X3S: A multi-modal approach to monitor and assess stress through human-computer interaction

Stress evaluation is nowadays gaining an increasing importance in a time in which inter-individual competition continuously pushes us to be better. Indeed, in the workplace, in the academia or in many other contexts there is increasing pressure for better performance, which pushes us forward but also wears us out. This phenomenon has been studied from many different angles, including psychology, medicine or occupational dynamics. In a medical or biological context, stress is a physical, mental, or emotional factor that causes bodily or mental tension, which can cause or influence the course of many medical conditions including psychological conditions such as depression and anxiety. In these cases, individuals are under an increasing demand for performance, driving them to be under constant pressure, and consequently to present variations in their levels of stress. To mitigate this condition, this paper proposes to add a new dimension in human–computer interaction through the development of a distributed multi-modal framework approach entitled X3S, which aims to monitor and assess the psychological stress of computer users during high-end tasks, in a non-intrusive and non-invasive way, through the access of soft sensors activity (e.g. task performance and human behaviour). This approach presents as its main innovative key the capacity to validate each stress model trained for each individual through the analysis of cortisol and stress assessment survey data. Overall, this paper discusses how groups of medical students can be monitored through their interactions with the computer. Its main aim is to provide a stress marker that can be effectively used in large numbers of users and without inconvenienceThis work is part-funded by ERDF–European Regional Development Fund and by National Funds through the FCT–Portuguese Foundation for Science and Technology within project NORTE-01-0247-FEDER-017832. FCT grant with the reference ICVS-BI-2016-005info:eu-repo/semantics/publishedVersio

Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation

Due to the extensive social influence, public health emergency has attracted great attention in today’s society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event’s social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency

Inteligencia Artificial en Ambientes de Aprendizaje Ubicuo: Una revisión sistemática de literatura

El aprendizaje ubicuo (u-learning) se refiere a un aprendizaje en cualquier momento y en cualquier lugar. El u-learning se va difundiendo día a día, al punto tal que hay países donde se ha convertido en un enfoque convencional de enseñanza y aprendizaje, y muchas instituciones lo adoptan cuando los alumnos no pueden asistir a clases presenciales. Por su parte, las Ciencias de la Computación, y concretamente el campo de la Inteligencia Artificial (IA) presenta herramientas y técnicas para apoyar el crecimiento del u-learning y proporcionar recomendaciones, inferir el contexto y las situaciones de aprendizaje, generar perfiles de estudiante y adaptar el contenido, las actividades de aprendizaje, los caminos de aprendizaje, entre otras aplicaciones. El objetivo de este estudio fue realizar una revisión sistemática de trabajos de IA en entornos de aprendizaje ubicuos entre los años 2013 a 2023, con el objetivo de lograr una visión de la literatura relevante, identificar las brechas y proporcionar un alcance claro para esta área de investigación. Para ello, se aplicó un enfoque ampliamente aceptado y flexible que consta de los siguientes pasos: planificación, ejecución y resumen de resultados. Los artículos se obtuvieron de bases de datos ampliamente utilizadas, a saber, IEEExplore, ACM, Science Direct, Springer y Google Académico. Se revisaron finalmente un total de 28 publicaciones preseleccionadas para este estudio entre 993 artículos identificados a través de búsquedas en las bases de datos mencionadas. Para refinar la necesidad de la revisión se propuso un marco de análisis bidimensional, compuesto por dos vistas diferentes pero complementarias que captura un aspecto particular de los sistemas de u-learning en los que se aplica IA. A su vez cada vista se descompone en facetas que facilitan la comprensión de un aspecto particular. Considerando cada una de las facetas, los resultados obtenidos muestran que la IA se aplica principalmente para: recomendar contenido a los estudiantes en base a diferentes aspectos, detectar el entorno de aprendizaje ubicuo y reaccionar a los cambios de contextos, recomendar rutas de aprendizaje supervisadas, e inferir el nivel de conocimiento del alumno sobre un tema. Las principales técnicas de IA utilizadas resultaron ser: los agentes inteligentes, las Redes Bayesianas, las ontologías y las Reglas

Persuasion in Context: Understanding the Impact of Communication Modality, Gender, Ethnicity, Cognitive, and Linguistic Style Volume One Claire L.

Information is increasingly being exchanged on a global stage, and so audiences are becoming more diverse through communication in varying digital modalities. Understanding persuasion is increasingly important, particularly in response to technological change regarding the way in which we communicate and increased usage in our daily lives. However, persuasion has yet to be fully explored in relation to gender, ethnicity, cognitive and linguistic style and their influence on decision-making in interactive, synthetic modalities. This thesis sought to address this gap by employing an interpersonal modernised persuasion paradigm across three distinct contexts. Accordingly, three experimental studies are presented: Study 1 is conducted face-to-face (FtF), Study 2 utilises anonymous instant messaging software, and Study 3 introduces a novel, immersive, and collaborative virtual reality environment, which enables communication to occur in real-time via embodiment of avatars. The aims of the thesis were to a), investigate the effect of communication modalities on persuasion outcomes, b) to explore whether cognitive biases mediate persuasion outcomes, c) whether gender and ethnicity influence dyadic persuasive interactions, and d), to understand the impact, or otherwise, of linguistic style - comprising of quantitative analysis including linguistic synchronicity and epistemic modality, on persuasion outcomes. The combined results highlighted how the virtual environment was akin to the FtF modality, showing a propensity for successful persuasive outcomes and increased metacognitive confidence in attitude change. This has ramifications for real-world effects when researchers utilise virtual technology to observe, measure and train real-world performances. The anonymous instant messaging platform led to enhanced resistance across gender and ethnic groups, with males being significantly more likely to oppose the persuasive arguments as a result. Overall however, ethnicity and gender did not influence persuasion outcomes, nor did cognitive style mediate or predict an individual’s disposition to persuasion. Finally, linguistic style highlighted differences across participants, with persuaded individuals using more cognitive processing and informal language during exchanges. Expanding our understanding of how judgements are formed, influenced and modified can serve to widen discussion, and support applied understandings regarding the management of conversations both on- and offline. All findings are presented and discussed in relation to the relevant theoretical literature throughout this body of work

CyberPsychological Computation on Social Community of Ubiquitous Learning

Under the modern network environment, ubiquitous learning has been a popular way for people to study knowledge, exchange ideas, and share skills in the cyberspace. Existing research findings indicate that the learners’ initiative and community cohesion play vital roles in the social communities of ubiquitous learning, and therefore how to stimulate the learners’ interest and participation willingness so as to improve their enjoyable experiences in the learning process should be the primary consideration on this issue. This paper aims to explore an effective method to monitor the learners’ psychological reactions based on their behavioral features in cyberspace and therefore provide useful references for adjusting the strategies in the learning process. In doing so, this paper firstly analyzes the psychological assessment of the learners’ situations as well as their typical behavioral patterns and then discusses the relationship between the learners’ psychological reactions and their observable features in cyberspace. Finally, this paper puts forward a CyberPsychological computation method to estimate the learners’ psychological states online. Considering the diversity of learners’ habitual behaviors in the reactions to their psychological changes, a BP-GA neural network is proposed for the computation based on their personalized behavioral patterns