization., , and . CoRR, (2023)MinMaxLTTB: Leveraging MinMax-Preselection to Scale LTTB., , , and . CoRR, (2023)Applying deep learning to reduce large adaptation spaces of self-adaptive systems with multiple types of goals., , , , and . SEAMS@ICSE, page 20-30. ACM, (2020)Effective Decision Making in Self-adaptive Systems Using Cost-Benefit Analysis at Runtime and Online Learning of Adaptation Spaces., , , and . ENASE (Selected Papers), volume 1023 of Communications in Computer and Information Science, page 373-403. Springer, (2018)Data Point Selection for Line Chart Visualization: Methodological Assessment and Evidence-Based Guidelines., , , and . CoRR, (2023)Powershap: A Power-Full Shapley Feature Selection Method., , , and . ECML/PKDD (1), volume 13713 of Lecture Notes in Computer Science, page 71-87. Springer, (2022)