People identification using gait information (i.e. the way a person walks) obtained from inertial sensors is a robust approach that can be used in multiple situations where vision-based systems are not applicable. Typically, previous methods use handcrafted features or deep learning approaches with pre-processed features as input. In contrast, we present a new deep learningbased end-to-end approach that employs raw inertial data as input. By this way, our approach is able to automatically learn the best representations without any constraint introduced by the pre-processed features. Moreover, we study the fusion of information from multiple inertial sensors and multi-task learning from multiple labels per sample. Our proposal is experimentally validated on the challenging dataset OU-ISIR, which is the largest available dataset for gait recognition using inertial information. After conducting an extensive set of experiments to obtain the best hyper-parameters, our approach is able to achieve state-ofthe- art results. Specifically, we improve both the identification accuracy (from 83.8% to 94.8%) and the authentication Equal- Error-Rate (from 5.6 to 1.1). Our experimental results suggest that (i) the use of hand-crafted features is not necessary for this task as deep learning approaches using raw data achieve better results; (ii) the fusion of information from multiple sensors allows to improve the results; and, (iii) multi-task learning is able to produce a single model that obtains similar or even better results in multiple tasks than the corresponding models trained for a single task.

This paper describes how a new Moodle module, called CTPracticals, is applied to the teaching of the practical content of a basic computer organization course. In the core of the module, an automatic verification engine enables it to process the VHDL designs automatically as they are submitted. Moreover, a straightforward modification of this engine would make it possible to extend its application to other programming languages. The module provides students with real-time knowledge of the state of their work by their accessing the result of the automatic assessment or feedback messages. Teachers have a constant global view of the status of their class and have available multiple options such as sending feedback messages to students, obtaining statistics, launching additional verifications in batch, and so on. Likewise, the module substantially improves some organizational aspects, and its design may help teachers to encourage teamwork. Its use partially frees teachers from certain routine work, saving time that can be devoted to teaching objectives and tutoring activities.