Abstract
Software businesses are continuously increasing their presence in the cloud. While cloud computing is not a new research topic, designing software for the cloud is still challenging, requiring engineers to invest in research to become proficient at working with it. Design patterns can be used to facilitate cloud adoption, as they provide valuable design knowledge and implementation guidelines for recurrent engineering problems. This work introduces a pattern language for designing software for the cloud. We believe developers can significantly reduce their R&D time by adopting these patterns to bootstrap their cloud architecture. The language comprises 10 patterns, organized into four categories: Automated Infrastructure Management, Orchestration and Supervision, Monitoring, and Discovery and Communication.

Abstract
General-purpose AI-assisted tools, such as ChatGPT, have recently gained much attention from the media and the general public. That raised questions about in which tasks we can apply such a tool. A good code design is essential for agile software development to keep it ready for change. In this context, identifying which design pattern can be appropriate for a given scenario can be considered an advanced skill that requires a high degree of abstraction and a good knowledge of object orientation. This paper aims to perform an exploratory study investigating the effectiveness of an AI-assisted tool in assisting developers in choosing a design pattern to solve design scenarios. To reach this goal, we gathered 56 existing questions used by teachers and public tenders that provide a concrete context and ask which design pattern would be suitable. We submitted these questions to ChatGPT and analyzed the answers. We found that 93% of the questions were answered correctly with a good level of detail, demonstrating the potential of such a tool as a valuable resource to help developers to apply design patterns and make design decisions.

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

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.