Abstract
Public datasets are becoming extremely important for the scientific and industrial community to accelerate the development of new approaches and to guarantee identical testing conditions for comparing methods proposed by different researchers. This research presents the Urban@CRAS dataset that captures several scenarios of one iconic region at Porto Portugal These scenario presents a multiplicity of conditions and urban situations including, vehicle-to-vehicle and vehicle-to-human interactions, cross-sides, turn-around, roundabouts and different traffic conditions. Data from these scenarios are timestamped, calibrated and acquired at 10 to 200 Hz by through a set of heterogeneous sensors installed in a roof of a car. These sensors include a 3D LIDAR, high-resolution color cameras, a high-precision IMU and a GPS navigation system. In addition, positioning information obtained from a real-time kinematic satellite navigation system (with 0.05m of error) is also included as ground-truth. Moreover, a benchmarking process for some typical methods for visual odometry and SLAM is also included in this research, where qualitative and quantitative performance indicators are used to discuss the advantages and particularities of each implementation. Thus, this research fosters new advances on the perception and navigation approaches of autonomous robots (and driving).

Abstract
Regulations, laws, norms, and other documents of legal nature are a relevant part of any governmental organisation. During digitisation and transformation stages towards a digital government model, information and communication technologies are explored to improve internal processes and working practices of government infrastructures. This paper introduces preliminary results on a research line devoted to developing visualisation techniques for enhancing the readability and comprehension of legal texts. The content of documents is conveyed to a well-defined model, which is enriched with semantic information extracted automatically. Then, a set of digital views are created for document exploration from both a structural and semantic point of view. Effective and easier to use digital interfaces can enable and promote citizens engagement in decision-making processes, provide information for the public, and also enhance the study and analysis of legal texts by lawmakers, legal practitioners, and assorted scholars. © 2018 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

Abstract
Interpretability is a fundamental property for the acceptance of machine learning models in highly regulated areas. Recently, deep neural networks gained the attention of the scientific community due to their high accuracy in vast classification problems. However, they are still seen as black-box models where it is hard to understand the reasons for the labels that they generate. This paper proposes a deep model with monotonic constraints that generates complementary explanations for its decisions both in terms of style and depth. Furthermore, an objective framework for the evaluation of the explanations is presented. Our method is tested on two biomedical datasets and demonstrates an improvement in relation to traditional models in terms of quality of the explanations generated. © Springer Nature Switzerland AG 2018.

Abstract
Scheduling assumes a crucial importance in manufacturing systems, optimizing the allocation of operations to the right resources at the most appropriate time. Particularly in the Flexible Manufacturing System (FMS) topology, where the combination of possibilities for this association exponential increases, the scheduling task is even more critical. This paper presents a heuristic scheduling method based on genetic algorithm for a robotic-centric FMS. Real experiments show the effectiveness of the proposed algorithm, ensuring a reliable and optimized scheduling process. © 2018 by World Scientific Publishing Co. Pte. Ltd.

Abstract
Innovative processes become available due to the high processing capacity of emergent infrastructures, such as cloud and ubiquitous computing and organizational infrastructures and applications. However, these intense computation processes are difficult to follow, where co-decision is required, for which the existence of disruptive visualization and collaboration tools that offer a visual tracing capacity with integrated decision supporting tools, are critical for its sustainable success. This project proposes: a) a set of immersive and disruptive visualization tools, supported by virtual and augmented reality, that enables a global perspective of any production agents; b) a data analytics tool to complement and assist the decision making; c) a resource federated network that allows the brokering and interaction between all existing resources; and d) a dynamic context-aware dashboard, to improve the overall productive process and contribute to intelligent manufacturing systems. The application domain addressed is Zero-Defects Diagnostics in manufacturing as well as in Industry 4.0 in general. © 2017 The Authors.

Abstract
Tomographic wavefront reconstruction is the main computational bottleneck to realize real-time correction for turbulence-induced wavefront aberrations in future laser-assisted tomographic adaptive-optics (AO) systems for ground-based giant segmented mirror telescopes because of its unprecedented number of degrees of freedom, N, i.e., the number of measurements from wavefront sensors. In this paper, we provide an efficient implementation of the minimum-mean-square error (MMSE) tomographic wavefront reconstruction, which is mainly useful for some classes of AO systems not requiring multi-conjugation, such as laser-tomographic AO, multi-object AO, and ground-layer AO systems, but is also applicable to multi-conjugate AO systems. This work expands that by Conan [Proc. SPIE 9148, 91480R (2014)] to the multi-wavefront tomographic case using natural and laser guide stars. The new implementation exploits the Toeplitz structure of covariance matrices used in an MMSE reconstructor, which leads to an overall ON log N real-time complexity compared with ON2 of the original implementation using straight vector-matrix multiplication. We show that the Toeplitz-based algorithm leads to 60 nm rms wavefront error improvement for the European Extremely Large Telescope laser-tomography AO system over a well-known sparse-based tomographic reconstruction; however, the number of iterations required for suitable performance is still beyond what a real-time system can accommodate to keep up with the time-varying turbulence.