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Refondre les politiques publiques avec le numérique / Gilles Babinet (2020)
Titre : Refondre les politiques publiques avec le numérique : administration territoriale, Etat, citoyens Type de document : Monographie Auteurs : Gilles Babinet, Auteur Editeur : Paris : Dunod Année de publication : 2020 Collection : Les beaux livres du savoir Importance : 240 p. Format : 16 x 22 cm ISBN/ISSN/EAN : 978-2-10-082076-4 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Administration
[Termes IGN] collectivité territoriale
[Termes IGN] document numérique
[Termes IGN] données massives
[Termes IGN] données ouvertes
[Termes IGN] évolution technologique
[Termes IGN] France (administrative)
[Termes IGN] intelligence artificielle
[Termes IGN] politique publique
[Termes IGN] sécurité informatique
[Termes IGN] support numérique
[Termes IGN] Union EuropéenneIndex. décimale : 20.00 Administration - généralités Résumé : (Auteur) 5G, démarches administratives en ligne, formation au numérique... la transformation digitale occupe une place prépondérante en France. Pourtant, l'Etat, les administrations territoriales et hospitalières rencontrent d'importantes difficultés pour mettre en oeuvre des services numériques. Les réformes se heurtent à des niveaux hétérogènes de compétences des administrations, à une capacité programmatique limitée des acteurs politiques et hauts fonctionnaires, et plus encore à une forte dissonance culturelle entre le monde de l'administration publique et celui du numérique. Mais si ces cultures sont différentes, elles ne sont pas incompatibles. Afin d'accélérer la transformation digitale au sein des institutions publiques et mettre en place les processus nécessaires pour une bonne transition, ce livre, riche en exemples et mises en situations, vulgarise le numérique et propose des outils concrets. Il développe une culture numérique générale, avant de présenter les technologies qui sont au coeur de la révolution digitale. Plus concrètement, il décrypte l'administration numérique des territoires français, et permet de comprendre les enjeux essentiels et nécessaires du management de la transformation (change management). Note de contenu : Introduction - Numérique pour l'Etat et numérique pour les territoires
1. Introduction au numérique
- Histoire de la révolution digitale
- Le management digital
- La souveraineté numérique
- Focus Estonie
- Le numérique dans l'Union européenne
- RGPD
- Le numérique dans la fonction publique française
2. Outils et technologies
- Technologie : notions clés
- Big data et intelligence artificielle
- Blockchain
- Cybersécurité
- Open Source
- Open Data
3. Administration publique et enjeux des territoires
3.1 Administration numérique française
- Numérique pour l'Etat et numérique pour les territoires
- DCANT
- La santé numérique dans le territoire
- Focus : l'identité numérique
3.2 Territoire français : service essentiel face au numérique
- Efficacité énergétique
- Environnement et agriculture
- Fracture et numérique
- Formation et transformation
- Accès et déploiement des réseaux
- Statistique publique et big data
- Transport, logistique, économie circulaire et territoires
4. Change management
- Gouvernance, vision et pensée design
- Top management et gouvernance
- Gestion du capital humain
- Exemple de workshopNuméro de notice : 26437 Affiliation des auteurs : non IGN Thématique : SOCIETE NUMERIQUE Nature : Monographie Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96370 Réservation
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Titre : Representation learning for natural language processing Type de document : Monographie Auteurs : Zhiyuan Liu, Éditeur scientifique ; Yankai Lin, Éditeur scientifique ; Maosong Sun, Éditeur scientifique Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2020 Importance : 334 p. ISBN/ISSN/EAN : 978-981-1555732-- Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] données massives
[Termes IGN] exploration de données
[Termes IGN] représentation des connaissances
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau sémantique
[Termes IGN] traitement du langage naturelRésumé : (Editeur) This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing. Note de contenu :
1. Representation Learning and NLP
1.1 Motivation
1.2 Why Representation Learning Is Important for NLP
1.3 Basic Ideas of Representation Learning
1.4 Development of Representation Learning for NLP
1.5 Learning Approaches to Representation Learning for NLP
1.6 Applications of Representation Learning for NLP
1.7 The Organization of This Book
2. Word Representation
2.1 Introduction
2.2 One-Hot Word Representation
2.3 Distributed Word Representation
2.4 Contextualized Word Representation
2.5 Extensions
2.6 Evaluation
3. Compositional Semantics
3.1 Introduction
3.2 Semantic Space
3.3 Binary Composition
3.4 N-Ary Composition
4. Sentence Representation
4.1 Introduction
4.2 One-Hot Sentence Representation
4.3 Probabilistic Language Model
4.4 Neural Language Model
4.5 Applications
5. Document Representation
5.1 Introduction
5.2 One-Hot Document Representation
5.3 Topic Model
5.4 Distributed Document Representation
5.5 Applications
6. Sememe Knowledge Representation
6.1 Introduction
6.2 Sememe Knowledge Representation
6.3 Applications
7. World Knowledge Representation
7.1 Introduction
7.2 Knowledge Graph Representation
7.3 Multisource Knowledge Graph Representation
7.4 Applications
8. Network Representation
8.1 Introduction
8.2 Network Representation
8.3 Graph Neural Networks
9. Cross-Modal Representation
9.1 Introduction
9.2 Cross-Modal Representation
9.3 Image Captioning
9.4 Visual Relationship Detection
9.5 Visual Question Answering
10. Resources
10.1 Open-Source Frameworks for Deep Learning
10.2 Open Resources for Word Representation
10.3 Open Resources for Knowledge Graph Representation
10.4 Open Resources for Network Representation
10.5 Open Resources for Relation Extraction
11. OutlookNuméro de notice : 26515 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.1007/978-981-15-5573-2 En ligne : http://doi.org/10.1007/978-981-15-5573-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97296 Security risk management for the Internet of things: Technologies and techniques for IoT security, privacy and data protection / John Soldatos (2020)
Titre : Security risk management for the Internet of things: Technologies and techniques for IoT security, privacy and data protection Type de document : Monographie Auteurs : John Soldatos, Éditeur scientifique Editeur : Boston, Delft : Now publishers Année de publication : 2020 Importance : 250 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-68083-682-0 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Société de l'information
[Termes IGN] attaque informatique
[Termes IGN] cyberinfrastructure
[Termes IGN] données massives
[Termes IGN] données numériques
[Termes IGN] internet des objets
[Termes IGN] sécurité informatiqueRésumé : (éditeur) In recent years, the rising complexity of Internet of Things (IoT) systems has increased their potential vulnerabilities and introduced new cybersecurity challenges. In this context, state of the art methods and technologies for security risk assessment have prominent limitations when it comes to large scale, cyber-physical and interconnected IoT systems. Risk assessments for modern IoT systems must be frequent, dynamic and driven by knowledge about both cyber and physical assets. Furthermore, they should be more proactive, more automated, and able to leverage information shared across IoT value chains. This book introduces a set of novel risk assessment techniques and their role in the IoT Security risk management process. Specifically, it presents architectures and platforms for end-to-end security, including their implementation based on the edge/fog computing paradigm. It also highlights machine learning techniques that boost the automation and proactiveness of IoT security risk assessments. Furthermore, blockchain solutions for open and transparent sharing of IoT security information across the supply chain are introduced. Frameworks for privacy awareness, along with technical measures that enable privacy risk assessment and boost GDPR compliance are also presented. Likewise, the book illustrates novel solutions for security certification of IoT systems, along with techniques for IoT security interoperability. In the coming years, IoT security will be a challenging, yet very exciting journey for IoT stakeholders, including security experts, consultants, security research organizations and IoT solution providers. The book provides knowledge and insights about where we stand on this journey. It also attempts to develop a vision for the future and to help readers start their IoT Security efforts on the right foot. Note de contenu : 1- Introduction
2- Security Data Modelling for Configurable Risk Assessment as a Service in IoT Systems
3- Data-driven IoT Security Using Deep Learning Techniques
4- Privacy Awareness, Risk Assessment, and Control Measures in IoT Platforms: BRAIN-IoT Approach
5- IoT Network Risk Assessment and Mitigation: The SerIoT Approach
6- Chariot-integrated Approach to Safety, Privacy, and Security – CHARIOT IPSE
7- Pattern-driven Security, Privacy, Dependability and Interoperability in IoT
8- Enabling Continuous Privacy Risk Management in IoT Systems
9- Data Protection Compliance Requirements for the Internet of Things
10- Cybersecurity Certification in IoT Environments
11- Firmware Software Analysis at Source Code and Binary Levels
12- End-to-End Security for IoT
13- Blockchain Ledger Solution Affirming Physical, Operational, and Functional Changes in an IoT System
14- Leveraging Interledger Technologies in IoT Security Risk ManagementNuméro de notice : 25979 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/SOCIETE NUMERIQUE Nature : Monographie DOI : 10.1561/9781680836837 En ligne : http://dx.doi.org/10.1561/9781680836837 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96707
Titre : Social Media and Machine Learning Type de document : Monographie Auteurs : Alberto Cano, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 96 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-83880-616-3 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] données massives
[Termes IGN] exploration de texte
[Termes IGN] langage naturel (informatique)
[Termes IGN] réseau social
[Termes IGN] sentimentRésumé : (éditeur) Social media has transformed society and the way people interact with each other. The volume and speed in which new content is being generated surpasses the processing capacity of machine learning systems. Analyzing such data demands new approaches coming from natural language processing, text mining, sentiment analysis, etc to understand and resolve the arising challenges. There is a need to develop robust and adaptable systems to tackle these open issues in real time, as well as to provide a meaningful summarization and visualization to the end users. This book provides the reader with a comprehensive overview of the latest developments in social media and machine learning, addressing research innovations, applications, trends, and open challenges in this crucial area. Note de contenu : 1- Introductory chapter: Data streams and online learning in social media
2- Automatic speech emotion recognition using machine learning
3- A case study of using big data processing in education: Method of matching members by optimizing collaborative
learning environment
4- Literature review on big data analytics methods
5- Information and communication based collaborative learning and behavior modeling using machine learning algorithmNuméro de notice : 28481 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/SOCIETE NUMERIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.78089 En ligne : https://doi.org/10.5772/intechopen.78089 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99165
Titre : Spatial big data, BIM and advanced GIS for smart transformation Type de document : Monographie Auteurs : Sara Shirowzhan, Éditeur scientifique ; Willie Tan, Éditeur scientifique ; Samad R.E. Sepasgozar, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 166 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03936-031-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] classification barycentrique
[Termes IGN] cycliste
[Termes IGN] données massives
[Termes IGN] modèle orienté agent
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] optimisation (mathématiques)
[Termes IGN] planification urbaine
[Termes IGN] réseau ferroviaire
[Termes IGN] réseau routier
[Termes IGN] secours d'urgence
[Termes IGN] système d'information géographique
[Termes IGN] téléphone intelligent
[Termes IGN] trafic routier
[Termes IGN] ville intelligenteRésumé : (éditeur) This book covers a range of topics including selective technologies and algorithms that can potentially contribute to developing an intelligent environment and smarter cities. While the connectivity and efficiency of smart cities is important, the analysis of the impact of construction development and large projects in the city is crucial to decision and policy makers, before the project is approved. This book also presents an agenda for future investigations to address the need for advanced tools such as mobile scanners, Geospatial Artificial Intelligence, Unmanned Aerial Vehicles, Geospatial Augmented Reality apps, Light Detection, and Ranging in smart cities. Some of selected specific tools presented in this book are as a simulator for improving the smart parking practices by modelling drivers with activity plans, a bike optimization algorithm to increase the efficiency of bike stations, an agent-based model simulation of human mobility with the use of mobile phone datasets. In addition, this book describes the use of numerical methods to match the network demand and supply of bicycles, investigate the distribution of railways using different indicators, presents a novel algorithm of direction-aware continuous moving K-nearest neighbor queries in road networks, and presents an efficient staged evacuation planning algorithm for multi-exit buildings. Note de contenu : 1- Digital twin and cyberGIS for improving connectivity and measuring the impact of infrastructure construction planning in smart cities
2- An efficient staged evacuation planning algorithm applied to multi-exit buildings
3- A hybrid framework for high-performance modeling of three-dimensional pipe networks
4- Direction-aware continuous moving K-nearest-neighbor query in road networks
5- The distribution pattern of the railway network in China at the county level
6- Data-driven bicycle network analysis based on traditional counting methods and GPS traces from smartphone
7- An agent-based model simulation of human mobility based on mobile phone data: How commuting relates to congestion
8- Heuristic bike optimization algorithm to improve usage efficiency of the station-free bike sharing system in Shenzhen, China
9- An occupancy simulator for a smart parking system: Developmental design and experimental considerationsNuméro de notice : 28440 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03936-031-4 En ligne : https://doi.org/10.3390/books978-3-03936-031-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98877 SUMAC'20 : Proceedings of the 2nd Workshop on Structuring and Understanding of Multimedia heritAge Contents / Valérie Gouet-Brunet (2020)PermalinkUnderstanding demographic and socioeconomic biases of geotagged Twitter users at the county level / Jiang Juqin in Cartography and Geographic Information Science, vol 46 n° 3 (May 2019)PermalinkiTowns, le nouveau moteur de visualisation 3D de données géospatiales du Géoportail / Mirela Konini in Responsabilité et environnement, n° 94 (Avril 2019)PermalinkGeographic space as a living structure for predicting human activities using big data / Bin Jiang in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)PermalinkA methodology with a distributed algorithm for large-scale trajectory distribution prediction / QiuLei Guo in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)Permalink(re)Considering Bertin in the age of big data and visual analytics / Alan M. MacEachren in Cartography and Geographic Information Science, vol 46 n° 2 (March 2019)PermalinkPermalinkPermalinkNumérique et territoires / Philippe Cohard (2019)PermalinkSpatial data management in apache spark: the GeoSpark perspective and beyond / Jia Yu in Geoinformatica, vol 23 n° 1 (January 2019)Permalink