LEADER 02317nlm 2200277 a 450 001 996402947703316 005 20210222075820.0 010 $a0-521-85849-6 100 $a20090303d2011---- uy 0 101 0 $aeng 102 $aUS 135 $adrcnu 200 1 $aAgrarian reform in Russia$ethe road from serfdom$fCarol S. Leonard 210 1 $aCambridge$aNew York$cCambridge University Press$d2011 215 $aTesto elettronico (PDF) (XIV, 402 p. : ill.) 230 $aBase dati testuale 330 8 $aQuesto testo sulla Russia esamina la storia delle riforme e degli interventi statali che interessano l'agricoltura. Esamina il processo di riforma in una crisi e analizza gli effetti di breve periodo delle avversità economiche, esamina la storia delle riforme e degli interventi statali che interessano l'agricoltura, esamina il processo di riforma in una crisi e analizza gli effetti di breve periodo delle avversità economiche. Esamina anche la produzione agricola e le istituzioni rurali nel lungo periodo dal 1861 ad oggi, esamina la storia delle riforme e dei principali interventi statali che hanno interessato l'agricoltura russa: l'abolizione della servitù della gleba nel 1861, le riforme di Stolypin, la NEP, la collettivizzazione, le riforme di Krusciov e infine la privatizzazione delle imprese agricole all'inizio degli anni '90. Mostra uno schema che emerge da un imperativo politico nei regimi imperiale, sovietico e post-sovietico e descrive come queste riforme fossero giustificate in nome dell'interesse nazionale durante gravi crisi: inflazione rapida, sconfitta militare, scioperi di massa, disordini rurali e/o disordini politici. Contiene capitoli sui diritti di proprietà, l'organizzazione rurale e il cambiamento tecnologico. Fornisce un nuovo strumento per misurare la produttività agricola dal 1861 al 1913 e aggiorna queste stime al presente. Questo libro è uno studio delle politiche volte a riorganizzare la produzione rurale e la loro efficacia nel trasformare le istituzioni. 606 0 $aRiforma agraria$xStoria$yRussia$2BNCF 676 $a333.3147 700 1$aLEONARD,$bCarol Scott$f1945-$0252263 801 0$bcba$aIT$bcba$gREICAT 912 $a996402947703316 959 $aEB 969 $aER 996 $aAgrarian reform in Russia$91766848 997 $aUNISA LEADER 07240nam 22005173 450 001 9910816303403321 005 20210901203631.0 010 $a1-5231-4584-6 010 $a1-63081-812-7 035 $a(CKB)4100000011990796 035 $a(MiAaPQ)EBC6683921 035 $a(Au-PeEL)EBL6683921 035 $a(OCoLC)1262373416 035 $a(NjHacI)994100000011990796 035 $a(EXLCZ)994100000011990796 100 $a20210901d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCognitive Electronic Warfare $eAn Artificial Intelligence Approach 205 $a1st ed. 210 1$aNorwood :$cArtech House,$d2021. 210 4$d©2021. 215 $a1 online resource (261 pages) 225 1 $aArtech House electronic warfare library 311 $a1-63081-811-9 320 $aIncludes bibliographical references and index. 327 $aIntro -- Cognitive Electronic Warfare: An Artificial Intelligence Approach -- Contents -- Foreword -- Preface -- 1 Introduction to Cognitive EW -- 1.1 What Makes a Cognitive System? -- 1.2 A Brief Introduction to EW -- 1.3 EW Domain Challenges Viewed from an AI Perspective -- 1.3.1 SA for ES and EW BDA -- 1.3.2 DM for EA, EP, and EBM -- 1.3.3 User Requirements -- 1.3.4 Connection between CR and EW Systems -- 1.3.5 EW System Design Questions -- 1.4 Choices: AI or Traditional? -- 1.5 Reader's Guide -- 1.6 Conclusion -- References -- 2 Objective Function -- 2.1 Observables That Describe the Environment -- 2.1.1 Clustering Environments -- 2.2 Control Parameters to Change Behavior -- 2.3 Metrics to Evaluate Performance -- 2.4 Creating a Utility Function -- 2.5 Utility Function Design Considerations -- 2.6 Conclusion -- References -- 3 ML Primer -- 3.1 Common ML Algorithms -- 3.1.1 SVMs -- 3.1.2 ANNs -- 3.2 Ensemble Methods -- 3.3 Hybrid ML -- 3.4 Open-Set Classification -- 3.5 Generalization and Meta-learning -- 3.6 Algorithmic Trade-Offs -- 3.7 Conclusion -- References -- 4 Electronic Support -- 4.1 Emitter Classification and Characterization -- 4.1.1 Feature Engineering and Behavior Characterization -- 4.1.2 Waveform Classification -- 4.1.3 SEI -- 4.2 Performance Estimation -- 4.3 Multi-Intelligence Data Fusion -- 4.3.1 Data Fusion Approaches -- 4.3.2 Example: 5G Multi-INT Data Fusion for Localization -- 4.3.3 Distributed-Data Fusion -- 4.4 Anomaly Detection -- 4.5 Causal Relationships -- 4.6 Intent Recognition -- 4.6.1 Automatic Target Recognition and Tracking -- 4.7 Conclusion -- References -- 5 EP and EA -- 5.1 Optimization -- 5.1.1 Multi-Objective Optimization -- 5.1.2 Searching Through the Performance Landscape -- 5.1.3 Optimization Metalearning -- 5.2 Scheduling -- 5.3 Anytime Algorithms -- 5.4 Distributed Optimization -- 5.5 Conclusion. 327 $aReferences -- 6 EBM -- 6.1 Planning -- 6.1.1 Planning Basics: Problem Definition, and Search -- 6.1.2 Hierarchical Task Networks -- 6.1.3 Action Uncertainty -- 6.1.4 Information Uncertainty -- 6.1.5 Temporal Planning and Resource Management -- 6.1.6 Multiple Timescales -- 6.2 Game Theory -- 6.3 HMI -- 6.4 Conclusion -- References -- 7 Real-Time In-mission Planning and Learning -- 7.1 Execution Monitoring -- 7.1.1 EW BDA -- 7.2 In-Mission Replanning -- 7.3 In-Mission Learning -- 7.3.1 Cognitive Architectures -- 7.3.2 Neural Networks -- 7.3.3 SVMs -- 7.3.4 Multiarmed Bandi -- 7.3.5 MDPs -- 7.3.6 Deep Q-Learning -- 7.4 Conclusion -- References -- 8 Data Management -- 8.1 Data Management Process -- 8.1.1 Metadata -- 8.1.2 Semantics -- 8.1.3 Traceability -- 8.2 Curation and Bias -- 8.3 Data Management -- 8.3.1 Data in an Embedded System -- 8.3.2 Data Diversity -- 8.3.3 Data Augmentation -- 8.3.4 Forgetting -- 8.3.5 Data Security -- 8.4 Conclusion -- References -- 9 Architecture -- 9.1 Software Architecture: Interprocess -- 9.2 Software Architecture: Intraprocess -- 9.3 Hardware Choices -- 9.4 Conclusion -- References -- 10 Test and Evaluation -- 10.1 Scenario Driver -- 10.2 Ablation Testing -- 10.3 Computing Accuracy -- 10.3.1 Regression and Normalized RMSE -- 10.3.2 Classification and Confusion Matrices -- 10.3.3 Evaluating Strategy Performance -- 10.4 Learning Assurance: Evaluating a Cognitive System -- 10.4.1 Learning Assurance Process -- 10.4.2 Formal Verification Methods -- 10.4.3 Empirical and Semiformal Verification Methods -- 10.5 Conclusion -- References -- 11 Getting Started: First Steps -- 11.1 Development Considerations -- 11.2 Tools and Data -- 11.2.1 ML Toolkits -- 11.2.2 ML Datasets -- 11.2.3 RF Data-Generation Tools -- 11.3 Conclusion -- References -- Acronyms -- About the Authors -- Index. 330 $aThis comprehensive book gives an overview of how cognitive systems and artificial intelligence (AI) can be used in electronic warfare (EW). Readers will learn how EW systems respond more quickly and effectively to battlefield conditions where sophisticated radars and spectrum congestion put a high priority on EW systems that can characterize and classify novel waveforms, discern intent, and devise and test countermeasures. Specific techniques are covered for optimizing a cognitive EW system as well as evaluating its ability to learn new information in real time. The book presents AI for electronic support (ES), including characterization, classification, patterns of life, and intent recognition. Optimization techniques, including temporal tradeoffs and distributed optimization challenges are also discussed. The issues concerning real-time in-mission machine learning and suggests some approaches to address this important challenge are presented and described. The book covers electronic battle management, data management, and knowledge sharing. Evaluation approaches, including how to show that a machine learning system can learn how to handle novel environments, are also discussed. Written by experts with first-hand experience in AI-based EW, this is the first book on in-mission real-time learning and optimization. 410 0$aArtech House electronic warfare library. 517 $aCognitive Electronic Warfare 606 $aArtificial intelligence 615 0$aArtificial intelligence. 676 $a623.043 700 $aHaigh$b Karen$01655059 701 $aAndrusenko$b Julia$01655060 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910816303403321 996 $aCognitive Electronic Warfare$94007253 997 $aUNINA