Chest X-ray images are useful for early COVID-19 diagnosis with the advantage that X-ray devices are already available in health centers and images are obtained immediately. Some datasets containing X-ray images with cases (pneumonia or COVID-19) and controls have been made available to develop machine-learning-based methods to aid in diagnosing the disease. However, these datasets are mainly composed of different sources coming from pre-COVID-19 datasets and COVID-19 datasets. Particularly, we have detected a significant bias in some of the released datasets used to train and test diagnostic systems, which might imply that the results published are optimistic and may overestimate the actual predictive capacity of the techniques proposed. In this article, we analyze the existing bias in some commonly used datasets and propose a series of preliminary steps to carry out before the classic machine learning pipeline in order to detect possible biases, to avoid them if possible and to report results that are more representative of the actual predictive power of the methods under analysis.

Pose estimation assesses the 6D pose of one or many objects in a scene. Considerable attention has been dedicated to the advancement of pose estimation algorithms capable of identifying the orientation of multiple objects within a scene in cases where partial occlusion occurs. However, only a few works focus on developing a parallelizable hypotheses-based estimator that naturally handles object symmetries. These algorithms should also tackle some issues: meaningless perspectives, objects with multiple uncertain local poses but a single global correct pose, and multiple correct poses. This paper proposes a novel probabilistic algorithm for pose estimation that addresses these issues. This probabilistic algorithm combines the information from multiple cameras to achieve a unique prediction that assembles global object information. The algorithm is tested over synthetic objects that simulate these issues. It achieves a rotation error below 1.5°, and a translation error of 1.5 pixels in the datasets used. Those results suggest that the algorithm can handle the mentioned issues up to a certain accuracy. Additionally, the method is compared against a state-of-the-art methodology of the LineMOD dataset. This comparison shows that our algorithm can compete against state-of-the-art algorithms in terms of accuracy.

Data drift can significantly impact the outcome of a model. Early detection of data drift is crucial for ensuring user confidence in predictions. It allows the user to check if a particular model needs retraining using updated data to adapt to the evolving process dynamics. This study compares five different statistical tests, namely four unidimensional and a new multidimensional test (MSPC), to identify data drift in both mean and deviation. While some are designed to detect drift in mean only, like our multidimensional proposal, others respond to changes in both mean and deviation. However, our Hotelling multidimensional method can be trained once and then applied in a single stage to any data stream with several attributes, and it can identify the most relevant variables causing a data drift with one execution, thus avoiding the need for a single univariate test for each attribute. Moreover, our method yields the relative importance of each attribute for drift and allows users to increase or decrease the relative weight of each variable regarding drift detection. It also may be capable of detecting drift due to changes in multivariate interactions. This behavior is especially suitable for real-world scenarios, such as industry, finance, or healthcare environments.

Industrial inspection industry requires high precision, fast and reliable systems, where images play a central role. These systems are composed by several hardware and also cyber-physical componentes where complexity increases when multiple heterogeneous sensor inputs are combined. Our 3D industrial inspection scanner is able to reconstruct complete objects without occlusion with use of multiple sensors and actuators using a complex software architecture. Our system allows increasing the throughput by removing the bottleneck network issue, decreasing network data transfer using a new edge systems architecture that segments and optimizes image transferring. Also, this work presents the results of applying technology developed during the FitOptiVis European ECSEL project. FitOptiVis will provide a reference architecture supporting composability built on suitable component abstractions and embedded sensing, actuation and processing devices adhering to those abstractions. The reference architecture will support design portability, on-line multi-objective quality and resource management and run-time adaptation guaranteeing system constraints and requirements based on platform virtualization. The FitOptiVis project will be applied to design the new architecture of the new edge components and develop the runtime system monitoring.