Abstract
Visualizations for Streaming Big Data need to handle high volumes of information in real-time, making it challenging to convey significant data changes without confusing users. A simple first approach would be switching from the current visual idiom to another, highlighting a significant change. Unfortunately, there are no guidelines to design effective transitions between two visual idioms in Streaming Big Data. Therefore, we created a tree of animation concepts to serve as a starting point for designing such animated transitions. The concepts represent several ways in which a visual idiom can be transformed into another. We chose three visual idioms to test our idea and arranged several concepts to apply at each possible pairing (six possibilities). For each pairing, we tested the accuracy of people's perceptions. Finally, we conducted a user study with 100 participants, where each participant answered various questions about transitions between two visual idioms shown in several videos. We concluded that to conceive appropriate animated transitions for Streaming Big Data (which also applies just for Data Streaming) that allow users to understand the changes in incoming data, varying how the proposed concepts are applied is not enough, highlighting the need for future research to address this challenge.

Abstract
In the manufacturing domain, the digital twin has become an emerging concept for decision-making through the integration of what-if simulation capabilities. In such systems, the processing of the entire space of alternative solutions is very time-consuming; recommendation systems are used to solve this; however, these suffer from several problems, namely data sparsity and cold-start. The application of trust-based models can mitigate these problems, particularly the cold-start problems, by providing valuable background for the recommendation system. This paper presents the implementation and experimental validation of a trust-based model for improving the digital twin based what-if simulation recommendation system, addressing the cold-start problems. The proposed trust model was applied in an assembly line case study to recommend the best configurations for the optimal number of AGVs (Autonomous Guided Vehicles). The results show that applying the trust-based model with similarity metrics improved the mitigation of the cold-start problem.

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Abstract
Workplace accidents are a concern for companies nowadays and can occur due to internal and external factors of the company. Thereby, several strategies are developed to predict and minimize the hazards in this environment. Companies resort to intelligent solutions, such as predictive analytics, aiming to increase productivity while ensuring safety in the work environment. In terms of accident prediction analysis, different input data are needed to ensure the accuracy of a predictive model. Therefore, this study aims to automatic collect and pre-process data from holidays for subsequent implementation in an accident-oriented predictive model to demonstrate its relevance in predicting accidents in the workplace.

Abstract
Gamification is a topic which aims to apply game elements to real world tasks, that results in a pleasant influence over a user behaviour towards an objective. Learning is one of the fields where gamification has been implemented and experimented to motivate students and improve their learning process. The first iterations account for the use of game elements such as points, levels and badges or achievements based on task completion according to rules set before. The learning tasks in this approach are not necessarily changed or take advantage of new forms of interactions and guidance. In this article we introduce the application of virtual reality, augmented reality, and machine learning as tools to improve upon the standard application of gamification, making the experience more immersive to the user. We hope to advance gamification to account for more elements, such as digital twins and digital aids in a learning application. In this article we detail possible scenarios for the application of virtual reality and augmented reality combined with machine learning in serious games and learning scenarios.