Abstract
Multivariate time series analysis is a vital but challenging task, with multidisciplinary applicability, tackling the characterization of multiple interconnected variables over time and their dependencies. Traditional methodologies often adapt univariate approaches or rely on assumptions specific to certain domains or problems, presenting limitations. A recent promising alternative is to map multivariate time series into high-level network structures such as multiplex networks, with past work relying on connecting successive time series components with interconnections between contemporary timestamps. In this work, we first define a novel cross-horizontal visibility mapping between lagged timestamps of different time series and then introduce the concept of multilayer horizontal visibility graphs. This allows describing cross-dimension dependencies via inter-layer edges, leveraging the entire structure of multilayer networks. To this end, a novel parameter-free topological measure is proposed and common measures are extended for the multilayer setting. Our approach is general and applicable to any kind of multivariate time series data. We provide an extensive experimental evaluation with both synthetic and real-world datasets. We first explore the proposed methodology and the data properties highlighted by each measure, showing that inter-layer edges based on cross-horizontal visibility preserve more information than previous mappings, while also complementing the information captured by commonly used intra-layer edges. We then illustrate the applicability and validity of our approach in multivariate time series mining tasks, showcasing its potential for enhanced data analysis and insights.

Abstract
While server-side assessment of programming exercises, with its ease of installing diverse compilers and execution environments, is common, it presents three key limitations: the necessity of a constant Internet connection, increased bandwidth consumption, and centralized execution load. The alternative is to rely on JavaScript, the single programming language supported by all standard web browsers. This paper introduces Osiris, a pure JavaScript multi-language transpiler designed to enable the execution of diverse programming languages within web browsers. Targeted primarily at Virtual Learning Environments (VLE) for language programming education, Osiris employs a parser generator to translate small student programs into JavaScript based on language-specific grammars with semantic rules. It also includes a comprehensive, though not exhaustive, JavaScript library that emulates the standard libraries of its supported languages. Validation of Osiris indicates the pedagogical effectiveness of browser-based transpilation for introductory programming education.

Abstract
The combination of storytelling and gamification in educational settings has emerged as a method to enhance student engagement and learning outcomes. Through an overarching narrative, course content can be connected while providing context for gamified exercises, creating a motivating and competitive learning experience. However, a narrative that resonates with one student may not interest others. The presented solution to this problem is to offer multiple narratives for students to choose from. This enables the students to engage with the material in ways that align with their interests and motivations. Yet, managing multiple narratives presents several challenges. Each narrative must cover all syllabus topics equally, and every exercise must be available across all narratives while maintaining consistent difficulty levels and learning objectives. This paper presents a systematic approach for creating gamified courses with multiple narratives. The methodology includes the development of a base course template and its narrative variations, along with transformation processes to generate exercises in the FGPE Ecosystem, namely AuthorKit and FGPE PLE. The final output is a single Moodle MBZ file that can be imported into Moodle, a widely adopted learning management system.

Abstract
The PAP900 dataset centers on the semantic relationship between affective words in Portuguese. It contains 900 word pairs, each annotated by at least 30 human raters for both semantic similarity and semantic relatedness. In addition to the semantic ratings, the dataset includes the word categorization used to build the word pairs and detailed sociodemographic information about annotators, enabling the analysis of the influence of personal factors on the perception of semantic relationships. Furthermore, this article describes in detail the dataset construction process, from word selection to agreement metrics. Data was collected from Portuguese university psychology students, who completed two rounds of questionnaires. In the first round annotators were asked to rate word pairs on either semantic similarity or relatedness. The second round switched the relation type for most annotators, with a small percentage being asked to repeat the same relation. The instructions given emphasized the differences between semantic relatedness and semantic similarity, and provided examples of expected ratings of both. There are few semantic relations datasets in Portuguese, and none focusing on affective words. PAP900 is distributed in distinct formats to be easy to use for both researchers just looking for the final averaged values and for researchers looking to take advantage of the individual ratings, the word categorization and the annotator data. This dataset is a valuable resource for researchers in computational linguistics, natural language processing, psychology, and cognitive science. (c) 2025TheAuthors.

Abstract
Indoor Positioning Systems (IPSs) are essential for applications requiring accurate location awareness in indoor environments. However, achieving high precision remains challenging due to signal interference and environmental variability. This study proposes a multimodal IPS that integrates Bluetooth Received Signal Strength Indicator (RSSI) measurements and video imagery using machine learning (ML) and ensemble learning techniques. The system was implemented and deployed in the Hall of Biodiversity at the Natural History and Science Museum of the University of Porto. The venue presented significant deployment issues, namely restrictions on beacon placement and lighting conditions. We trained independent ML models on RSSI and video datasets, and combined them through ensemble learning methods. The experimental results from test scenarios, which included simulated visitor trajectories, showed that ensemble models consistently outperformed the RSSI-based and video-based models. These findings demonstrate that the use of multimodal data can significantly improve IPS accuracy despite constraints such as multipath interference, low lighting, and limited beacon infrastructure. Overall, they highlight the potential of multimodal data for deployments in complex indoor environments.

Abstract
Lock-free data structures have become increasingly significant due to their algorithmic advantages in multi-core cache-based architectures. Safe Memory Reclamation (SMR) is a technique used in concurrent programming to ensure that memory can be safely reclaimed without causing data corruption, dangling pointers, or access to freed memory. The ERA theorem states that any SMR method for concurrent data structures can only provide at most two of the three main desirable properties: Ease of use, Robustness, and Applicability. This fundamental trade-off influences the design of efficient lock-free data structures at an early stage. This work redesigns a previous lock-free hash map to fully exploit the properties of the ERA theorem and to leverage the characteristics of multi-core cache-based architectures by minimizing the number of cache misses, which are a significant bottleneck in multi-core environments. Experimental results show that our design outperforms the previous design, which was already quite competitive when compared against the Concurrent Hash Map design of the Intel's TBB library.