Facial expressions are essential for non-verbal human communication as they convey behavioral intentions and emotional states. While facial action units (AUs) can occur bilaterally or unilaterally, the existing research in affective computing predominantly concentrates on bilateral expressions, largely due to the lack of datasets with unilateral AU labels. In this study, we present a method for generating unilateral AU labels and assess its efficacy against expert-labeled facial images. Furthermore, we introduce a dedicated model trained on the generated data and evaluate its performance across multiple datasets. Our findings offer insights into feature extraction for unilateral facial expression recognition. This research contributes to advancing the understanding and recognition of nuanced facial expressions, with potential applications in various domains such as healthcare and human-computer interaction.

Online learning’s popularity has surged. However, teachers face the challenge of the lack of non-verbal communication with students, making it difficult to perceive their learning-centered affective states (LCAS), leading to missed intervention opportunities. Addressing this challenge requires a system that detects students’ LCAS from their non-verbal cues and informs teachers in an actionable way. To design such a system, it is essential to explore field experts’ needs and requirements. Therefore, we conducted design-based research focus groups with teachers to determine which LCAS they find important to know during online lectures and their preferred communication methods. The results indicated that confusion, engagement, boredom, frustration, and curiosity are the most important LCAS and that the proposed system should take into account teachers’ cognitive load and give them autonomy in the choice of content and frequency of the information. Considering the obtained feedback, a prototype of two versions was developed. The prototype was evaluated by teachers utilizing the System Usability Scale (SUS). Results indicated an average SUS score of 80.5 and 74.5 for each version, suggesting acceptable usability. These findings can guide the design and development of a system that can help teachers recognize students’ LCAS, thus improving synchronous online learning.

Training facial emotion recognition models requires large sets of data and costly annotation processes. To alleviate this problem, we developed a gamified method of acquiring annotated facial emotion data without an explicit labeling effort by humans. The game, which we named Facegame, challenges the players to imitate a displayed image of a face that portrays a particular basic emotion. Every round played by the player creates new data that consists of a set of facial features and landmarks, already annotated with the emotion label of the target facial expression. Such an approach effectively creates a robust, sustainable, and continuous machine learning training process. We evaluated Facegame with an experiment that revealed several contributions to the field of affective computing. First, the gamified data collection approach allowed us to access a rich variation of facial expressions of each basic emotion due to the natural variations in the players' facial expressions and their expressive abilities. We report improved accuracy when the collected data were used to enrich well-known in-the-wild facial emotion datasets and consecutively used for training facial emotion recognition models. Second, the natural language prescription method used by the Facegame constitutes a novel approach for interpretable explainability that can be applied to any facial emotion recog-nition model. Finally, we observed significant improvements in the facial emotion perception and expression skills of the players through repeated game play.

Contemporary organizations need to adapt their business processes swiftly to cope with ever-changing requirements. Requirement changes originate from a wide variety of sources. Business analysts gather these requirements, resolve conflicts, analyze impacts, and prepare actionable improvement plans. These tasks require a comprehensive knowledge of business processes and other entities within the organization. Business process model repositories, which may contain hundreds of models, are important sources of such cross-functional information. In this study, we introduce an approach which facilitates business process improvement by utilizing the comprehensive information covered by process models. Specifically, we associate requirements with other organizational entities based on their transitive relations with process models. To infer these associations, our approach makes use of natural language processing techniques and enterprise semantics. A quantitative evaluation of our approach, which took place within a major telecommunication company, displayed that it accurately detects associations between requirements and process models. Furthermore, semi-structured interviews with business analysts revealed that their expectations are high on efficiency increases due to the usage of this approach.