This paper deals with imbalanced time series classification problems. In particular, we propose to learn time series classifiers that maximize the minimum recall of the classes rather than the accuracy. Consequently, we manage to obtain classifiers which tend to give the same importance to all the classes. Unfortunately, for most of the traditional classifiers, learning to maximize the minimum recall of the classes is not trivial (if possible), since it can distort the nature of the classifiers themselves. Neural networks, in contrast, are classifiers that explicitly define a loss function, allowing it to be modified. Given that the minimum recall is not a differentiable function, and therefore does not allow the use of common gradient-based learning methods, we apply and evaluate several smooth approximations of the minimum recall function. A thorough experimental evaluation shows that our approach improves the performance of state-of-the-art methods used in imbalanced time series classification, obtaining higher recall values for the minority classes, incurring only a slight loss in accuracy.

The objective of the FRACTAL project is to create a new approach to reliable edge computing. The computing node will be the building block of scalable Internet of Things (from Low Computing to High Computing Edge Nodes). The cognitive skill will be given by an internal and external architecture that allows forecasting its internal performance and the state of the surrounding world. The node will have the capability of learning how to improve its performance against the uncertainty of the environment. New industrial functions will flourish through the created space of the cognitive system. Cognitive advantages are brought to a resilient edge and a computing paradigm that lay down between the physical world and the cloud.

The dramatic increase in the amount of information stored on the web makes it more important to familiarize people with disabilities and elderly people with digital devices and applications and to adapt websites to enable their use by these users. Discapnet is a website mainly aimed at visually disabled people, and navigation is a challenging task for its users. In this context, system evaluation and problem detection become crucial aspects for enhancing user experience and may contribute greatly to diminishing the existing technological gap. This study proposes a system based on web‐mining techniques that collects in‐use information while the user is accessing the web (thus, being a noninvasive system). The proposed system models users in the wild and discovers navigation problems appearing in Discapnet and can also be used for problem detection when new users are navigating the site. The system was tested and its efficiency demonstrated in an experiment involving navigation under supervision, in which 82.6% of a set of disabled people were automatically labeled as having problems with the website.

Web personalization becomes essential in industries and specially for the case of users with special needs such as visually impaired people.Adaptation may very much speed up the navigation of visually impaired people and contribute to diminish the existing technological gap.This work is the first stage of a web mining process carried out in discapnet: a website created to promote the social and work integration of people with disabilities where slow navigation has been detected.Based on observation in-use where behaviours emerge applying a web mining process to server log data, we designed a system to generate user navigation profiles and adapt to the web site through link prediction.Two approaches for user profiling were implemented: a global system built based on the complete database and a modular approach carried out discovering the navigation profiles within different zones.Although both approaches are effective, the modular approach outperforms.When 25% of the navigation of the new user has happened the designed system is able to propose a set of links where nearly 60% of them (2 out of 3) is among the ones the new user will be using in the future.This will definitely make the navigation easier saving a lot of time.