Modeling human learning involved in car driving

In this paper, car driving is considered at the level of human tracking and maneuvering in the context of other traffic. A model analysis revealed the most salient features determining driving performance and safety. Learning car driving is modelled based on a system theoretical approach and based on a neural network approach. The aim of this research is to assess the relative merit of both approaches to describe human learning behavior in car driving specifically and in operating dynamic systems in general

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Improved computation of individual ZPD in a distance learning system

This paper builds upon theoretical studies in the field of social constructivism. Lev Vygotsky is considered one of the greatest representatives of this research line, with his theory of the Zone of Proximal Development (ZPD). Our work aims at integrating this concept in the practice of a computer-assisted learning system. For each learner, the system stores a model summarizing the current Student Knowledge (SK). Each educational activity is specified through the deployed content, the skills required to tackle it, and those acquired, and is further annotated by the effort estimated for the task. The latter may change from one student to another, given the already achieved competence. A suitable weighting of the robustness (certainty) of student’s skills, stored in SK, and their combination are used to verify the inclusion of a learning activity in the student’s ZPD. With respect to our previous work, the algorithm for the calculation of the ZPD of the individual student has been optimized, by enhancing the certainty weighting policy, and a graphical display of the ZPD has been added. Thanks to the latter, the student can get a clear vision of the learning paths that he/she can presently tackle. This both facilitates the educational process, and helps developing the metacognitive ability self-assessment

The safety case and the lessons learned for the reliability and maintainability case

This paper examine the safety case and the lessons learned for the reliability and maintainability case

Discriminatively Trained Latent Ordinal Model for Video Classification

We study the problem of video classification for facial analysis and human action recognition. We propose a novel weakly supervised learning method that models the video as a sequence of automatically mined, discriminative sub-events (eg. onset and offset phase for "smile", running and jumping for "highjump"). The proposed model is inspired by the recent works on Multiple Instance Learning and latent SVM/HCRF -- it extends such frameworks to model the ordinal aspect in the videos, approximately. We obtain consistent improvements over relevant competitive baselines on four challenging and publicly available video based facial analysis datasets for prediction of expression, clinical pain and intent in dyadic conversations and on three challenging human action datasets. We also validate the method with qualitative results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text overlap with arXiv:1604.0150