Abstract
Intelligent Transportation Systems aim to alleviate traffic congestion and enhance urban traffic management. Transformer-based methods have shown promise in traffic prediction due to their capability to handle long-range dependencies. However, they disregard local context during parallel processing and can be computationally expensive for large traffic networks. On the other hand, they miss the hierarchical information hidden in regions of large traffic networks. To address these issues, we introduce CSCN, a novel framework that clusters traffic sensors based on data similarity, employs clustered multi-head self-attention for efficient hierarchical pattern learning, and utilizes causal convolutional attention for capturing local temporal trends. In addition to these advancements, we integrate snapshot ensemble learning into CSCN, allowing for the exploitation of diverse snapshots obtained during training to enrich predictive performance. Evaluations of real-world data highlight CSCN's superiority in traffic flow prediction, showcasing its potential for enhancing transportation systems with improved accuracy and efficiency.

Abstract
Industry 4.0 is transforming manufacturing environments, with robotics being a key technology that enhances various capabilities. The flexibility of Autonomous Mobile Robots has led to the rise of multi-robot systems in industrial settings. Considering the high cost of these robots, it is essential to determine the best fit of number and type before making any major investments. Simulation and modeling are valuable decision-support tools, allowing the simulation of different setups to address robot fleet sizing issues. This paper introduces a decision-support framework that combines a fleet manager software stack with the FlexSim simulator, helping decision-makers determine the most suitable mobile robots fleet size tailored to their needs. Unlike previous approaches, the developed solution integrates the same real robot coordination software in both simulation and actual deployment, ensuring that tested scenarios accurately reflect real-world conditions. A case study was conducted to evaluate the framework, involving multiple tasks of loading and unloading materials within a warehouse. Five different scenarios with varying fleet sizes were simulated, and their performances assessed. The analysis concluded that, for the case study under consideration, a fleet of three robots was the most suitable, considering relevant key performance indicators. The results confirmed that the developed solution is an effective alternative for addressing the problem and represents a novel technology with no prior state-of-the-art equivalents.

Abstract
Dense retrieval models are generally trained using supervised learning approaches for representation learning, which require a labeled dataset (i.e., query-document pairs). However, training such models from scratch is not feasible for most languages, particularly under-resourced ones, due to data scarcity and computational constraints. As an alternative, pretrained dense retrieval models can be fine-tuned for specific downstream tasks or applied directly in zero-shot settings. Given the lack of labeled data for Tetun and the fact that existing dense retrieval models do not currently support the language, this study investigates their application in zero-shot, out-of-distribution scenarios. We adapted these models to Tetun documents, producing zero-shot embeddings, to evaluate their performance across various document representations and retrieval strategies for the ad-hoc text retrieval task. The results show that most pretrained monolingual dense retrieval models outperformed their multilingual counterparts in various configurations. Given the lack of dense retrieval models specialized for Tetun, we combine Hiemstra LM with ColBERTv2 in a hybrid strategy, achieving a relative improvement of +2.01% in P@10, +4.24% in MAP@10, and +2.45% in NDCG@10 over the baseline, based on evaluations using 59 queries and up to 2,000 retrieved documents per query. We propose dual tuning parameters for the score fusion approach commonly used in hybrid retrieval and demonstrate that enriching document titles with summaries generated by a large language model (LLM) from the documents' content significantly enhances the performance of hybrid retrieval strategies in Tetun. To support reproducibility, we publicly release the LLM-generated document summaries and run files.

Abstract

Abstract
Sound design plays an important role in serious games, influencing user experience and learning outcomes. However, deriving general principles and best practices remains challenging, as most literature relies on case-based studies in different application domains. Through a systematic review of the literature, 21 studies were analyzed to address two key questions: 1) what types of serious games and application domains incorporate sound design? and 2) what sound design strategies are implemented to enhance user experience and learning outcomes? The findings show that serious games have mainly focused on education, healthcare, and training, using sound to enhance motivation (50%), cognition (32%), and knowledge acquisition (18%). Furthermore, sound design strategies fulfill distinct roles: sound effects enhance feedback and engagement, background music influences motivation and cognitive processing, ambient sounds support navigation and emotional regulation, and dialogue facilitates knowledge acquisition. The findings highlight the need for further research to establish standardized sound design principles to optimize user experience and learning outcomes in serious games. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

Abstract
There are now multiple proposals for Conflict-free Replicated Data Types (CRDTs) in SQL databases aimed at distributed systems. Some, such as ElectricSQL, provide only relational tables as convergent replicated maps, but this omits semantics that would be useful for merging updates. Others, such as Pg\_crdt, provide access to a rich library of encapsulated column types. However, this puts merge and query processing outside the scope of the query optimizer and restricts the ability of an administrator to influence access paths with materialization and indexes. Our proposal, CRDV, overcomes this challenge by using two layers implemented as SQL views: The first provides a replicated relational table from an update history, while the second implements varied and rich types on top of the replicated table. This allows the definition of merge semantics, or even entire new data types, in SQL itself, and enables global optimization of user queries together with merge operations. Therefore, it naturally extends the scope of query optimization and local transactions to operations on replicated data, can be used to reproduce the functionality of common CRDTs with simple SQL idioms, and results in better performance than alternatives.