Gamification methods adapt the mechanics of games to educational environments for the improvement of the teaching-learning process. Serious games play an important role as tools for gamification, in particular in the context of software engineering courses because of the idiosyncratic nature of the topic. However, the studies on the improvement of student performance resulting from the use of gamification and serious games in courses with different contexts are not conclusive. More empirical research is thus needed to obtain reliable results on the effectiveness, benefits and drawbacks. The overall objective of this work is to study the benefits generated by serious games in the teaching-learning process of Computer Engineering degrees, analyzing the impact on the motivation and student satisfaction, as well as on the learning outcomes and results finally achieved. To this end, an intervention is proposed in the subject of Computer Architecture based on two components covering theoretical and practical sessions. In the theoretical sessions, a serious game experience using Kahoot has been introduced, complementing the master classes and class exercises. For the practical sessions, the development of projects with groups of students has been proposed, whose results in terms of computer performance can be compared through a competition (hackathon). Evaluation of the serious game-based intervention has been approached in terms of student satisfaction and motivation, as well as improved academic performance. In order to assess student satisfaction, surveys have been used to assess the effect on student motivation and satisfaction. For the evaluation of academic performance, a comparative analysis between an experimental and a control group has been carried out, noting a slight increase in the experimental group students’ marks.

This paper reviews recent deep learning-based registration methods. Registration is the process that computes the transformation that aligns datasets, and the accuracy of the result depends on multiple factors. The most significant factors are the size of input data; the presence of noise, outliers and occlusions; the quality of the extracted features; real-time requirements; and the type of transformation, especially those defined by multiple parameters, such as non-rigid deformations. Deep Registration Networks (DRNs) are those architectures trying to solve the alignment task using a learning algorithm. In this review, we classify these methods according to a proposed framework based on the traditional registration pipeline. This pipeline consists of four steps: target selection, feature extraction, feature matching, and transform computation for the alignment. This new paradigm introduces a higher-level understanding of registration, which makes explicit the challenging problems of traditional approaches. The main contribution of this work is to provide a comprehensive starting point to address registration problems from a learning-based perspective and to understand the new range of possibilities.

Preserving maritime ecosystems is a major concern for governments and administrations. Additionally, improving fishing industry processes, as well as that of fish markets, to have a more precise evaluation of the captures, will lead to a better control on the fish stocks. Many automated fish species classification and size estimation proposals have appeared in recent years, however, they require data to train and evaluate their performance. Furthermore, this data needs to be organized and labelled. This paper presents a dataset of images of fish trays from a local wholesale fish market. It includes pixel-wise (mask) labelled specimens, along with species information, and different size measurements. A total of 1,291 labelled images were collected, including 7,339 specimens of 59 different species (in 60 different class labels). This dataset can be of interest to evaluate the performance of novel fish instance segmentation and/or size estimation methods, which are key for systems aimed at the automated control of stocks exploitation, and therefore have a beneficial impact on fish populations in the long run.

The use of RGB-D sensors for mapping and recognition tasks in robotics or, in general, for virtual reconstruction has increased in recent years. The key aspect of these kinds of sensors is that they provide both depth and color information using the same device. In this paper, we present a comparative analysis of the most important methods used in the literature for the registration of subsequent RGB-D video frames in static scenarios. The analysis begins by explaining the characteristics of the registration problem, dividing it into two representative applications: scene modeling and object reconstruction. Then, a detailed experimentation is carried out to determine the behavior of the different methods depending on the application. For both applications, we used standard datasets and a new one built for object reconstruction.