In this article, we present an integrated instructive methodological approach. We begin with a set of proposals for educational innovation oriented towards active learning that have been tested separately and implemented for various subjects in courses of different levels. The approach integrates the following elements: (1) the dynamic generation of digital content by students and their integration into shared knowledge bases of the subjects involved; (2) the systematic use of quality content, mainly in video format, distributed through online platforms as support for flipped classrooms; (3) peer evaluation to support the development of reflective and selfcritical capacities; and (4) systematic collaboration with students and professors from other universities to develop the enumerated activities. The methodology has been tested in a variety of subjects, thanks to its flexibility. In all experienced cases, it has been shown that it is feasible for students to generate enough valuable and reusable content. In addition, students have expressed high levels of satisfaction with the implementation of the proposal.
Human-machine interaction is increasingly dependent on speech communication. Machine Learning models are usually applied to interpret human speech commands. However, these models can be fooled by adversarial examples, which are inputs intentionally perturbed to produce a wrong prediction without being noticed. While much research has been focused on developing new techniques to generate adversarial perturbations, less attention has been given to aspects that determine whether and how the perturbations are noticed by humans. This question is relevant since high fooling rates of proposed adversarial perturbation strategies are only valuable if the perturbations are not detectable. In this paper we investigate to which extent the distortion metrics proposed in the literature for audio adversarial examples, and which are commonly applied to evaluate the effectiveness of methods for generating these attacks, are a reliable measure of the human perception of the perturbations. Using an analytical framework, and an experiment in which 18 subjects evaluate audio adversarial examples, we demonstrate that the metrics employed by convention are not a reliable measure of the perceptual similarity of adversarial examples in the audio domain.
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