The emotional response to a stimulus is typically measured in three variables called valence, arousal and dominance. Based on such dimensions, Bradley and Lang ( 1999 ) published the Affective Norms for English Words (ANEW), a corpus of affective ratings for 1,034 non-contextualized words. Expanded and adapted to many languages, ANEW provides a corpus to evaluate and to predict human responses to different stimuli, and it has been used in a number of studies involving analysis of emotions. However, ANEW seems not to appropriately predict affective responses to concepts when these are contextualized in certain situational backgrounds, in which words can have different connotations from those in non-contextualized scenarios. These contextualized affective norms have not been sufficiently contrasted yet because the literature does not provide a corpus of the ANEW list in specific contexts. On this basis, this paper reports on the creation of a new corpus of affective norms for the original 1,034 ANEW words in a particular context (a fictional scene of suspense). An extensive quantitative data analysis comparing both corpora was carried out, confirming that the affective ratings are highly influenced by the context. The corpus can be downloaded as Supplementary Material .

The recognition of activities of daily living is an important research area of interest in recent years. The process of activity recognition aims to recognize the actions of one or more people in a smart environment, in which a set of sensors has been deployed. Usually, all the events produced during each activity are taken into account to develop the classification models. However, the instant in which an activity started is unknown in a real environment. Therefore, only the most recent events are usually used. In this paper, we use statistics to determine the most appropriate length of that interval for each type of activity. In addition, we use ontologies to automatically generate features that serve as the input for the supervised learning algorithms that produce the classification model. The features are formed by combining the entities in the ontology, such as concepts and properties. The results obtained show a significant increase in the accuracy of the classification models generated with respect to the classical approach, in which only the state of the sensors is taken into account. Moreover, the results obtained in a simulation of a real environment under an event-based segmentation also show an improvement in most activities.

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Suspense is a key narrative issue in terms of emotional gratifications. Reactions in response to this type of entertainment are positively related to enjoyment, having a significant impact on the audience's immersion and suspension of disbelief. Related to computational modeling of this feature, some automatic storytelling systems include limited implementations of suspense management system in their core. In this way, the interest of this subject in the area of creativity has resorted to different definitions from fields as narratology and the film industry, as much as several proposals of its constituent features. Among their characteristics, uncertainty is one of the most discussed in terms of impact and need: while many authors affirm that uncertainty is essential to evoke suspense, others limit or reject its influence. Furthermore, the paradox of suspense reflects the problem of including uncertainty as a component required in suspense creation systems. Due to this need to contrast the effects of the uncertainty in order to compute a general model for automatic storytelling systems, we conducted an experiment measuring suspense experienced by a group of subjects that read a story. While a group of them were told the ending of the story in advance, the members of the other group experimented the same story in chronological order. Both the subjects' reported suspense and their physiological responses are gathered and analyzed. Results provide evidence to conclude that uncertainty affects the emotional response of readers, but independently and in a different form than suspense does. It will help to propose a model in which uncertainty is processed separately as management of the amount of knowledge about the outcome available to the spectator, which acts as a control signal to modulate the input features, but not directly in suspense computing.

ResumenEn este artículo se presenta un sistema multiusuario específicamente diseñado para facilitar el aprendizaje colaborativo de idiomas a través de dispositivos móviles. El sistema facilita una versión móvil que implementa una tarea de aprendizaje, llamada Terminkalender, que fue diseñada inicialmente para ser realizada en soporte papel y que se usó con éxito durante varios años con estudiantes del nivel A1 de alemán (MCERL). Dicha tarea requiere que los estudiantes intercambien mensajes escritos a fin de planificar y anotar en un calendario personal una serie de citas para actividades. Si bien la versión en papel ya tenía gran potencial para motivar a los estudiantes a interactuar entre ellos y usar la lengua meta, la app tiene un valor añadido, ya que no solo facilita el proceso de interacción entre los propios estudiantes sino que, además, permite a los docentes revisar y analizar las interacciones producidas a partir de los registros almacenados. Para ello se ha implementado un conjunto de elementos software que incluye: un portal web, un chat para la comunicación textual, un servicio de retroalimentación en tiempo real y una herramienta para registrar las interacciones entre los usuarios. La experiencia presentada permite estimar el potencial que tiene el sistema para analizar el comportamiento de los usuarios y sus patrones de interacción, así como para evaluar diferentes indicadores de rendimiento relacionados con el uso y las competencias en lengua meta.Palabras clave: software educativo; enseñanza de lenguas; aprendizaje en grupo; telecomunicación; sistema multimedia. AbstractThis paper presents a multi-user mobile learning system-specifically designed to enhance collaborative language learning through mobile devices. The system delivers an app version of a paper-based learning task, called Terminkalender, which has successfully been used for several years with students from an A1-level German language course (CEFR). The