Abstract
Data intensive applications increasingly make use of multiple data stores in the cloud, providing a diversity of data and query models, as well as durability and scale trade-offs. However, this has a severe impact on reliability, as the key fault-tolerance mechanism for database systems, i.e. ACID transactions, is no longer available. Although it is possible to implement transactions without changes to the database servers, this either requires a proxy server, which compromises scale and availability, or a client-side layer that changes the data schema, excludes legacy applications, and adds significant overhead. We address this challenge with a proposal to delegate functionality from a client-side transactional layer to a server-side query engine such that compatibility with legacy applications is restored. We implemented a proof-of-concept and show that it significantly improves performance for analytical applications.

Abstract
Artificial Intelligence (AI), in particular Generative Artificial Intelligence (GenAI), is a quickly developing field capable of revolutionizing educational digital escape rooms. Traditionally reliant on static content, these immersive environments have faced limitations in adaptability, replayability, and personalization. However, recent advancements in AI and GenAI enable dynamic puzzle generation, adaptive storytelling, and AI-driven non-player characters (NPCs) with agentic AI, allowing for highly responsive and personalized experiences. This paper reviews the state-of-the-art in integrating AI (with the focus on GenAI) into educational digital escape rooms, integrating interdisciplinary insights from cognitive science, game design, and machine learning, and showing how AI can improve engagement, scalability, and content diversity, but also indicates challenges related to ethical AI use, bias in algorithmic decision-making, and the need for robust evaluation frameworks to assess player satisfaction and learning outcomes.

Abstract
The effectiveness of digital marketing relies on the seamless integration of intelligent technology, enabling encounters that closely resemble those experienced with physical vendors in the real world. Thus, the importance of scalable artificial intelligence (AI) systems guided by a multimodal approach cannot be overstated, as they can be used to gain a deeper understanding of user preferences and engagement behaviors. The investigation conducted concerning multimodal learning in this review uncovers a variety of benefits and limitations on the available data, presenting consistency in finding the relationship between modalities. The results suggest multimodality as a topic with a noticeable dearth of research, yet a promising path to reduce uncertainty and develop innovative perspectives on decision-making for Digital Marketing improvement tasks. The complexity inherent in data processes like analysis, processing, and granular modulation requires a lot of effort for researchers to build accurate multimodal representations while trying to suppress imprecision in these new elements. Therefore, our approach aims to explore how theoretical foundations are successfully applied to learning operational procedures, considering real-life case comprehension, the technical challenges of the learning process, and the importance given to each feature. Even so, comparing the restrictions found in the state-of-the-art made possible the reformulation of limitations to this particular type of technology and encouraged the search for more guidelines on the entire process.

Abstract
The Industry 4.0 paradigm (I4.0) supports the improvement of industrial processes through Information and Communication Technologies (ICT), with information systems providing real-time information to humans and machines, in order to make the production process more flexible and efficient. In this context, Virtual Assistants (VA) collect and process production data and provide contextualized and real-time information to the workers in the production environment. This paper presents a prototype of a VA developed to collect production data from heterogeneous sources in the factory, process them based on contextual information, and provide workers with useful information to assist them in taking informed decisions. In that context, VA can represent a valuable aid to improve overall productivity and efficiency in the I4.0 factories.

Abstract
Human-Computer Interaction (HCI) research has illuminated how technology can influence users’ awareness of their environmental impact and the potential for mitigating these impacts. From hot water saving to food waste reduction, researchers have systematically and widely tried to find pathways to speed up achieving sustainable development goals through persuasive technology interventions. However, motivating users to adopt sustainable behaviors through interactive technologies presents significant psychological, cultural, and technical challenges in creating engaging and long-lasting experiences. Aligned with this perspective, there is a dearth of research and design solutions addressing the use of persuasive technology to promote sustainable recycling behavior. Guided by a participatory design approach, this investigation focuses on the design opportunities for leveraging persuasive and human-centered Internet of Things (IoT) applications to enhance user engagement in recycling activities. The assumption is that one pathway to achieve this goal is to adopt persuasive strategies that may be incorporated into the design of sustainable applications. The insights gained from this process can then be applied to various sustainable HCI scenarios and therefore contribute to HCI’s limited understanding in this area by providing a series of design-oriented research recommendations for informing the development of persuasive and socially engaged recycling platforms. In particular, we advocate for the inclusion of educational content, real-time interactive feedback, and intuitive interfaces to actively engage users in recycling activities. Moreover, recognizing the cultural context in which the technology is socially situated becomes imperative for the effective implementation of smart devices to foster sustainable recycling practices. To this end, we present a case study that seeks to involve children and adolescents in pro-recycling activities within the school environment.

Abstract
Imbalanced datasets present a challenge in machine learning, especially in binary classification scenarios where one class significantly outweighs the other. This imbalance often leads to models favoring the majority class, resulting in inadequate predictions for the minority class, specifically in false negatives. In response to this issue, this work introduces the MinFNR ensemble algorithm, designed to minimize False Negative Rates (FNR) in imbalanced datasets. The new approach strategically combines data-level, algorithmic-level, and hybrid-level approaches to enhance overall predictive capabilities while minimizing computational resources using the Set Covering Problem (SCP) formulation. Through a comprehensive evaluation of diverse datasets, MinFNR consistently outperforms individual algorithms, showing its potential for applications where the cost of false negatives is substantial, such as fraud detection and medical diagnosis. This work also contributes to ongoing efforts to improve the reliability and effectiveness of machine learning algorithms in real imbalanced scenarios.