Abstract
Social media platforms, increasingly used as news sources for varied data analytics, have transformed how information is generated and disseminated. However, the unverified nature of this content raises concerns about trustworthiness and accuracy, potentially negatively impacting readers’ critical judgment due to disinformation. This work aims to contribute to the automatic data quality validation field, addressing the rapid growth of online content on wiki pages. Our scalable solution includes stream-based data processing with feature engineering, feature analysis and selection, stream-based classification, and real-time explanation of prediction outcomes. The explainability dashboard is designed for the general public, who may need more specialized knowledge to interpret the model’s prediction. Experimental results on two datasets attain approximately 90% values across all evaluation metrics, demonstrating robust and competitive performance compared to works in the literature. In summary, the system assists editors by reducing their effort and time in detecting disinformation.

Abstract
The paper presents a new control concept based on the process moment instead of the process states or the process output signal. The control scheme is based on separate control of reference tracking and disturbance rejection. The tracking control is achieved by additionally feeding the input of the process model by the scaled output signal of the process model. The advantage of such feedback is that the final state of the process output can be analytically calculated and used for control instead of the actual process output value. The disturbance rejection, including model imperfections, is controlled by feeding back the filtered difference between the process output and the model output to the process input. The performance of tracking and disturbance rejection is simply controlled by two user-defined gains. Several examples have shown that the new control method provides very good and stable tracking and disturbance rejection performance.

Abstract

Abstract
Histology is a preclinical subject transversal in medical, dental, and veterinary curricula. Classical teaching approaches in histology are often undermined by lower motivation and engagement of students, which may be addressed by innovative learning environments. Herein, we developed a serious game approach and compared it with a classical teaching style. The students' feedback was evaluated by questionnaires, and their performance on quizzes and exam's scores were assessed. The serious game (Histopoly) consisted of a game-based web application for the teacher/game master, a digital gaming application used by the students as a controller, and a projected digital board game. The board featured rows for the four fundamental tissues (epithelial, connective, muscular, and nervous) paired with question tiles and additional tiles with more demanding activities (e.g., drawing, presenting slides, and making a syllabus). Participants included all veterinary students enrolled in the first year. Paired laboratory sessions were split with four sections (n = 94 students) playing Histopoly at the end of all sessions and two sections (n = 28 students) completing small evaluations every three weeks at the beginning of sessions. According to the questionnaires, students that played the serious game were more motivated, engaged, and more interconnected with classmates. The activity was considered fun, and students enjoyed the classes more. No differences in the final examination scores were found, but the percentage of correct answers provided throughout the serious game was significantly higher. Overall, these findings argue for the inclusion of serious games in modern histology teaching to promote student engagement in learning.

Abstract
Prognosis after intracranial hemorrhage (ICH) is influenced by a complex interplay between imaging and tabular data. Rapid and reliable prognosis are crucial for effective patient stratification and informed treatment decision-making. In this study, we aim to enhance image-based prognosis by learning a robust feature representation shared between prognosis and the clinical and demographic variables most highly correlated with it. Our approach mimics clinical decision-making by reinforcing the model to learn valuable prognostic data embedded in the image. We propose a 3D multi-task image model to predict prognosis, Glasgow Coma Scale and age, improving accuracy and interpretability. Our method outperforms current state-of-the-art baseline image models, and demonstrates superior performance in ICH prognosis compared to four board-certified neuroradiologists using only CT scans as input. We further validate our model with interpretability saliency maps. Code is available at https://github.com/MiriamCobo/MultitaskLearning_ICH_Prognosis.git.

Abstract
The potential of Augmented Reality (AR) has been harnessed to create immersive game settings, present layers of relevant information in museums, streamline procedures in healthcare and industry, and captivate consumers through innovative marketing strategies. Certain artifacts lend themselves well to representation in AR, especially those requiring a seamless fusion of the information layer with physical space. This integration underscores the suitability of information design artifacts for AR implementation. This study aims to delineate the distinctive attributes of AR in remediating information design, effectively catering to the user's informational needs. To this end, we analyzed the Google Translate app, examining it through the analytical lens of body schema and haptic engagement. The findings reveal that AR manifests as a performative, personalized, crafted image that fosters involvement through agency. The performative nature of the image directs attention, while individual images collectively form a collection. It is recommended that AR design be centered around achieving harmony among body, media, and space.