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Aligning UML 2.0 State Machines and Temporal Logic for the Efficient Execution of Services.

, , and . OTM Conferences (2), volume 4276 of Lecture Notes in Computer Science, page 1613-1632. Springer, (2006)

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Machine Learning in IoT for Autonomous, Adaptive Sensing., , and . ERCIM News, (2017)Adaptive, Correlation-Based Training Data Selection for IoT Device Management., , and . IoTSMS, page 169-176. IEEE, (2019)Information-driven adaptive sensing based on deep reinforcement learning., , , and . IOT, page 2:1-2:8. ACM, (2020)A Measurement-Driven Approach to Understand Urban Greenhouse Gas Emissions in Nordic Cities., , , , and . NIK, Bibsys Open Journal Systems, Norway, (2016)Towards a model-driven method for reliable applications: from ideal to realistic transmission semantics., , and . SERENE, page 2-11. ACM, (2010)Energy Consumption Estimation for Energy-Aware, Adaptive Sensing Applications., , and . MSPN, volume 10566 of Lecture Notes in Computer Science, page 222-235. Springer, (2017)Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting., , , and . Sensors, 21 (23): 8009 (2021)Container-Based IoT Architectures: Use Case for Visual Person Counting., , , and . NAIS, volume 3431 of CEUR Workshop Proceedings, CEUR-WS.org, (2023)Automatic Generation of Compatible Interfaces from Partitioned UML Activities.. SDL Forum, volume 5719 of Lecture Notes in Computer Science, page 182-199. Springer, (2009)An Analysis of Design Parameters for Energy Management of Wireless Sensor Devices., , , , and . ICIT, page 1-8. IEEE, (2022)