Abstract
Incidental visualizations are meant to be perceived at-a-glance, on-the-go, and during short exposure times, but are not seen on demand. Instead, they appear in people's fields of view during an ongoing primary task. They differ from glanceable visualizations because the information is not received on demand, and they differ from ambient visualizations because the information is not continuously embedded in the environment. However, current graphical perception guidelines do not consider situations where information is presented at specific moments during brief exposure times without being the user's primary focus. Therefore, we conducted a crowdsourced user study with 99 participants to understand how accurate people's incidental graphical perception is. Each participant was tested on one of the three conditions: position of dots, length of lines, and angle of lines. We varied the number of elements for each combination and the display time. During the study, participants were asked to perform reproduction tasks, where they had to recreate a previously shown stimulus in each. Our results indicate that incidental graphical perception can be accurate when using position, length, and angles. Furthermore, we argue that incidental visualizations should be designed for low exposure times (between 300 and 1000 ms).

Abstract
Immersive experiences offered by virtual reality (VR) have the power to impact tourists' decision-making and on-site experiences. However, prior research has focused on explaining VR's acceptance by tourists as a function of technological capacity, rather than user characteristics, such as innovativeness. This research intends to fill the existing knowledge gap regarding the role of technological innovativeness on VR experiences. To do so, this study examines whether individuals' technological innovativeness influences their perceptions of a VR tourism experience and, in turn, their intention to recommend the experience. The study provides a VR experience to 405 tourists at a winery. The results reveal that the tourists' technological innovativeness influences their perception of enjoyment, experience quality, and satisfaction. These findings suggest that, when developing or distributing VR content, the technological innovativeness of the audience is important to consider. The findings have theoretical and practical value, with direct implications for tourism professionals and policymakers.

Abstract
The increase in digital teaching and learning methodologies creates the opportunity for new educational approaches, both in terms of pedagogical practice and in the availability of new technological tools. The flipped classroom as an active teaching methodology is one example of blended learning (b-learning), which aims to harmonize and enhance the fusion of face-to-face teaching with online teaching, allowing students to get better use of both face-to-face contact with classmates and professors and digital teaching resources. However, active teaching methodologies allow us to merge educational techniques from different methodological approaches, for example, gamification and team-based learning (TBL), among others. This study aims to demonstrate how to implement a flipped classroom with the possibility of integrating gamification and TBL, indicating possibilities and challenges to overcome, through the comparative study and research carried out with students in higher education. The study was conducted with a group of 88 students from the engineering and technology fields, which showed that students have a very positive perception of active teaching methodologies and their teaching and learning techniques, especially those involving digital. Data collection was performed by a survey submitted to quantitative analysis using the Software SPSS version 28. © 2024 the author(s)

Abstract
This paper introduces Labadain-30k+, a monolingual dataset comprising 33.6k documents in Tetun, a low-resource language spoken in Timor-Leste. The dataset was acquired through web crawling and augmented with Wikipedia documents released by Wikimedia. Both sets of documents underwent thorough manual audits at the document level by native Tetun speakers, resulting in the construction of a Tetun text dataset well-suited for a variety of natural language processing and information retrieval tasks. This dataset was employed to conduct a comprehensive content analysis aimed at providing a nuanced understanding of document composition and the evolution of Tetun documents on the web. The analysis revealed that news articles constitute the predominant documents within the dataset, accounting for 89.87% of the total, followed by Wikipedia documents at 4.34%, and legal and governmental documents at 3.65%, among others. Notably, there was a substantial increase in the number of documents in 2020, indicating 11.75 percentage points rise in document quantity, compared to an average of 4.76 percentage points per year from 2001 to 2023. Moreover, the year 2017, marked by the increased popularity of online news in Tetun, served as a threshold for analyzing the evolution of document writing on the web pre- and post-2017, specifically regarding vocabulary usage. Surprisingly, this analysis showed a significant increase of 6.12 percentage points in the Tetun written adhering to the Tetun official standard. Additionally, the persistence of Portuguese loanwords in that trajectory remained evident, reflecting an increase of 5.09 percentage points. © 2024 ELRA Language Resource Association.

Abstract

Abstract
This paper focuses on the importance of Business Intelligence (BI) tools in the business context and the urgent need for more effective implementation of time series forecasting models in these resources. It shows the utility and applicability of Sage Enterprise Intelligence (SEI), an integrated BI tool in Enterprise Resource Planning (ERP) Sage, by illustrating how it enhances data analysis and decision-making processes. Additionally, a study will show the application of time series forecasting models: Seasonal AutoRegressive Integrated Moving Average (SARIMA) and additive Holt-Winters to the sales value of a fuel sector company. The research was conducted through a case study in which sales data were collected from 2016 to 2023. The results indicate that neither of the two models exceeded the sales figures reflecting the company's market position. In this case study, both models performed well, with the residuals verifying the assumptions. However, the additive Holt-Winters model had lower errors, which is why it was selected for the final step: forecasting 12 months.