2018
Authors
Gaspar, AR; Nunes, A; Pinto, AM; Matos, A;
Publication
ROBOTICS AND AUTONOMOUS SYSTEMS
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).
2018
Authors
Carvalho, NR; Barbosa, LS;
Publication
ICEGOV
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
Authors
Silva, W; Fernandes, K; Cardoso, MJ; Cardoso, JS;
Publication
MLCN/DLF/iMIMIC@MICCAI
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.
2018
Authors
Pereira, AI; Ferreira, A; Barbosa, J; Lima, J; Leitão, P;
Publication
Human-Centric Robotics- Proceedings of the 20th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2017
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.
2018
Authors
Felinto, AS; Jacobina, CB; Carlos, GAA; Mello, JPRA; de Freitas, NB; da Silva, I;
Publication
2018 IEEE Energy Conversion Congress and Exposition (ECCE)
Abstract
2018
Authors
Alves, S; Broda, S;
Publication
FSCD
Abstract
In this paper we define a framework to address different kinds of problems related to type inhabitation, such as type checking, the emptiness problem, generation of inhabitants and counting, in a uniform way. Our framework uses an alternative representation for types, called the pre-grammar of the type, on which different methods for these problems are based. Furthermore, we define a scheme for a decision algorithm that, for particular instantiations of the parameters, can be used to show different inhabitation related problems to be in PSPACE.
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