LEADER 02880 am 2200637 n 450 001 9910283542003321 005 20180326 010 $a979-1-02-401043-4 024 7 $a10.4000/books.purh.3157 035 $a(CKB)4100000005959183 035 $a(FrMaCLE)OB-purh-3157 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/59347 035 $a(PPN)241291054 035 $a(EXLCZ)994100000005959183 100 $a20180828j|||||||| ||| 0 101 0 $afre 135 $auu||||||m|||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 13$aUn siècle de journalisme culturel en Normandie et dans d'autres provinces $e1785-1885 /$fCatriona Seth, Éric Wauters 210 $aMont-Saint-Aignan $cPresses universitaires de Rouen et du Havre$d2018 215 $a1 online resource (202 p.) 311 $a2-87775-522-3 330 $aEn onze contributions, ce recueil analyse les pratiques du journalisme culturel dans la province française, au cours du siècle qui suit la création en 1785 du Journal de Rouen. S'inscrivant dans une tradition encyclopédique et dominée par une activité théâtrale souvent intense, la chronique de l'actualité culturelle cherche à promouvoir, à travers des auteurs, des ?uvres ou des événements, la production intellectuelle locale. Mais elle constitue aussi, sans spécialisation rédactionnelle affirmée et avant la loi de 1881 sur la liberté de la presse, un moyen d'échapper aux tentatives du pouvoir d'instrumentaliser la culture et de faire passer, malgré la censure, des idées nouvelles et une critique sociale ou politique sous couvert de gazette des spectacles ou d'articles d'érudition. 606 $aHistory 606 $aLibrary, Information & Communication sciences 606 $aculture 606 $athéâtre 606 $ajournalisme 610 $ajournalisme 610 $aculture 610 $athéâtre 615 4$aHistory 615 4$aLibrary, Information & Communication sciences 615 4$aculture 615 4$athéâtre 615 4$ajournalisme 700 $aBazire$b Laure$01322107 701 $aBourdin$b Philippe$0222996 701 $aCandaux$b Jean-Daniel$0427530 701 $aÉlart$b Joann$01322108 701 $aHaffemayer$b Stéphane$01317017 701 $aLe Guillou$b Claire$01322109 701 $aRieul$b Inès$01322110 701 $aSeth$b Catriona$01285844 701 $aSibout$b Cécile-Anne$01293803 701 $aTriolaire$b Cyril$01298447 701 $aWauters$b Éric$01285971 701 $aSeth$b Catriona$01285844 701 $aWauters$b Éric$01285971 801 0$bFR-FrMaCLE 906 $aBOOK 912 $a9910283542003321 996 $aUn siècle de journalisme culturel en Normandie et dans d'autres provinces$93034700 997 $aUNINA LEADER 06035nam 22006735 450 001 9910409672303321 005 20251225181935.0 010 $a3-030-45778-8 024 7 $a10.1007/978-3-030-45778-5 035 $a(CKB)4100000011223252 035 $a(MiAaPQ)EBC6179529 035 $a(DE-He213)978-3-030-45778-5 035 $a(PPN)243761147 035 $a(EXLCZ)994100000011223252 100 $a20200419d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for Networking $eSecond IFIP TC 6 International Conference, MLN 2019, Paris, France, December 3?5, 2019, Revised Selected Papers /$fedited by Selma Boumerdassi, Éric Renault, Paul Mühlethaler 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (498 pages) $cillustrations 225 1 $aInformation Systems and Applications, incl. Internet/Web, and HCI,$x2946-1642 ;$v12081 311 08$a3-030-45777-X 327 $aNetwork Anomaly Detection using Federated Deep Autoencoding Gaussian Mixture Model -- Towards a Hierarchical Deep Learning Approach for Intrusion Detection -- Network Trafic Classifi cation using Machine Learning for Software Defined Networks -- A Comprehensive Analysis of Accuracies of Machine Learning Algorithms for Network Intrusion Detection -- Q-routing: from the algorithm to the routing protocol -- Language Model Co-occurrence Linking for Interleaved Activity Discovery -- Achieving Proportional Fairness in WiFi Networks via Bandit Convex Optimization -- Denoising Adversarial Autoencoder for Obfuscated Tra c Detection and Recovery -- Root Cause Analysis of Reduced Accessibility in 4G Networks -- Space-time pattern extraction in alarm logs for network diagnosis -- Machine Learning Methods for Connection RTT and Loss Rate Estimation Using MPI Measurements Under Random Losses -- Algorithm Selection and Model Evaluation in Application Design using Machine Learning -- GAMPAL: Anomaly Detection forInternet Backbone Tra c by Flow Prediction with LSTM-RNN -- Revealing User Behavior by Analyzing DNS Tra c -- A new approach to determine the optimal number of clusters based on the Gap statistic -- MLP4NIDS: an e cient MLP-based Network Intrusion Detection for CICIDS2017 dataset -- Random Forests with a Steepend Gini-Index Split Function and Feature Coherence Injection -- Emotion-based Adaptive Learning Systems -- Machine learning methods for anomaly detection in IoT networks, with illustrations -- DeepRoute: Herding Elephant and Mice Flows with Reinforcement Learning -- Arguments Against using the 1998 DARPA Dataset for Cloud IDS Design and Evaluation and Some Alternative -- Estimation of the Hidden Message Length in Steganography: A Deep Learning Approach -- An Adaptive Deep Learning Algorithm Based Autoencoder for Interference Channels -- A Learning Approach for Road Tra c Optimization in Urban Environments -- CSI based Indoor localization using Ensemble Neural Networks -- Bayesian Classi ersin Intrusion Detection Systems -- A Novel Approach towards Analysis of Attacker Behavior in DDoS Attacks -- Jason-RS, a Collaboration between Agents and an IoT Platform -- Scream to Survive(S2S): Intelligent System to Life-Saving in Disasters Relief -- Association Rules Algorithms for Data Mining Process Based on Multi Agent System -- Internet of Things: Security Between Challenges and Attacks -- Socially and biologically inspired computing for self-organizing communications networks. . 330 $aThis book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019. The 26 revised full papers included in the volume were carefully reviewed and selected from 75 submissions. They present and discuss new trends in deep and reinforcement learning, patternrecognition and classi cation for networks, machine learning for network slicingoptimization, 5G system, user behavior prediction, multimedia, IoT, securityand protection, optimization and new innovative machine learning methods, performanceanalysis of machine learning algorithms, experimental evaluations ofmachine learning, data mining in heterogeneous networks, distributed and decentralizedmachine learning algorithms, intelligent cloud-support communications,ressource allocation, energy-aware communications, software de ned networks,cooperative networks, positioning and navigation systems, wireless communications,wireless sensor networks, underwater sensor networks. 410 0$aInformation Systems and Applications, incl. Internet/Web, and HCI,$x2946-1642 ;$v12081 606 $aData mining 606 $aComputer engineering 606 $aComputer networks 606 $aApplication software 606 $aData protection 606 $aData Mining and Knowledge Discovery 606 $aComputer Engineering and Networks 606 $aComputer and Information Systems Applications 606 $aData and Information Security 615 0$aData mining. 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aApplication software. 615 0$aData protection. 615 14$aData Mining and Knowledge Discovery. 615 24$aComputer Engineering and Networks. 615 24$aComputer and Information Systems Applications. 615 24$aData and Information Security. 676 $a006.31 676 $a004.6 702 $aBoumerdassi$b Selma$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRenault$b Éric$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMühlethaler$b Paul$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910409672303321 996 $aMachine learning for networking$91906176 997 $aUNINA