Random Sample Consensus, most commonly abbreviated as RANSAC, is a robust estimation method for the parameters of a model contaminated by a sizable percentage of outliers. In its simplest form, the process starts with a sampling of the minimum data needed to perform an estimation, followed by an evaluation of its adequacy, and further repetitions of this process until some stopping criterion is met. Multiple variants have been proposed in which this workflow is modified, typically tweaking one or several of these steps for improvements in computing time or the quality of the estimation of the parameters. RANSAC is widely applied in the field of robotics, for example, for finding geometric shapes (planes, cylinders, spheres, etc.) in cloud points or for estimating the best transformation between different camera views. In this paper, we present a review of the current state of the art of RANSAC family methods with a special interest in applications in robotics.

This paper presents a music generation method which is an extension of a previously presented method that generates coherent melodies using a melodic coherence structure extracted from a template piece. This extension, which has been applied for generating bertso melodies, adds the generation of the rhythmic content of the melodies, for which a rhythmic coherence structure of the template piece is also created. To do so, a pattern discovery and ranking method is used to discover the rhythmically repeated segments that are interesting, and create a rhythmic coherence structure which can have several levels of nesting. Independent sampling processes have been developed for melodic and rhythmic content, using an adapted optimization method for sampling the rhythmic content of the new pieces. An evaluation process has been carried out to evaluate some of the generated pieces, considering on one hand how the listeners perceive them and on the other hand whether they share the features with bertso melodies. It has been concluded from this evaluation that the method is capable of generating good coherent bertso melodies.

Deep neural network models have been developed in different fields, bringing many advances in several tasks. However, they have also started to be incorporated into tasks with critical risks. That worries researchers who have been interested in studying possible attacks on these models, discovering a long list of threats from which every model should be defended. The weight modification attack is presented and discussed among researchers, who have presented several versions and analyses about such a threat. It focuses on detecting multiple vulnerable weights to modify, misclassifying the desired input data. Therefore, analysis of the different approaches to this attack helps understand how to defend against such a vulnerability. This work presents a new version of the weight modification attack. Our approach is based on three processes: input data clusterization, weight selection, and modification of the weights. Data clusterization allows a directed attack to a selected class. Weight selection uses the gradient given by the input data to identify the most-vulnerable parameters. The modifications are incorporated in each step via limited noise. Finally, this paper shows how this new version of fault injection attack is capable of misclassifying the desired cluster completely, converting the 100% accuracy of the targeted cluster to 0–2.7% accuracy, while the rest of the data continues being well-classified. Therefore, it demonstrates that this attack is a real threat to neural networks.

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