Abstract: A device for training a privacy-preserving generative model (Ct) for management of data privacy configured to generate a synthetic time series, the synthetic time series being defined by a length, a sequence of timestamps and a sequence of data of structure type, each data of structure type including a n-tuple of features.

Abstract: A device for providing a counterfactual explanation (E) of an original decision (D) from an automated decision-making system based on a model of classifier type for decision optimization.

Abstract: Data samples are selected for power consumption management. This includes receiving samples associated with respective times, distributed in a sliding time window as current samples and in a past period as past samples. Selected past samples are determined by keeping a first share of the past samples, including most recent ones, and a second share through eliminating among the past samples deprived from the first share, called a complementary share, part of the past samples in function of at least some of the current samples and of elimination conditions depending on similarity criteria applied to at least the first and complementary shares. The selected past samples are provided with the current samples for power consumption management. Applications to power failure detection and power consumption dynamic adaptation.

Abstract: Data samples related to health management are selected. This includes receiving samples associated with respective times, distributed in a sliding time window as current samples and in a past period as past samples. Selected past samples are determined by keeping a first share of the past samples, including the most recent ones, and a second share through eliminating among the past samples deprived from the first share, called a complementary share, part of the past samples in function of at least some of the current samples and of elimination conditions depending on similarity criteria applied to at least the first and complementary shares. The selected past samples are provided with the current samples for performing therapy-directed tasks. Also, applications to medical diagnosis, therapeutic treatment, medical rehabilitation and drug development.

Abstract: Data samples are selected for power consumption management. This includes receiving samples associated with respective times, distributed in a sliding time window as current samples and in a past period as past samples. Selected past samples are determined by keeping a first share of the past samples, including most recent ones, and a second share through eliminating among the past samples deprived from the first share, called a complementary share, part of the past samples in function of at least some of the current samples and of elimination conditions depending on similarity criteria applied to at least the first and complementary shares. The selected past samples are provided with the current samples for power consumption management. Applications to power failure detection and power consumption dynamic adaptation.