Abstract
In this study we explore the impact of multisensory stimuli in virtual reality on users' emotional responses, addressing a knowledge gap in this rapidly evolving field. Utilizing a range of sensory inputs, including taste, haptics, and smell, in addition to audiovisual cues, this study aims to understand how different combinations of these stimuli affect the users' emotional experience. Two immersive virtual experiences have been developed for this purpose. One included a scenario to evoke positive emotions through selectively chosen pleasant multisensory stimuli, validated in a focus group. The other sought the contrary: to trigger negative emotions by integrating selected combinations of unpleasant multisensory stimuli, also validated in the same focus group. Through a comparative analysis, our findings revealed significant differences in emotional responses between the groups exposed to positive and negative stimuli combinations. Results indicated that combinations involving haptics and taste were particularly effective in eliciting intense emotions using positive stimuli, but their impact was less significant with negative stimuli. This investigation suggests that a fully multisensory virtual environment integrating positive stimuli might lead to cognitive overload, reducing overall emotional responses. In contrast, environments with negative stimuli could enhance emotional engagement and be more likely to avoid cognitive overload. These findings have important implications for designing emotionally resonant and compelling virtual reality experiences. This research enhances the understanding of sensory integration in virtual reality and its effects on emotional engagement, offering valuable insights for developing more impactful virtual experiences.

Abstract
This research investigates the use of artificial intelligence algorithms to identify behavioural patterns in computer use, with the aim of detecting trends that help to flag cases of depression by analysing the human-computer interaction records of these users, thereby increasing the quality of the data for early detection of these situations. Following design science methodology, a case study will be conducted using an existing mental health screening questionnaire, integrating an artificial intelligence layer to map mouse and keyboard interactions, followed by machine learning analysis of the records. The results of the machine learning assisted questionnaires will be compared with the results of the questionnaires without the mapping. If there is a significant difference, this model could be useful for making predictions about emotional states, contributing to the field of artificial intelligence and helping to prevent depression, which is the focus of the research, although the aim is to look at mental health in a global way. © 2025 Elsevier B.V., All rights reserved.

Abstract
Creating content for digital video game is an expensive segment of the development process, and many techniques have been explored to automate it. Much of the generated content is graphical, ranging from textures and sprites to typographical elements and user interfaces. Numerous techniques have been explored to automate the generation of these assets, with recent advancements incorporating artificial intelligence methodologies such as deep learning generative models. This study comprehensively surveys the literature from 2016 onwards, focusing on using machine learning to generate image-based assets for video game development, reviewing the deep learning approaches employed, and analyzing the specific challenges found. Specifically, the deep learning approaches employed, the problems addressed within the domain, and the metrics used for evaluating the results. The study demonstrates a knowledge gap in generative methods for some types of video game assets. Additionally, applicability and effectiveness of the most used evaluation metrics in the literature are studied. As future research prospects, with the increase in popularity of generative AI, the adoption of such techniques will be seen in automation processes. © 2018 IEEE.

Abstract
This work aims to identify teaching and learning practices in practical classes of Computer Network Technology courses, which promote the use of the Physical Laboratory (PL) as an epistemic tool to improve learning in epistemic terms. Content analysis of Multimodal Narrations (MN) of three classes by two teachers were used. An MN aggregates and organizes the data collected in the PL environment. Based on the results, we infer that the student and the teacher, under certain conditions, use the physical laboratory as an epistemic tool since the physical interactions prove its use and reuse. In addition, this study allows, in the context of work in the physical laboratory of networks, to identify that the orchestrations of mediation patterns adopted by the teacher influence the students' epistemic practices and the use of the laboratory as a tool to produce new knowledge. The following contributions are presented: (1) The quality of the students' epistemic practices is increased if, in the teacher's dynamics of mediation, the control of the students' action is reduced; (2) The orchestration of the teacher's mediation patterns is essential to achieve beneficial results in student learning with the use of artifacts from the physical laboratory of Computer Networks; (3) For the physical laboratory to become an epistemic tool, it is necessary that the mediation standards allow students to develop epistemic practices to a high or very high degree and there is a certain mediation orchestration.

Abstract
This study discusses the intricate relations between generative artificial intelligence (AI) and music composers. Based on a previous rapid review of recent literature, it reinforces a gap and suggests the need to develop human-centered generative AI design strategies prioritizing cultural artistic (and non-artistic) aspects. We posit that AI-based music generation solutions should resonate with the cultural diversity of stakeholders who are impacted by these systems in practice. The paper highlights the significance of metaphorical design as an effective method in human-AI music co-creation by leveraging familiar interfaces and features that are rooted in everyday objects and cognitive models derived from real-world settings. Our insights illustrate possible ways of (re)framing human-AI metaphorical design to shape perceptions and facilitate seamless interactions between humans and intelligent systems in music co-creativity, particularly at the compositional level. At the heart of this research is the alignment of AI-driven music creation systems with user needs, values, and expectations that vary from culture to culture and thus require a continuous and transparent adaptation of the technology in use to accommodate individual preferences and the socio-algorithmic specificities underlying musicians’ activities.

Abstract
Microtasks have become increasingly popular in the digital labor market since they provide easy access to a crowd of people with varying skills and aptitudes to perform remote work tasks that even the most capable algorithmic systems are unable to complete in a timely and efficient fashion. However, despite the latest advancements in crowd-powered and contiguous interfaces, many crowd workers still face some accessibility issues, which ultimately deteriorate the quality of the work produced. To mitigate this problem, we restrict attention to the development of two different web-based mini-games with a focus on cognitive personalization. We have conducted a pilot gamified experience, with six participants with autism, dyslexia, and attention deficit hyperactivity. The results suggest that a web-based mini-game can be incorporated in preliminary microtask-based crowdsourcing execution stages to achieve enhanced cognitive personalization in crowdsourcing settings.