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PresenceSense: zero-training algorithm for individual presence detection based on power monitoring.

, , , , and . BuildSys@SenSys, page 1-10. ACM, (2014)

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Inverse modeling of non-cooperative agents via mixture of utilities., , , , and . CDC, page 6327-6334. IEEE, (2016)Poisoning Attacks on Data-Driven Utility Learning in Games., , , and . ACC, page 5774-5780. IEEE, (2018)Statistical Learning Towards Gamification in Human-Centric Cyber-Physical Systems.. University of California, Berkeley, USA, (2018)base-search.net (ftcdlib:qt2cq3q9bz).A Novel Graphical Lasso Based Approach Towards Segmentation Analysis in Energy Game-Theoretic Frameworks., , , , , and . ICMLA, page 1702-1709. IEEE, (2019)REST: a reliable estimation of stopping time algorithm for social game experiments., , , , and . ICCPS, page 90-99. ACM, (2015)Improving Device Directedness Classification of Utterances With Semantic Lexical Features., , , , and . ICASSP, page 7859-7863. IEEE, (2020)Leveraging correlations in utility learning., , , and . ACC, page 5249-5256. IEEE, (2017)Diagnosing wind turbine faults using machine learning techniques applied to operational data., , , , and . ICPHM, page 1-8. IEEE, (2016)PresenceSense: zero-training algorithm for individual presence detection based on power monitoring., , , , and . BuildSys@SenSys, page 1-10. ACM, (2014)Using Domain Knowledge Features for Wind Turbine Diagnostics., , , , , and . ICMLA, page 300-307. IEEE Computer Society, (2016)