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25th International Conference on pattern recognition, ICPR 2020

作者： Saenz-Gamboa, Jhon Jairo de la Iglesia-Vayá, Maria Gómez, Jon A. Biomedical Imaging Joint Unit Foundation for the Promotion of Health and Biomedical Research FISABIO-CIPF València Spain Pattern Recognition and Human Language Technology research center Universitat Politècnica de València València Spain

ISBN: (纸本)9781728188089

This work addresses the problem of automatically segmenting the MR images corresponding to the lumbar spine. The purpose is to detect and delimit the different structural elements like vertebrae, intervertebral discs, nerves, blood vessels, etc. This task is known as semantic segmentation. The approach proposed in this work is based on convolutional neural networks whose output is a mask where each pixel from the input image is classified into one of the possible classes. Classes were defined by radiologists and correspond to structural elements and tissues. The proposed network architectures are variants of the U-Net. Several complementary blocks were used to define the variants: spatial attention models, deep supervision and multi-kernels at input, this last block type is based on the idea of inception. Those architectures which got the best results are described in this paper, and their results are discussed. Two of the proposed architectures outperform the standard U-Net used as baseline. © 2020 IEEE

关键词： Semantic Segmentation

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The Varied Impacts of Energy Storage and Photovoltaics on Fossil Fuel Emissions

The Varied Impacts of Energy Storage and Photovoltaics on Fo...

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IEEE Power and Energy Society General Meeting

作者： Emily Barrett Brandon Thayer Karen Studarus Seemita Pal Pattern Recognition and Human Language Technology Research Center Universitat Politecnica de Valencia Valencia Spain

ISBN: (纸本)9781538622131;9781538622124

The emissions consequences of smart grid technologies can be significant but are not always intuitive. This is particularly true in the implementation of energy storage (ES) to enable the installation of solar photovoltaic (PV) systems. Using the web-based calculator at and prototypical distribution feeders, this paper explores the CO_2, SO_2 and NO_x impacts of ES deployed with solar PV, where the energy storage system is operated to minimize load variation assuming hourly dispatch. Five regions of the country were explored using 15 prototypical distribution feeders and 2015 historical data. Impacts vary in direction, magnitude, and trend, and require a context-dependent screening method for faithful representation.

关键词： Meteorology Load modeling Fossil fuels Energy storage Smart grids Data models Photovoltaic systems

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Conspiracy vs Critical Thinking Using an Ensemble of Transformers with Data Augmentation Techniques 25

Conspiracy vs Critical Thinking Using an Ensemble of Transfo...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Tulbure, Angelo Maximilian Ardanuy, Mariona Coll Universitat Politècnica de València València Spain Politecnico di Milano Milan Italy Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

This paper provides an overview of our contributions to the PAN at CLEF2024 Oppositional thinking analysis shared task, which focuses on distinguishing between conspiratorial and critical thinking narratives. The competition featured two main tasks. The first task is a binary classification task that aims at determining whether a text is conspiratorial or critical. The second task is a span-level detection task, in which the goal is to detect elements of oppositional narratives in the texts. Two annotated datasets, one in English and one in Spanish, were provided, each of 5K telegram comments. Our best-performing approaches combined custom fine-tuned Transformer models with data augmentation techniques. We achieved an F1-Score of 0.8917 for English and of 0.8293 for Spanish for task 1, and a span-F1 score of 0.6279 for English and 0.6129 for Spanish for task 2. Our task 2 approach achieved the best results in the shared task for both English and Spanish. © 2024 Copyright for this paper by its authors.

关键词： binary classification conspiracy theories critical thinking data augmentation ensembling models oppositional thinking PAN 2024 token classification

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Sign language Gesture Classification Using Neural Networks 4

Sign Language Gesture Classification Using Neural Networks

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4th International Conference on Advances in Speech and language Technologies for Iberian languages, IberSPEECH 2018

作者： Parcheta, Zuzanna Martínez-Hinarejos, Carlos-D. Sciling S.L. Carrer del Riu 321 Pinedo 46012 Spain Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camino de Vera s/n 46022 Spain

Recent studies have demonstrated the power of neural networks for different fields of artificial intelligence. In most fields, such as machine translation or speech recognition, neural networks outperform previously used methods (Hidden Markov Models with Gaussian Mixtures, Statistical Machine Translation, etc.). In this paper, the efficiency of the LeNet convolutional neural network for isolated word sign language recognition is demonstrated. As a preprocessing step, we apply several techniques to obtain the same dimension for the input that contains gesture information. The performance of these preprocessing techniques on a Spanish Sign language dataset is evaluated. These approaches outperform previously obtained results based on Hidden Markov Models. © 4th International Conference, IberSPEECH 2018.

关键词： Gesture recognition

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Py4MER: A CTC-Based Mathematical Expression recognition System 11th

Py4MER: A CTC-Based Mathematical Expression Recognition Syst...

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11th Iberian Conference on pattern recognition and Image Analysis, IbPRIA 2023

作者： Anitei, Dan Sánchez, Joan Andreu Benedí, José Miguel Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica València Valencia Spain Valencian Graduate School and Research Network of Artificial Intelligence Camí de Vera s/n Valencia46022 Spain

ISBN: (纸本)9783031366154

Mathematical expression recognition is a research field that aims to develop algorithms and systems capable of interpreting mathematical content. The recognition of MEs requires handling two-dimensional symbol relationships such as sub/superscripts, matrices and nested fractions, among others. The prevalent technology for addressing these challenges are based on encoder-decoder architectures with attention models. In this paper we propose the Py4MER system, based on Convolutional Recurrent Neural Network (CRNN) models, for the recognition and transcription of MEs into LaTeX mark-up sequences. This model is proposed as an alternative to encoder-decoder approaches, as CRNN models trained through Connectionist Temporal Classification (CTC) implicitly model the dependencies between symbols and do not suffer from under/over parsing of the input image, generating more consistent mark-up. The proposed model is evaluated on the Im2Latex-100k data set based on both textual and image-level metrics, showing a remarkable improvement from other CTC-based approaches. recognition results are analyzed for different ME lengths and ME structures. Furthermore, a study based on the edit distance is performed, showing a considerable improvement in precision when up to 5 edit operations are considered. Finally, we show that CTC-based CRNN models can adapt to non left-to-right ordering of ME elements, warranting more research for this approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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How Much Does Tokenization Affect Neural Machine Translation? 1

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20th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2019

作者： Domingo, Miguel García-Martínez, Mercedes Helle, Alexandre Casacuberta, Francisco Herranz, Manuel Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camino de Vera s/n Valencia46022 Spain Pangeanic/B.I Europa PangeaMT Technologies Division Valencia Spain

ISBN: (数字)9783031243370

ISBN: (纸本)9783031243363

Tokenization or segmentation is a wide concept that covers simple processes such as separating punctuation from words, or more sophisticated processes such as applying morphological knowledge. Neural Machine Translation (NMT) requires a limited-size vocabulary for computational cost and enough examples to estimate word embeddings. Separating punctuation and splitting tokens into words or subwords has proven to be helpful to reduce vocabulary and increase the number of examples of each word, improving the translation quality. Tokenization is more challenging when dealing with languages with no separator between words. In order to assess the impact of the tokenization in the quality of the final translation on NMT, we experimented on five tokenizers over ten language pairs. We reached the conclusion that the tokenization significantly affects the final translation quality and that the best tokenizer differs for different language pairs. © 2023, Springer Nature Switzerland AG.

关键词： Neural machine translation

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Combining handwriting and speech recognition for transcribing historical handwritten documents

Combining handwriting and speech recognition for transcribin...

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International Conference on Document Analysis and recognition

作者： Emilio Granell Carlos-D. Martínez-Hinarejos Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Valencia Spain

Transcription of historical documents is an interesting task for libraries in order to make available their funds. In the lasts years, the use of Handwritten Text recognition allowed paleographs to speed up the manual transcription process, since they are able to correct on a draft transcription. Another alternative is obtaining the draft transcription by dictating the contents to an Automatic Speech recognition system. When both sources (image and speech) are available, a multimodal combination is possible, and an iterative process can be used in order to refine the final hypothesis. In this work, a multimodal combination based on confusion networks is presented. Results on two different sets of data, with different difficulty level, show that the proposed technique provides similar or better draft transcriptions than a previously proposed approach, allowing for a faster transcription process.

关键词： Iterative decoding Acoustics Proposals Laplace equations Integrated optics Optical imaging

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ICFHR2014 Competition on Handwritten Text recognition on Transcriptorium Datasets (HTRtS)

ICFHR2014 Competition on Handwritten Text Recognition on Tra...

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International Workshop on Frontiers in Handwriting recognition

作者： Joan Andreu Sánchez Verónica Romero Alejandro H. Toselli Enrique Vidal Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

A contest on Handwritten Text recognition organised in the context of the ICFHR 2014 conference is described. Two tracks with increased freedom on the use of training data were proposed and three research groups participated in these two tracks. The handwritten images for this contest were drawn from an English data set which is currently being considered in the Tran scriptorium project. The goal of this project is to develop innovative, efficient and cost-effective solutions for the transcription of historical handwritten document images, focusing on four languages: English, Spanish, German and Dutch. For the English language, the so-called "Bentham collection" is being considered in Tran scriptorium. It encompasses a large set of manuscripts written by the renowned English philosopher and reformer Jeremy Bentham (1748-1832). A small subset of this collection has been chosen for the present HTR competition. The selected subset has been written by several hands (Bentham himself and his secretaries) and entails significant variabilities and difficulties regarding the quality of text images and writing styles. Training and test data were provided in the form of carefully segmented line images, along with the corresponding transcripts. The three participants achieved very good results, with transcription word error rates ranging from 15.0% down to 8.6%.

关键词： Training Hidden Markov models Histograms Artificial neural networks Text recognition Training data Adaptive optics

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Active learning for interactive neural machine translation of data streams

arXiv

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arXiv 2018年

作者： Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

We study the application of active learning techniques to the translation of unbounded data streams via interactive neural machine translation. The main idea is to select, from an unbounded stream of source sentences, those worth to be supervised by a human agent. The user will interactively translate those samples. Once validated, these data is useful for adapting the neural machine translation model. We propose two novel methods for selecting the samples to be validated. We exploit the information from the attention mechanism of a neural machine translation system. Our experiments show that the inclusion of active learning techniques into this pipeline allows to reduce the effort required during the process, while increasing the quality of the translation system. Moreover, it enables to balance the human effort required for achieving a certain translation quality. Moreover, our neural system outperforms classical approaches by a large margin. Copyright © 2018, The Authors. All rights reserved.

关键词： Neural machine translation

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A neural, interactive-predictive system for multimodal sequence to sequence tasks

arXiv

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arXiv 2019年

作者： Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

We present a demonstration of a neural interactive-predictive system for tackling multimodal sequence to sequence tasks. The system generates text predictions to different sequence to sequence tasks: machine translation, image and video captioning. These predictions are revised by a human agent, who introduces corrections in the form of characters. The system reacts to each correction, providing alternative hypotheses, compelling with the feedback provided by the user. The final objective is to reduce the human effort required during this correction process. This system is implemented following a client–server architecture. For accessing the system, we developed a website, which communicates with the neural model, hosted in a local server. From this website, the different tasks can be tackled following the interactive-predictive framework. We open-source all the code developed for building this system. The demonstration in hosted in http://***/ interactive-seq2seq. Copyright © 2019, The Authors. All rights reserved.

关键词： Websites

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