Abstract
Purpose - This study investigates the barriers to the adoption of Virtual Reality (VR) in the tourism industry. Although VR has great potential to enhance the tourist experience, the adoption of this technology is still limited in the tourism sector. Building on the fundamental principles of the Technology -Organization -Environment (TOE) theory and its contribution to perceptions of technology adoption, this study aims to fill the knowledge gap regarding the specific barriers to VR adoption by tourism enterprises. Methodology - To achieve this objective, interviews were conducted with managers of tourism companies, and the data was analysed using qualitative methodology through MAXQDA 20 software. Conclusions - The results reveal that the main barriers identified by managers mainly include lack of knowledge about VR, particularly in the tourism sector. The perceived lack of usefulness, limited experience with the technology, and reluctance to invest in technological equipment also emerge as barriers to VR adoption. Originality of research - This study can help companies in the tourism sector to develop more effective strategies to overcome these barriers, thereby improving the tourist experience and increasing their competitiveness in the market using VR equipment.

Abstract

Abstract
Data-to-text systems offer a transformative approach to generating textual content in data-rich environments. This paper describes the architecture and deployment of Prosebot, a community-driven data-to-text platform tailored for generating textual summaries of football matches derived from match statistics. The system enhances the visibility of lower-tier matches, traditionally accessible only through data tables. Prosebot uses a template-based Natural Language Generation (NLG) module to generate initial drafts, which are subsequently refined by the reading community. Comprehensive evaluations, encompassing both human-mediated and automated assessments, were conducted to assess the system's efficacy. Analysis of the community-edited texts reveals that significant segments of the initial automated drafts are retained, suggesting their high quality and acceptance by the collaborators. Preliminary surveys conducted among platform users highlight a predominantly positive reception within the community.

Abstract
Narratives have been the subject of extensive research across various scientific fields such as linguistics and computer science. However, the scarcity of freely available datasets, essential for studying this genre, remains a significant obstacle. Furthermore, datasets annotated with narratives components and their morphosyntactic and semantic information are even scarcer. To address this gap, we developed the Text2Story Lusa datasets, which consist of a collection of news articles in European Portuguese. The first datasets consists of 357 news articles and the second dataset comprises a subset of 117 manually densely annotated articles, totaling over 50 thousand individual annotations. By focusing on texts with substantial narrative elements, we aim to provide a valuable resource for studying narrative structures in European Portuguese news articles. On the one hand, the first dataset provides researchers with data to study narratives from various perspectives. On the other hand, the annotated dataset facilitates research in information extraction and related tasks, particularly in the context of narrative extraction pipelines. Both datasets are made available adhering to FAIR principles, thereby enhancing their utility within the research community.

Abstract
This paper proposes Labadain Crawler, a data collection pipeline tailored to automate and optimize the process of constructing textual corpora from the web, with a specific target to low-resource languages. The system is built on top of Nutch, an open-source web crawler and data extraction framework, and incorporates language processing components such as a tokenizer and a language identification model. The pipeline efficacy is demonstrated through successful testing with Tetun, one of Timor-Leste's official languages, resulting in the construction of a high-quality Tetun text corpus comprising 321.7k sentences extracted from over 22k web pages. The contributions of this paper include the development of a Tetun tokenizer, a Tetun language identification model, and a Tetun text corpus, marking an important milestone in Tetun text information retrieval.

Abstract
Modern hardware accelerators, such as FPGAs, allow offloading large regions of C/C++ code in order to improve the execution time and/or the energy consumption of software applications. An outstanding challenge with this approach, however, is solving the Hardware/Software (Hw/Sw) partitioning problem. Given the increasing complexity of both the accelerators and the potential code regions, one needs to adopt a holistic approach when selecting an offloading region by exploring the interplay between communication costs, data usage patterns, and target-specific optimizations. To this end, we propose representing a C application as an extended task graph (ETG) with flexible granularity, which can be manipulated through the merging and splitting of tasks. This approach involves generating a task graph overlay on the program's Abstract Syntax Tree (AST) that maps tasks to functions and the flexible granularity operations onto inlining/outlining operations. This maintains the integrity and readability of the original source code, which is paramount for targeting different accelerators and enabling code optimizations, while allowing the offloading of code regions of arbitrary complexity based on the data patterns of their tasks. To evaluate the ETG representation and its compiler, we use the latter to generate ETGs for the programs in Rosetta and MachSuite benchmark suites, and extract several metrics regarding data communication, task-level parallelism, and dataflow patterns between pairs of tasks. These metrics provide important information that can be used by Hw/Sw partitioning methods.