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Lecture notes in Computer Science
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Titre : CDPS: Constrained DTW-Preserving Shapelets Type de document : Article/Communication Auteurs : Hussein El Amouri, Auteur ; Thomas Lampert, Auteur ; Pierre Gançarski, Auteur ; Clément Mallet , Auteur
Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2023 Collection : Lecture notes in Computer Science Sous-collection : Lecture Notes in Artificial Intelligence num. 13713 Projets : HIATUS / Giordano, Sébastien Conférence : ECML PKDD 2022, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 19/09/2022 23/09/2022 Grenoble France Proceedings Springer Projets : HERELLES / Gançarski, Pierre Importance : pp 21 - 37 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse de données
[Termes IGN] analyse de groupement
[Termes IGN] classification
[Termes IGN] déformation temporelle dynamique (algorithme)
[Termes IGN] distance euclidienne
[Termes IGN] jeu de données localisées
[Termes IGN] série temporelle
[Termes IGN] traitement de données localisées
[Termes IGN] transformationRésumé : (auteur) The analysis of time series for clustering and classification is becoming ever more popular because of the increasingly ubiquitous nature of IoT, satellite constellations, and handheld and smart-wearable devices, etc. The presence of phase shift, differences in sample duration, and/or compression and dilation of a signal means that Euclidean distance is unsuitable in many cases. As such, several similarity measures specific to time-series have been proposed, Dynamic Time Warping (DTW) being the most popular. Nevertheless, DTW does not respect the axioms of a metric and therefore Learning DTW-Preserving Shapelets (LDPS) have been developed to regain these properties by using the concept of shapelet transform. LDPS learns an unsupervised representation that models DTW distances using Euclidean distance in shapelet space. This article proposes constrained DTW-preserving shapelets (CDPS), in which a limited amount of user knowledge is available in the form of must link and cannot link constraints, to guide the representation such that it better captures the user’s interpretation of the data rather than the algorithm’s bias. Subsequently, any unconstrained algorithm can be applied, e.g. K-means clustering, k-NN classification, etc, to obtain a result that fulfils the constraints (without explicit knowledge of them). Furthermore, this representation is generalisable to out-of-sample data, overcoming the limitations of standard transductive constrained-clustering algorithms. CLDPS is shown to outperform the state-of-the-art constrained-clustering algorithms on multiple time-series datasets. Numéro de notice : C2022-052 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE/INFORMATIQUE/MATHEMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-031-26387-3_2 Date de publication en ligne : 17/03/2023 En ligne : https://doi.org/10.1007/978-3-031-26387-3_2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103157 A benchmark of named entity recognition approaches in historical documents : application to 19th century French directories / Nathalie Abadie (2022)
Titre : A benchmark of named entity recognition approaches in historical documents : application to 19th century French directories Type de document : Article/Communication Auteurs : Nathalie Abadie Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2022 Collection : Lecture notes in Computer Science, ISSN 0302-9743 num. 13237 Projets : SODUCO / Perret, Julien Conférence : DAS 2022, 5th IAPR International Workshop on Document Analysis Systems 22/05/2022 25/05/2022 La Rochelle France Proceedings Springer Importance : pp 445 - 460 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] dix-neuvième siècle
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] exploration de texte
[Termes IGN] objet géohistorique
[Termes IGN] reconnaissance de noms
[Termes IGN] traitement du langage naturelRésumé : (auteur) Named entity recognition (NER) is a necessary step in many pipelines targeting historical documents. Indeed, such natural language processing techniques identify which class each text token belongs to, e.g. “person name”, “location”, “number”. Introducing a new public dataset built from 19th century French directories, we first assess how noisy modern, off-the-shelf OCR are. Then, we compare modern CNN- and Transformer-based NER techniques which can be reasonably used in the context of historical document analysis. We measure their requirements in terms of training data, the effects of OCR noise on their performance, and show how Transformer-based NER can benefit from unsupervised pre-training and supervised fine-tuning on noisy data. Results can be reproduced using resources available at https://github.com/soduco/paper-ner-bench-das22 and https://zenodo.org/record/6394464. Numéro de notice : C2022-030 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-031-06555-2_30 En ligne : http://dx.doi.org/10.1007/978-3-031-06555-2_30 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101088 Combining deep learning and mathematical morphology for historical map segmentation / Yizi Chen (2021)
Titre : Combining deep learning and mathematical morphology for historical map segmentation Type de document : Chapitre/Contribution Auteurs : Yizi Chen Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2021 Collection : Lecture notes in Computer Science, ISSN 0302-9743 num. 12708 Projets : SODUCO / Perret, Julien Conférence : DGMM 2021, 1st International Joint Conference on Discrete Geometry and Mathematical Morphology 24/05/2021 27/05/2021 Uppsala Suède Proceedings Springer Importance : pp 79 - 92 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse diachronique
[Termes IGN] apprentissage profond
[Termes IGN] carte ancienne
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] données maillées
[Termes IGN] morphologie mathématique
[Termes IGN] vectorisationRésumé : (auteur) The digitization of historical maps enables the study of ancient, fragile, unique, and hardly accessible information sources. Main map features can be retrieved and tracked through the time for subsequent thematic analysis. The goal of this work is the vectorization step, i.e., the extraction of vector shapes of the objects of interest from raster images of maps. We are particularly interested in closed shape detection such as buildings, building blocks, gardens, rivers, etc. in order to monitor their temporal evolution. Historical map images present significant pattern recognition challenges. The extraction of closed shapes by using traditional Mathematical Morphology (MM) is highly challenging due to the overlapping of multiple map features and texts. Moreover, state-of-the-art Convolutional Neural Networks (CNN) are perfectly designed for content image filtering but provide no guarantee about closed shape detection. Also, the lack of textural and color information of historical maps makes it hard for CNN to detect shapes that are represented by only their boundaries. Our contribution is a pipeline that combines the strengths of CNN (efficient edge detection and filtering) and MM (guaranteed extraction of closed shapes) in order to achieve such a task. The evaluation of our approach on a public dataset shows its effectiveness for extracting the closed boundaries of objects in historical maps. Numéro de notice : H2021-001 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : 10.1007/978-3-030-76657-3_5 Date de publication en ligne : 16/05/2021 En ligne : https://hal.science/hal-03101578v1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96739 Advances in Intelligent Data Analysis XVIII : 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29 2020 / Michael R. Berthold (2020)
Titre : Advances in Intelligent Data Analysis XVIII : 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29 2020 Type de document : Actes de congrès Auteurs : Michael R. Berthold, Éditeur scientifique ; Ad Feelders, Éditeur scientifique ; Georg Krempl, Éditeur scientifique Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2020 Collection : Lecture notes in Computer Science, ISSN 0302-9743 num. 12080 Conférence : IDA 2020, 18th International Symposium on Intelligent Data Analysis, Advances in Intelligent Data Analysis XVIII 27/04/2020 29/04/2020 Constance Allemagne OA Proceedings Importance : 588 p. ISBN/ISSN/EAN : 978-3-030-44584-3 Note générale : Information Systems and Applications, incl. Internet/Web, and HCI Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] analyse d'image numérique
[Termes IGN] analyse de données
[Termes IGN] appariement d'images
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage dirigé
[Termes IGN] réseau neuronal artificiel
[Termes IGN] vision par ordinateurRésumé : (Editeur) This open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation. Numéro de notice : 26312 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Actes DOI : 10.1007/978-3-030-44584-3 Date de publication en ligne : 14/05/2020 En ligne : https://www.springer.com/gp/book/9783030445836 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95127 Towards interoperable research infrastructures for environmental and earth sciences / Zhiming Zhao (2020)
Titre : Towards interoperable research infrastructures for environmental and earth sciences : a reference model guided approach for common challenges Type de document : Monographie Auteurs : Zhiming Zhao, Éditeur scientifique ; Margareta Hellström, Éditeur scientifique Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2020 Collection : Lecture notes in Computer Science, ISSN 0302-9743 num. 12003 Importance : 373 p. ISBN/ISSN/EAN : 978-3-030-52829-4 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Société de l'information
[Termes IGN] données environnementales
[Termes IGN] géosciences
[Termes IGN] infrastructure
[Termes IGN] instrument de mesure
[Termes IGN] interopérabilité
[Termes IGN] monde virtuel
[Termes IGN] recherche scientifiqueRésumé : (Editeur) This open access book summarises the latest developments on data management in the EU H2020 ENVRIplus project, which brought together more than 20 environmental and Earth science research infrastructures into a single community. It provides readers with a systematic overview of the common challenges faced by research infrastructures and how a ‘reference model guided’ engineering approach can be used to achieve greater interoperability among such infrastructures in the environmental and earth sciences. The 20 contributions in this book are structured in 5 parts on the design, development, deployment, operation and use of research infrastructures. Part one provides an overview of the state of the art of research infrastructure and relevant e-Infrastructure technologies, part two discusses the reference model guided engineering approach, the third part presents the software and tools developed for common data management challenges, the fourth part demonstrates the software via several use cases, and the last part discusses the sustainability and future directions. Note de contenu : 1--Data Management in Environmental and Earth Sciences
- Supporting Cross-Domain System-Level Environmental and Earth Science / Alex Vermeulen, Helen Glaves, Sylvie Pouliquen, and Alexandra Kokkinaki
- ICT Infrastructures for Environmental and Earth Sciences / Keith Jeffery, Antti Pursula, and Zhiming Zhao
- Common Challenges and Requirements / Barbara Magagna, Paul Martin, Abraham Nieva de la Hidalga, Malcolm Atkinson, and Zhiming Zhao
2--Reference Model Guided System Design and Development
- The ENVRI Reference Model / Abraham Nieva de la Hidalga, Alex Hardisty, Paul Martin, Barbara Magagna, and Zhiming Zhao
- Reference Model Guided Engineering / Zhiming Zhao and Keith Jeffery
- Semantic and Knowledge Engineering Using ENVRI RM / Paul Martin, Xiaofeng Liao, Barbara Magagna, Markus Stocker, and Zhiming Zhao
3--Common Data Management Services in Environmental RIs
- Data Curation and Preservation / Keith Jeffery
- Data Cataloguing / Erwann Quimbert, Keith Jeffery, Claudia Martens, Paul Martin, and Zhiming Zhao
- Identification and Citation of Digital Research Resources / Margareta Hellström, Maria Johnsson, and Alex Vermeulen
- Data Processing and Analytics for Data-Centric Sciences / Leonardo Candela, Gianpaolo Coro, Lucio Lelii, Giancarlo Panichi, and Pasquale Pagano
- Virtual Infrastructure Optimisation / Spiros Koulouzis, Paul Martin, and Zhiming Zhao
- Data Provenance / Barbara Magagna, Doron Goldfarb, Paul Martin, Malcolm Atkinson, Spiros Koulouzis, and Zhiming Zhao
- Semantic Linking of Research Infrastructure Metadata / Paul Martin, Barbara Magagna, Xiaofeng Liao, and Zhiming Zhao
- Authentication, Authorization, and Accounting / Alessandro Paolini, Diego Scardaci, Nicolas Liampotis, Vincenzo Spinoso, Baptiste Grenier, and Yin Chen
- Virtual Research Environments for Environmental and Earth Sciences : Approaches and Experiences / Keith Jeffery, Leonardo Candela, and Helen Glaves
4--Case Studies
- Case Study: Data Subscriptions Using Elastic Cloud Services / Spiros Koulouzis, Thierry Carval, Jani Heikkinen, Antti Pursula and Zhiming Zhao
- Case Study: ENVRI Science Demonstrators with D4Science / Leonardo Candela, Markus Stocker, Ingemar Häggström, Carl-Fredrik Enell, Domenico Vitale, Dario Papale, Baptiste Grenier, Yin Chen, and Matthias Obst
- Case Study: LifeWatch Italy Phytoplankton VRE / Elena Stanca, Nicola Fiore, Ilaria Rosati, Lucia Vaira, Francesco Cozzoli, and Alberto Basset
5--Sustainability and Future Challenges
- Towards Cooperative Sustainability / Wouter Los
- Towards Operational Research Infrastructures with FAIR : Data and Services / Zhiming Zhao, Keith Jeffery, Markus Stocker, Malcolm Atkinson, and Andreas PetzoldNuméro de notice : 26497 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Recueil / ouvrage collectif DOI : 10.1007/978-3-030-52829-4 En ligne : https://doi.org/10.1007/978-3-030-52829-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96989 
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