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In the Internet of Things (IoT), the large volume of data generated by sensors poses significant computational challenges in resource-constrained environments. Most existing machine learning algorithms are unable to train a proper model using a significant ...
We propose fingerprinting, a new technique that consists in constructing compact, fast-to-compute and privacy-preserving binary representations of datasets. We illustrate the effectiveness of our approach on the emblematic big data problem of K-Nearest-Nei ...
Purpose of review The goal of our nation-wide initiative is to provide clinicians intuitive and robust tools for accurate diagnosis, therapy monitoring and prognosis of cognitive decline that is based on large-scale multidomain data. Recent findings We des ...
We consider a quadrotor equipped with a forward-facing camera, and an user freely moving in its proximity; we control the quadrotor in order to stay in front of the user, using only camera frames. To do so, we train a deep neural network to predict the dro ...
Convolutional neural networks (CNNs) based approaches for semantic alignment and object landmark detection have improved their performance significantly. Current efforts for the two tasks focus on addressing the lack of massive training data through weakly ...
Speech Emotion Recognition (SER) has been shown to benefit from many of the recent advances in deep learning, including recurrent based and attention based neural network architectures as well. Nevertheless, performance still falls short of that of humans. ...
Here we demonstrate the suitability of a local mutual information measure for estimating the temporal dynamics of cross-frequency coupling (CFC) in brain electrophysiological signals. In CFC, concurrent activity streams in different frequency ranges intera ...
Classifiers used in the wild, in particular for safety-critical systems, should not only have good generalization properties but also should know when they don't know, in particular make low confidence predictions far away from the training data. We show t ...
Location Privacy-Preserving Mechanisms (LPPMs) in the literature largely consider that users' data available for training wholly characterizes their mobility patterns. Thus, they hardwire this information in their designs and evaluate their privacy propert ...
Faults in the brittle crust constitute preexisting weakness zones that can be reactivated depending on their friction, orientation within the local stress field, and stress field magnitude. Analytical approaches to evaluate the potential for fault reactiva ...
