EmotionsOnto: an Ontology for Developing Affective Applications

Abstract: EmotionsOnto is a generic ontology for describing emotions and their detection and expression systems taking contextual and multimodal elements into account. The ontology is proposed as a way to develop an easily computerizable and flexible formal model. Moreover, it is based on the Web Ontology Language (OWL) standard, which also makes ontologies easily shareable and extensible. Once formalized as an ontology, the knowledge about emotions can be used in order to make computers more personalised and adapted to users' needs. The ontology has been validated and evaluated by means of an applications based on a emotionsaware Tangible User Interface (TUI). The TUI is guided by emotion knowledge previously gathered using the same TUI and modelled using EmotionsOnto

Servicios de confianza semántica para entornos ambientales seguros

Este proyecto se centra en la seguridad y la confianza en sistemas y tecnologías de la información y la comunicación (TIC). Es parte de un trabajo original de investigación llevado a cabo en la empresa Safelayer Secure Communications, en el que se combinan conceptos y tecnologías innovadoras para mejorar la seguridad y la confianza en las TIC de los nuevos escenarios que se presentan en la Internet del Futuro y la ya imparable Sociedad de la Información

Enhancing electronic intelligent tutoring systems by responding to affective states

The overall aim of this research is the exploration mechanisms which allow an understanding of the emotional state of students and the selection of an appropriate cognitive and affective feedback for students on the basis of students' emotional state and cognitive state in an affective learning environment. The learning environment in which this research is based is one in which students learn by watching an instructional video. The main contributions in the thesis include: - A video study was carried out to gather data in order to construct the emotional models in this research. This video study adopted a methodology in qualitative research called “Quick and Dirty Ethnography”(Hughes et al., 1995). In the video study, the emotional states, including boredom, frustration, confusion, flow, happiness, interest, were identified as being the most important to a learner in learning. The results of the video study indicates that blink frequencies can reflect the learner's emotional states and it is necessary to intervene when students are in self-learning through watching an instructional video in order to ensure that attention levels do not decrease. - A novel emotional analysis model for modeling student’s cognitive and emotional state in an affective learning system was constructed. It is an appraisal model which is on the basis of an instructional theory called Gagne’s theory (Gagne, 1965). - A novel emotion feedback model for producing appropriate feedback tactics in affective learning system was developed by Ontology and Influence Diagram ii approach. On the basis of the tutor-remediation hypothesis and the self-remediation hypothesis (Hausmann et al., 2013), two feedback tactic selection algorithms were designed and implemented. The evaluation results show: the emotion analysis model can be used to classify negative emotion and hence deduce the learner’s cognitive state; the degree of satisfaction with the feedback based on the tutor-remediation hypothesis is higher than the feedback based on self-remediation hypothesis; the results indicated a higher degree of satisfaction with the combined cognitive and emotional feedback than cognitive feedback on its own

An ontology for description of emotional cues

There is a great variety of theoretical models of emotions and implementation technologies which can be used in the design of affective computers. Consequently, designers and researchers usually made practical choices of models and develop ad-hoc solutions that sometimes lack flexibility. In this paper we introduce a generic approach to modeling emotional cues. The main component of our approach is the ontology of emotional cues. The concepts in the ontology are grouped into three global modules representing three layers of emotions’ detection or production: the emotion module, the emotional cue module, and the media module. The emotion module defines emotions as represented with emotional cues. The emotional cue module describes external emotional representations in terms of media properties. The media module describes basic media properties important for emotional cues. Proposed ontology enables flexible description of emotional cues at different levels of abstraction. This approach could serve as a guide for the flexible design of affective devices independently of the starting model and the final way of implementation

An ontology for description of emotional cues

There is a great variety of theoretical models of emotions and implementation technologies which can be used in the design of affective computers. Consequently, designers and researchers usually made practical choices of models and develop ad-hoc solutions that sometimes lack flexibility. In this paper we introduce a generic approach to modeling emotional cues. The main component of our approach is the ontology of emotional cues. The concepts in the ontology are grouped into three global modules representing three layers of emotions’ detection or production: the emotion module, the emotional cue module, and the media module. The emotion module defines emotions as represented with emotional cues. The emotional cue module describes external emotional representations in terms of media properties. The media module describes basic media properties important for emotional cues. Proposed ontology enables flexible description of emotional cues at different levels of abstraction. This approach could serve as a guide for the flexible design of affective devices independently of the starting model and the final way of implementation