Abstract
This chapter aims to understand how Esports organizations can improve digital marketing strategies, considering the unique characteristics of this sector and the importance of maintaining solid relationships with the target audience. The research was carried out using a mixed methodology, which included the application of quantitative research to evaluate the behaviors of Esports fans and a qualitative literature review to explore the trends and challenges of digital marketing in this context. The results show that the esports audience consists predominantly of young males, with a strong interest in video games, technology and pop culture. The personalization of digital strategies, focusing on platforms such as YouTube and Twitch, as well as the use of promotions and sweepstakes, proved essential for audience engagement. Although the use of influencers has a neutral perception, campaigns that offer direct benefits, such as promotions, are more attractive.

Abstract
Context: Testing activities are essential for the quality assurance of mobile applications under development. Despite its importance, some studies show that testing is not widely applied in mobile applications. Some characteristics of mobile devices and a varied market of mobile devices with different operating system versions lead to a highly fragmented mobile ecosystem. Thus, researchers put some effort into proposing different solutions to optimize mobile application testing. Objective: The main goal of this paper is to provide a categorization and classification of existing testing infrastructures to support mobile application testing. Methods: To this aim, the study provides a Systematic Mapping Study of 27 existing primary studies. Results: We present a new classification and categorization of existing types of testing infrastructure, the types of supported devices and operating systems, whether the testing infrastructure is available for usage or experimentation, and supported testing types and applications. Conclusion: Our findings show a need for mobile testing infrastructures that support multiple phases of the testing process. Moreover, we showed a need for testing infrastructure for context-aware applications and support for both emulators and real devices. Finally, we pinpoint the need to make the research available to the community whenever possible.

Abstract
We present CIDER, a novel framework for the collaborative editing of 3D augmented scenes. The framework allows multiple users to manipulate the virtual elements added to the real environment independently and without unexpected changes, comparing the different editing proposals and finalising a collaborative result. CIDER leverages the use of 'layers' encapsulating the state of the environment. Private layers can be edited independently by the different subjects, and a global one can be collaboratively updated with 'commit' operations. In this paper, we describe in detail the system architecture and the implementation as a prototype for the HoloLens 2 headsets, as well as the motivations behind the interaction design. The system has been validated with a user study on a realistic interior design task. The study not only evaluated the general usability but also compared two different approaches for the management of the atomic commit: forced (single-phase) and voting (requiring consensus), analyzing the effects of this choice on collaborative behaviour. According to the users' comments, we performed improvements to the interface and further tested their effectiveness.

Abstract
Explainable AI (xAI) emerged as one of the ways of addressing the interpretability issues of the so-called black-box models. Most of the xAI artifacts proposed so far were designed, as expected, for human users. In this work, we posit that such artifacts can also be used by computer systems. Specifically, we propose a set of metrics derived from LIME explanations, that can eventually be used to ascertain the quality of each output of an underlying image classification model. We validate these metrics against quantitative human feedback, and identify 4 potentially interesting metrics for this purpose. This research is particularly useful in concept drift scenarios, in which models are deployed into production and there is no new labelled data to continuously evaluate them, becoming impossible to know the current performance of the model.

Abstract
Chest radiography (CXR) is one of the most used medical imaging modalities. Nevertheless, the interpretation of CXR images is time-consuming and subject to variability. As such, automated systems for pathology detection have been proposed and promising results have been obtained, particularly using deep learning. However, these tools suffer from poor explainability, which represents a major hurdle for their adoption in clinical practice. One proposed explainability method in CXR is through contrastive examples, i.e. by showing an alternative version of the CXR except without the lesion being investigated. While image-level normal/healthy image synthesis has been explored in literature, normal patch synthesis via inpainting has received little attention. In this work, a method to synthesize contrastive examples in CXR based on local synthesis of normal CXR patches is proposed. Based on a contextual attention inpainting network (CAttNet), an anatomically-guided inpainting network (AnaCAttNet) is proposed that leverages anatomical information of the original CXR through segmentation to guide the inpainting for a more realistic reconstruction. A quantitative evaluation of the inpainting is performed, showing that AnaCAttNet outperforms CAttNet (FID of 0.0125 and 0.0132 respectively). Qualitative evaluation by three readers also showed that AnaCAttNet delivers superior reconstruction quality and anatomical realism. In conclusion, the proposed anatomical segmentation module for inpainting is shown to improve inpainting performance.

Abstract
The use of Convolutional Neural Network (CNN) models for image classification tasks has gained significant popularity. However, the lack of interpretability in CNN models poses challenges for debugging and validation. To address this issue, various explanation methods have been developed to provide insights into CNN models. This paper focuses on the validity of these explanation methods for ordinal regression tasks, where the classes have a predefined order relationship. Different modifications are proposed for two explanation methods to exploit the ordinal relationships between classes: Grad-CAM based on Ordinal Binary Decomposition (GradOBDCAM) and Ordinal Information Bottleneck Analysis (OIBA). The performance of these modified methods is compared to existing popular alternatives. Experimental results demonstrate that GradOBD-CAM outperforms other methods in terms of interpretability for three out of four datasets, while OIBA achieves superior performance compared to IBA.