Abstract
This paper presents a solution for visualizing oil spills at sea by combining satellite data with virtual choreographies. The system enables dynamic, interactive visualization of oil slicks, reflecting their shape, movement, and interaction with environmental factors like currents and wind. High resolution geospatial data supports a multiplatform experience with aerial and underwater perspectives. This approach promotes independence, interoperability, and multiplatform compatibility in environmental disaster monitoring. The results validate virtual choreographies as effective tools for immersive exploration and analysis, offering structured data narratives beyond passive visualization especially valuable for mixed reality applications.

Abstract
Deep Learning (DL) has emerged as a powerful tool for vulnerability detection, often outperforming traditional solutions. However, developing effective DL models requires large amounts of real-world data, which can be difficult to obtain in sufficient quantities. To address this challenge, DiverseVul dataset has been curated as one of the largest datasets of vulnerable and non-vulnerable C/C++ functions extracted exclusively from real-world projects. Its goal is to provide high-quality, large-scale samples for training DL models. Nevertheless, during our study several inconsistencies were identified in the raw dataset while applying pre-processing techniques, highlighting the need for a refined version. In this work, we present a refined version of DiverseVul dataset, which is used to fine-tune a large language model, LLaMA 3.2, for vulnerability detection. Experimental results show that the use of pre-processing techniques led to an improvement in performance, with the model achieving an F1-Score of 66%, a competitive result when compared to our baseline, which achieved a 47% F1-Score in software vulnerability detection.

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

Abstract
Stroke often leads to severe motor impairment, especially in the upper limbs, greatly reducing a patient's ability to perform daily tasks. Effective rehabilitation is essential to restore function and improve quality of life. Traditional therapies, while useful, may lack engagement, leading to low motivation and poor adherence. Gamification-using game-like elements in non-game contexts-offers a promising way to make rehabilitation more engaging. The authors explore a gamified rehabilitation system designed in Unity 3D using a Kinect V2 camera. The game includes key features such as adjustable difficulty, real-time and predominantly positive feedback, user friendliness, and data tracking for progress. The evaluations were conducted with 18 healthy participants, most of whom had prior virtual reality experience. About 77% found the application highly motivating. While the gameplay was well received, the visual design was noted as lacking engagement. Importantly, all users agreed that the game offers a broad range of difficulty levels, making it accessible to various users. The results suggest that the system has strong potential to improve rehabilitation outcomes and encourage long-term use through enhanced motivation and interactivity.

Abstract
As storytelling influences consumer attitudes and opinions, conditioning the tourist experience by appealing to the imagination, this paper reviews the literature covering the analysis of 66 papers that focus on the storytelling of the visitor/tourist as the main subject. The article is divided into four main themes: (a) storytelling as a tool to attract tourists; (b) the role of the storyteller; (c) the tourist as a storyteller; and (d) what makes a good story. The Hoshin Kanri Matrix was used to showcase each of the main themes. Although storytelling has been widely used to attract tourists, it is crucial that tourist-based storytelling can be a credible substitute for destination-based storytelling, as empathy, authenticity and the emotional attachment of tourists as storytellers play an important role as good stories, transforming and co-creating their experiences that emerge from the interaction of tourists, residents, and intermediaries.