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Carreteras 2021年第235期4卷 8-18页

作者： Pellicer Armiñana, Eugenio Paredes Palacios, Roberto Felipo Sanjuán, Jesús Sánchez-Robles Bello, Juan Grupo de Gestión del Proceso Proyecto-Construcción Universitat Politécnica de Valencia Spain Centro de Investigación Pattern Recognition and Human Language Technology Universitat Politécnica de Valencia Spain Pavasal Empresa Constructora S.A. Spain Proyectos y Obras. CPS infraestructuras Movilidad y Medio Ambiente S.L. Spain

The integrated system for the efficient maintenance of urban pavements is an innovation project derived from collaboration between a public university and prívate enterprises. This system automates the tasks of auscultation of urban pavements obtaining images with conventional cameras and later processing them, using deep learning techniques. In addition, it Incorporates optimization and predictive decision making that Includes, not only traditional technical criteria (such as the state of the pavement and weather conditions, for example), but also environmental (CO2emissions) and social criteria (proximity to critical infrastructure). The application is based on a friendly visual interface that allows identification and quantification of pavement deterioration in an automatic way, the prediction of the future state of the network, as well as the establishment of an efficient and proactive maintenance and rehabilitation program of urban pavement in the medium-long term. © 2021 Asociacion Espanola de la Carretera. All rights reserved.

关键词： Interface states

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A resource-light method for cross-lingual semantic textual similarity

arXiv

引用

arXiv 2018年

作者： Glavaš, Goran Franco-Salvador, Marc Ponzetto, Simone P. Rosso, Paolo Data and Web Science Group School of Business Informatics and Matemathics University of Mannheim B6 26 MannheimDE-68159 Germany Symanto Research Pretzfelder Strasse 15 NurembergDE-90425 Germany Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camino de Vera s/n ValenciaES-46022 Spain

Recognizing semantically similar sentences or paragraphs across languages is beneficial for many tasks, ranging from cross-lingual information retrieval and plagiarism detection to machine translation. Recently proposed methods for predicting cross-lingual semantic similarity of short texts, however, make use of tools and resources (e.g., machine translation systems, syntactic parsers or named entity recognition) that for many languages (or language pairs) do not exist. In contrast, we propose an unsupervised and a very resource-light approach for measuring semantic similarity between texts in different languages. To operate in the bilingual (or multilingual) space, we project continuous word vectors (i.e., word embeddings) from one language to the vector space of the other language via the linear translation model. We then align words according to the similarity of their vectors in the bilingual embedding space and investigate different unsupervised measures of semantic similarity exploiting bilingual embeddings and word alignments. Requiring only a limited-size set of word translation pairs between the languages, the proposed approach is applicable to virtually any pair of languages for which there exists a sufficiently large corpus, required to learn monolingual word embeddings. Experimental results on three different datasets for measuring semantic textual similarity show that our simple resource-light approach reaches performance close to that of supervised and resource-intensive methods, displaying stability across different language pairs. Furthermore, we evaluate the proposed method on two extrinsic tasks, namely extraction of parallel sentences from comparable corpora and cross-lingual plagiarism detection, and show that it yields performance comparable to those of complex resource-intensive state-of-the-art models for the respective tasks. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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Automatic Semantic Segmentation of the Lumbar Spine: Clinical Applicability in a Multi-parametric and Multi-centre Study on Magnetic Resonance Images

arXiv

引用

arXiv 2021年

作者： Sáenz-Gamboa, Jhon Jairo Domenech, Julio Alonso-Manjarrés, Antonio Gómez, Jon A. de la Iglesia-Vayá, Maria Av. de Catalunya 21 València46020 Spain Orthopedic Surgery Department Hospital Arnau de Vilanova Carrer de San Clemente s/n València46015 Spain Radiology Department Hospital Arnau de Vilanova Carrer de San Clemente s/n València46015 Spain Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camí de Vera s/n València46022 Spain Regional Ministry of Universal Health and Public Health in Valencia Carrer de Misser Mascó 31 València46010 Spain

One of the major difficulties in medical image segmentation is the high variability of these images, which is caused by their origin (multi-centre), the acquisition protocols (multi-parametric), as well as the variability of human anatomy, the severity of the illness, the effect of age and gender, among others. The problem addressed in this work is the automatic semantic segmentation of lumbar spine Magnetic Resonance images using convolutional neural networks. The purpose is to assign a class label to each pixel of an image. Classes were defined by radiologists and correspond to different structural elements like vertebrae, intervertebral discs, nerves, blood vessels, and other tissues. The proposed network topologies are variants of the U-Net architecture. Several complementary blocks were used to define the variants: Three types of convolutional blocks, spatial attention models, deep supervision and multilevel feature extractor. This document describes the topologies and analyses the results of the neural network designs that obtained the most accurate segmentations. Several of the proposed designs outperform the standard U-Net used as baseline, especially when used in ensembles where the output of multiple neural networks is combined according to different *** Codes 92B20, 92C50, 68T07, 68T45, 68U10, 92B10 © 2021, CC BY.

关键词： Convolution

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