Abstract
We present CIDER, a novel framework for the collaborative editing of 3D augmented scenes. The framework allows multiple users to manipulate the virtual elements added to the real environment independently and without unexpected changes, comparing the different editing proposals and finalising a collaborative result. CIDER leverages the use of 'layers' encapsulating the state of the environment. Private layers can be edited independently by the different subjects, and a global one can be collaboratively updated with 'commit' operations. In this paper, we describe in detail the system architecture and the implementation as a prototype for the HoloLens 2 headsets, as well as the motivations behind the interaction design. The system has been validated with a user study on a realistic interior design task. The study not only evaluated the general usability but also compared two different approaches for the management of the atomic commit: forced (single-phase) and voting (requiring consensus), analyzing the effects of this choice on collaborative behaviour. According to the users' comments, we performed improvements to the interface and further tested their effectiveness.

Abstract
The European Health Data Space (EHDS) sets out regulations for the management of electronic health data, distinguishing between its primary use in direct healthcare and patient data portability, and its secondary use for research, innovation, and policy-making. We aim to empower individuals to take control of their health by enhancing personal health data management. In this work, we discuss the challenges in electronic personal health data access and propose developing a system that leverages personal health knowledge graphs and retrieval-augmented generation to ensure FAIR personal electronic health data representation and personalized health information delivery.

Abstract

Abstract
This study proposes a virtual reality (VR) experience that aims at raising awareness toward climate change through the simulation of a wildfire, a natural disaster that is becoming increasingly frequent in Portugal, and promote the adoption of sustainable behaviours and mitigation strategies. Here, the feasibility of such an approach is tested implementing both subjective, with the Igroup Presence Questionnaire (IPQ) to evaluate the senses of presence, involvement and realism, and objective measures, i.e., a set of features extracted from the in-VR movement trajectories. The mean scores of IPQ items demonstrated that such devastating event is particularly effective in enhancing participants' involvement and sense of presence within the virtual environment, reinforcing the potential of VR to foster pro-environmental attitudes. Results also highlighted that these feelings were not altered by VR familiarity, whereas presence ratings were higher for participants who visited the actual location that the virtual environment replicated. Correlation analysis also discovered significant relationships between subjective and objective parameters. © 2025 IEEE.

Abstract
The growing interest in learning more about consumer behaviors through analytical techniques requires the integration of innovative approaches that relate their needs to strategic marketing procedures. Multimodality and Affective Computing combined a series of robust optimizations for this challenge, implying the complexity of each application. However, the entanglement of different modalities demands new and tailored refinements to enhance adaptability and accuracy in the field. This paper outlines the implementation of a Multimodal Artificial Intelligence methodology with Affective Computing to enhance consumer insights and marketing strategies. The application combines different data modalities, such as textual, visual, and audio inputs, to tackle complex issues in dealing with consumer sentiment. The proposed approach uses advanced preprocessing techniques, including word embeddings, neural networks, and recurrent models, to extract information from diverse modalities. Fusion strategies, such as attention-based and late fusion procedures, are utilized to combine knowledge, facilitating robust sentiment detection. The implementation includes the analysis of real-time customer feedback on social media and product assessments, demonstrating improvements in predicting engagement and shaping consumer behavior. The results underscore the practical viability of the suggested method, promoting progress in multimodal sentiment analysis to extract actionable consumer insights in marketing.

Abstract
With the growing interest in underwater exploration and monitoring, autonomous underwater vehicles have become essential. The recent interest in onboard deep learning (DL) has advanced real-time environmental interaction capabilities relying on efficient and accurate vision-based DL models. However, the predominant use of sonar in underwater environments, characterized by limited training data and inherent noise, poses challenges to model robustness. This autonomy improvement raises safety concerns for deploying such models during underwater operations, potentially leading to hazardous situations. This article aims to provide the first comprehensive overview of sonar-based DL under the scope of robustness. It studies sonar-based DL perception task models, such as classification, object detection, segmentation, and simultaneous localization and mapping. Furthermore, this article systematizes sonar-based state-of-the-art data sets, simulators, and robustness methods, such as neural network verification, out-of-distribution, and adversarial attacks. This article highlights the lack of robustness in sonar-based DL research and suggests future research pathways, notably establishing a baseline sonar-based data set and bridging the simulation-to-reality gap.