a b s t r a c tEmotions-aware applications are getting a lot of attention as a way to improve the user experience, and also thanks to increasingly affordable Brain-Computer Interfaces (BCI). Thus, projects collecting emotionrelated data are proliferating, like social networks sentiment analysis or tracking students' engagement to reduce Massive Online Open Courses (MOOCs) drop out rates. All them require a common way to represent emotions so it can be more easily integrated, shared and reused by applications improving user experience. Due to the complexity of this data, our proposal is to use rich semantic models based on ontology. EmotionsOnto is a generic ontology for describing emotions and their detection and expression systems taking contextual and multimodal elements into account. The ontology has been applied in the context of EmoCS, a project that collaboratively collects emotion common sense and models it using the EmotionsOnto and other ontologies. Currently, emotion input is provided manually by users. However, experiments are being conduced to automatically measure users's emotional states using Brain-Computer Interfaces.

Technical advances, particularly the integration of wearable and embedded sensors, facilitate tracking of physiological responses in a less intrusive way. Currently, there are many devices that allow gathering biometric measurements from human beings, such as EEG Headsets or Health Bracelets. The massive data sets generated by tracking of EEG and physiology may be used, among other things, to infer knowledge about human moods and emotions. Apart from direct biometric signal measurement, eye tracking systems are nowadays capable of determining the point of gaze of the users when interacting in ICT environments, which provides an added value research on many different areas, such as psychology or marketing. We present a process in which devices for eye tracking, biometric, and EEG signal measurements are synchronously used for studying both basic and complex emotions. We selected the least intrusive devices for different signal data collection given the study requirements and cost constraints, so users would behave in the most natural way possible. On the one hand, we have been able to determine basic emotions participants were experiencing by means of valence and arousal. On the other hand, a complex emotion such as empathy has also been detected. To validate the usefulness of this approach, a study involving forty-four people has been carried out, where they were exposed to a series of affective stimuli while their EEG activity, biometric signals, and eye position were synchronously recorded to detect self-regulation. The hypothesis of the work was that people who self-regulated would show significantly different results when analyzing their EEG data. Participants were divided into two groups depending on whether Electro Dermal Activity (EDA) data indicated they self-regulated or not. The comparison of the results obtained using different machine learning algorithms for emotion recognition shows that using EEG activity alone as a predictor for self-regulation does not allow properly determining whether a person in self-regulation its emotions while watching affective stimuli. However, adequately combining different data sources in a synchronous way to detect emotions makes it possible to overcome the limitations of single detection methods.

MedISys is a media monitoring system initially intended for news items related to human health. The tool has how been extended by the Joint Research Centre, Universitat de Lleida and IRTA to also deal with plant health threats. This EFSA-funded project was based on a knowledge representation approach that generated an ontology, a formal representation of knowledge related to plant health threats. The ontology models plant pests and diseases, together with other concepts related with them: affected crops, hosts, vectors and symptoms. First of all, a collection of news sources related to plant health threats was collected to be monitored by MedISys. These sources included already known manually curated Web pages but also additional ones discovered by performing global Web searches using terms appearing in the ontology. Then, the news items coming from these sources were filtered using MedISys using a set of categories with keywords to identify those actually about plant health threats. Most of these categories focused on known threats and used terms associated with the 117 pests and diseases selected at the beginning of the project. Additionally, categories for unknown threats were also developed. In this case the categories included keywords that are usually used by experts to describe unknown threats and keywords related with symptoms expressions. All these MedISys categories combined provide mechanism to monitor plant health threats mentions in media, from newspapers to social media, ranging from those that explicitly mention a named threat (useful to monitor re-emerging threats or their spread) to those related to unknown ones (to monitor potential new threats). The project concluded with an evaluation of the e-mail alerts and reports generated by MedISys based on the previous categories. A survey and tests with real users were conducted and the results analysed to generate a set of recommendations and improvements to facilitate the use of MedISys as a plant health threats monitoring tool.

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Background The scientific evidence highlights the difficulties that healthcare professionals experience when managing patients with chronic pain. One of the causes of this difficulty could be related to the acquired training and the lack of knowledge about the neurophysiology of pain. In the present study, we assessed the effectiveness of a gamified web platform in acquiring knowledge about pain neurophysiology and determining the satisfaction and motivation of students of the Degree in Physiotherapy at the University of Lleida. Methods A quasi-experimental study was carried out with a sample of 60 students who had access to a gamified web platform that included notes, videos, and clinical cases prepared by the teaching staff and was based on a previous study that included patients and healthcare professionals. Results The results show that after the intervention, there was a statistically significant increase in knowledge about the neurophysiology of pain, and the effect size was in the desired area of ​​effect. Likewise, many students considered that their motivation had increased as a result of the methodology used in the present study. Conclusions The results support the use of this methodology to promote knowledge about the neurophysiology of pain while improving students’ motivation.

This study conducts research on deepfakes technology evolution and trends based on a bibliometric analysis of the articles published on this topic along with six research questions: What are the main research areas of the articles in deepfakes? What are the main current topics in deepfakes research and how are they related? Which are the trends in deepfakes research? How do topics in deepfakes research change over time? Who is researching deepfakes? Who is funding deepfakes research? We have found a total of 331 research articles about deepfakes in an analysis carried out on the Web of Science and Scopus databases. This data serves to provide a complete overview of deepfakes. Main insights include: different areas in which deepfakes research is being performed; which areas are the emerging ones, those that are considered basic, and those that currently have the most potential for development; most studied topics on deepfakes research, including the different artificial intelligence methods applied; emerging and niche topics; relationships among the most prominent researchers; the countries where deepfakes research is performed; main funding institutions. This paper identifies the current trends and opportunities in deepfakes research for practitioners and researchers who want to get into this topic.

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