Abstract
One of the main strategies for fostering the entrepreneurial mindset in students has been the development of training activities inside and outside classrooms using serious games. The chapter makes a brief description regarding five serious games initiatives that intend to promote and facilitate entrepreneurship education for different educational stages. Furthermore, the authors discuss the key benefits and challenges by the introduction of serious games in the learning process. The authors concluded by identifying that serious games have the potentiality of improving the learning process by providing an immersive experience for students that increases their motivation and potentiates the acquisition of multidisciplinary competencies. However, there are some challenges that should be properly considered and mitigated, namely in terms of assessing learning goals, integration with didactical systems, and support for different styles of learning.

Abstract
Technological advancements have created a media ecosystem in which traditional journalism sees its existence strongly threatened by the emergence of new players. Social networks have created a competitive environment that, whether due to its dispersion or its capillarity, has relegated the mainstream media to a secondary role in the media ecosystem. Ironically, the technologies that threaten traditional journalism are also those that can save it; provided they are used correctly. Journalism, weakened by the economic crisis and with increasingly smaller newsrooms, has artificial intelligence as an opportunity to recover a certain centrality in the media ecosystem. This paper studies AIDA, a project from the Brazilian television network Globo. This project looked to automation as a way to avoid errors and ambiguities in the news. The study of the AIDA case, complemented by interviews, presents the challenges to achieve the automatization of news regarding electoral polls.

Abstract
This paper describes the research, development and evaluation process of a solution based on computer vision for the detection and prevention of Computer Vision Syndrome, a type of eye fatigue characterized by the appearance of ocular symptoms during or after prolonged periods watching digital screens. The system developed targets users of computers and mobile devices, detecting and warning users to the occurrence of eye fatigue situations and suggesting corrective behaviours in order to prevent more complicated health consequences. The implementation resorts to machine learning techniques, using eye images datasets for training the eye state detection algorithm. OpenCV Lib was used for eye's segmentation and subsequent fatigue analysis. The final goal of the system is to provide users and health professionals with quality data analysis of eye fatigue levels, in order to raise awareness over accumulated stress and promote behaviour change. © 2019 IEEE.

Abstract
In this paper, a method for estimation of human pose is proposed, making use of ToF (Time of Flight) cameras. For this, a YOLO based object detection method was used, to develop a top-down method. In the first stage, a network was developed to detect people in the image. In the second stage, a network was developed to estimate the joints of each person, using the image result from the first stage. We show that a deep learning network trained from scratch with ToF images yields better results than taking a deep neural network pretrained on RGB data and retraining it with ToF data. We also show that a top-down detector, with a person detector and a joint detector works better than detecting the body joints over the entire image.

Abstract
The fusion of pre-operative 3D magnetic resonance (MR) images with real-time 3D ultrasound (US) images can be the most beneficial way to guide minimally invasive cardiovascular interventions without radiation. Previously, we addressed this topic through a strategy to segment the left ventricle (LV) on interventional 3D US data using a personalized shape prior obtained from a pre-operative MR scan. Nevertheless, this approach was semi-automatic, requiring a manual alignment between US and MR image coordinate systems. In this paper, we present a novel solution to automate the abovementioned pipeline. In this sense, a method to automatically detect the right ventricular (RV) insertion point on the US data was developed, which is subsequently combined with pre-operative annotations of the RV position in the MR volume, therefore allowing an automatic alignment of their coordinate systems. Moreover, a novel strategy to ensure a correct temporal synchronization of the US and MR models is applied. Finally, a full evaluation of the proposed automatic pipeline is performed. The proposed automatic framework was tested in a clinical database with 24 patients containing both MR and US scans. A similar performance between the proposed and the previous semi-automatic version was found in terms of relevant clinical measurements. Additionally, the automatic strategy to detect the RV insertion point showed its effectiveness, with a good agreement against manually identified landmarks. Overall, the proposed automatic method showed high feasibility and a performance similar to the semi-automatic version, reinforcing its potential for normal clinical routine.

Abstract
Deformational Plagiocephaly (DP) refers to an asymmetrical distortion of an infant's skull resulting from external forces applied over time. The diagnosis of this condition is performed using asymmetry indexes that are estimated from specific anatomical landmarks, whose are manually defined on head models acquired using laser scans. However, this manual identification is susceptible to intra-/inter-observer variability, being also time-consuming. Therefore, automatic strategies for the identification of the landmarks and, consequently, extraction of asymmetry indexes, are claimed. A novel pipeline to automatically identify these landmarks on 3D head models and to estimate the relevant cranial asymmetry indexes is proposed. Thus, a template database is created and then aligned with the unlabelled patient through an iterative closest point (ICP) strategy. Here, an initial rigid alignment followed by an affine one are applied to remove global misalignments between each template and the patient. Next, a non-rigid alignment is used to deform the template information to the patient-specific shape. The final position of each landmark is computed as a local weight average of all candidate results. From the identified landmarks, a head's coordinate system is automatically estimated and later used to estimate cranial asymmetry indexes. The proposed framework was evaluated in 15 synthetic infant head's model. Overall, the results demonstrated the accuracy of the identification strategy, with a mean average distance of 2.8 +/- 0.6 mm between the identified landmarks and the ground-truth. Moreover, for the estimation of cranial asymmetry indexes, a performance comparable to the inter-observer variability was achieved.