Abstract
Microcalcifications are early indicators of breast cancer that appear on mammograms as small bright regions within the breast tissue. To assist screening radiologists in reading mammograms, supervised learning techniques have been found successful to detect micro-calcifications automatically. Among them, Convolutional Neural Networks (CNNs) can automatically learn and extract low-level features that capture contrast and spatial information, and use these features to build robust classifiers. Therefore, spatial enhancement that enhances local contrast based on spatial context is expected to positively influence the learning task of the CNN and, as a result, its classification performance. In this work, we propose a novel spatial enhancement technique for microcalcifications based on the removal of haze, an apparently unrelated phenomenon that causes image degradation due to atmospheric absorption and scattering. We tested the influence of dehazing of digital mammograms on the microcalcification detection performance of two CNNs inspired by the popular AlexNet and VGGnet. Experiments were performed on 1, 066 mammograms acquired with GE Senographe systems. Statistically significantly better microcalcification detection performance was obtained when dehazing was used as preprocessing. Results of dehazing were superior also to those obtained with Contrast Limited Adaptive Histogram Equalization (CLAHE).

Abstract
Localization and mapping of autonomous robots in a hard and unstable environment (Steep Slope Vineyards) is a challenging research topic. Typically, the commonly used dead reckoning systems can fail due to the harsh conditions of the terrain and the Global Position System (GPS) accuracy can be considerably noisy or not always available. One solution is to use wireless sensors in a network as landmarks. This paper evaluates a ultra-wideband time-of-flight based technology (Pozyx), which can be used as cost-effective solution for application in agricultural robots that works in harsh environment. Moreover, this paper implements a Localization Extended Kalman Filter (EKF) that fuses odometry with the Pozyx Range measurements to increase the default Pozyx Algorithm accuracy. © Springer International Publishing AG 2018.

Abstract

Abstract
Annotated data is one of the most important components for supervised learning tasks. To ensure the reliability of the models, this data is usually labeled by several human annotators through volunteering or using Crowdsourcing platforms. However, such approaches are unfeasible (regarding time and cost) in datasets with an enormous number of entries, which in the specific case of journalistic relevance detection in social media posts, is necessary due to the wide scope of topics that can be considered relevant. Therefore, with the goal of building a relevance detection model, we propose an architecture to build a large scale annotated dataset regarding the journalistic relevance of Twitter posts (i.e. tweets). This methodology is based on the predictability of the content in Twitter accounts. Next, we used the retrieved dataset and build relevance detection models, combining text, entities, and sentiment features. Finally, we validated the best model through a smaller manually annotated dataset with posts from Facebook and Twitter. The F1-measure achieved in the validation dataset was 63% which is still far from excellent. However, given the characteristics of the validation data, these results are encouraging since 1) our model is not affected by content from other social networks and 2) our validation dataset was restrained to a specific time interval and specific keywords (which can affect the performance of the model). © 2017 IEEE.

Abstract
This paper demonstrates the potential benefits of the integration of robot based sensing and Enterprise Information Systems extended with information about the geometric location and volumetric information of the parts contained in logistic supermarkets. The comparison of this extended world model with hierarchical spatial representations produced by a fleet of robots traversing the logistic supermarket corridors enables the continuous assessment of inconsistencies between reality, i.e., the spatial representations collected from online 3D data, and the modelled information, i.e., the world model. Results show that it is possible to detect inconsistencies reliably and in real time. The proposed approach contributes to the development of more robust and effective Enterprise Information Systems.

Abstract
The Robot Operating System (ROS) is nowadays one of the most popular frameworks for developing robotic applications. To ensure the (much needed) dependability and safety of such applications we forecast an increasing demand for ROS-specific coding standards, static analyzers, and tools alike. Unfortunately, the development of such standards and tools can be hampered by ROS modularity and configurability, namely the substantial number of primitives (and respective variants) that must, in principle, be considered. To quantify the severity of this problem, we have mined a large number of existing ROS packages to understand how its primitives are used in practice, and to determine which combinations of primitives are most popular. This paper presents and discusses the results of this study, and hopefully provides some guidance for future standardization efforts and tool developers.