Search Results - (((((((kant OR manthe) OR king) OR semantic) OR cantor) OR anne) OR halted) OR ranting) algorithms.

Theoretical Computer Science : Proceedings of the 10th Italian Conference on ICTCS '07. by Italiano, Giuseppe F.

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Design optimization of fluid machinery : applying computational fluid dynamics and numerical optimization by Kim, Kwang-Yong, 1956-, Samad, Abdus, Benini, Ernesto

Table of Contents: “…2.2.5.3 Periodic/Cyclic Boundary Conditions2.2.5.4 Symmetry Boundary Conditions; 2.2.6 Moving Reference Frame (MRF); 2.2.7 Verification and Validation; 2.2.8 Commercial CFD Software; 2.2.9 Open Source Codes; 2.2.9.1 OpenFOAM; References; Chapter 3 Optimization Methodology; 3.1 Introduction; 3.1.1 Engineering Optimization Definition; 3.1.2 Design Space; 3.1.3 Design Variables and Objectives; 3.1.4 Optimization Procedure; 3.1.5 Search Algorithm; 3.2 Multi-Objective Optimization (MOO); 3.2.1 Weighted Sum Approach; 3.2.2 Pareto-Optimal Front…”
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From complexity in the natural sciences to complexity in operation management systems by Briffaut, Jean-Pierre

Table of Contents: “…Complexity in perspective -- 1.2.1. Etymology and semantics -- 1.2.2. Methods proposed for dealing with complexity from the Middle Ages to the 17th Century and their current outfalls -- 1.3. …”
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Fog and fogonomics : challenges and practices of fog computing, communication, networking, strategy, and economics

Table of Contents: “…5.5.2 Survivability, Availability, and Reliability 122 -- 5.6 Sovereignty, Privacy, Security, Interoperability, and Management 123 -- 5.6.1 Data Sovereignty 123 -- 5.6.2 Privacy and Security 123 -- 5.6.3 Heterogeneity and Interoperability 124 -- 5.6.4 Monitoring, Orchestration, and Management 124 -- 5.7 Trade-Offs 125 -- 5.8 Conclusion 126 -- References 126 -- 6 Incentive Schemes for User-Provided Fog Infrastructure 129 /George Iosifidis, Lin Gao, Jianwei Huang, and Leandros Tassiulas -- 6.1 Introduction 129 -- 6.2 Technology and Economic Issues in UPIs 132 -- 6.2.1 Overview of UPI models for Network Connectivity 132 -- 6.2.2 Technical Challenges of Resource Allocation 134 -- 6.2.3 Incentive Issues 135 -- 6.3 Incentive Mechanisms for Autonomous Mobile UPIs 137 -- 6.4 Incentive Mechanisms for Provider-assisted Mobile UPIs 140 -- 6.5 Incentive Mechanisms for Large-Scale Systems 143 -- 6.6 Open Challenges in Mobile UPI Incentive Mechanisms 145 -- 6.6.1 Autonomous Mobile UPIs 145 -- 6.6.1.1 Consensus of the Service Provider 145 -- 6.6.1.2 Dynamic Setting 146 -- 6.6.2 Provider-assisted Mobile UPIs 146 -- 6.6.2.1 Modeling the Users 146 -- 6.6.2.2 Incomplete Market Information 147 -- 6.7 Conclusions 147 -- References 148 -- 7 Fog-Based Service Enablement Architecture 151 /Nanxi Chen, Siobhán Clarke, and Shu Chen -- 7.1 Introduction 151 -- 7.1.1 Objectives and Challenges 152 -- 7.2 Ongoing Effort on FogSEA 153 -- 7.2.1 FogSEA Service Description 156 -- 7.2.2 Semantic Data Dependency Overlay Network 158 -- 7.2.2.1 Creation and Maintenance 159 -- 7.2.2.2 Semantic-Based Service Matchmarking 161 -- 7.3 Early Results 164 -- 7.3.1 Service Composition 165 -- 7.3.1.1 SeDDON Creation in FogSEA 167 -- 7.3.2 Related Work 168 -- 7.3.2.1 Semantic-Based Service Overlays 169 -- 7.3.2.2 Goal-Driven Planning 170 -- 7.3.2.3 Service Discovery 171 -- 7.3.3 Open Issue and Future Work 172 -- References 174 -- 8 Software-Defined Fog Orchestration for IoT Services 179 /Renyu Yang, Zhenyu Wen, David McKee, Tao Lin, Jie Xu, and Peter Garraghan.…”
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Handbook of safety principles

Table of Contents: “…Success or Failure / Ann Enander -- 30.8. Relations to Other Safety Principles / Ann Enander -- References / Ann Enander -- Further Reading / Ann Enander -- 31. …”
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Danforth's obstetrics and gynecology.

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Artificial intelligence and data mining approaches in security frameworks

Table of Contents: “…87 -- 5.1.2 Purpose of Spamming 88 -- 5.1.3 Spam Filters Inputs and Outputs 88 -- 5.2 Content-Based Spam Filtering Techniques 89 -- 5.2.1 Previous Likeness–Based Filters 89 -- 5.2.2 Case-Based Reasoning Filters 89 -- 5.2.3 Ontology-Based E-Mail Filters 90 -- 5.2.4 Machine-Learning Models 90 -- 5.2.4.1 Supervised Learning 90 -- 5.2.4.2 Unsupervised Learning 90 -- 5.2.4.3 Reinforcement Learning 91 -- 5.3 Machine Learning–Based Filtering 91 -- 5.3.1 Linear Classifiers 91 -- 5.3.2 Naïve Bayes Filtering 92 -- 5.3.3 Support Vector Machines 94 -- 5.3.4 Neural Networks and Fuzzy Logics–Based Filtering 94 -- 5.4 Performance Analysis 97 -- 5.5 Conclusion 97 -- References 98 -- 6 Artificial Intelligence in the Cyber Security Environment 101 Jaya Jain -- 6.1 Introduction 102 -- 6.2 Digital Protection and Security Correspondences Arrangements 104 -- 6.2.1 Operation Safety and Event Response 105 -- 6.2.2 AI2 105 -- 6.2.2.1 CylanceProtect 105 -- 6.3 Black Tracking 106 -- 6.3.1 Web Security 107 -- 6.3.1.1 Amazon Macie 108 -- 6.4 Spark Cognition Deep Military 110 -- 6.5 The Process of Detecting Threats 111 -- 6.6 Vectra Cognito Networks 112 -- 6.7 Conclusion 115 -- References 115 -- 7 Privacy in Multi-Tenancy Frameworks Using AI 119 Shweta Solanki -- 7.1 Introduction 119 -- 7.2 Framework of Multi-Tenancy 120 -- 7.3 Privacy and Security in Multi-Tenant Base System Using AI 122 -- 7.4 Related Work 125 -- 7.5 Conclusion 125 -- References 126 -- 8 Biometric Facial Detection and Recognition Based on ILPB and SVM 129 Shubhi Srivastava, Ankit Kumar and Shiv Prakash -- 8.1 Introduction 129 -- 8.1.1 Biometric 131 -- 8.1.2 Categories of Biometric 131 -- 8.1.2.1 Advantages of Biometric 132 -- 8.1.3 Significance and Scope 132 -- 8.1.4 Biometric Face Recognition 132 -- 8.1.5 Related Work 136 -- 8.1.6 Main Contribution 136 -- 8.1.7 Novelty Discussion 137 -- 8.2 The Proposed Methodolgy 139 -- 8.2.1 Face Detection Using Haar Algorithm 139 -- 8.2.2 Feature Extraction Using ILBP 141 -- 8.2.3 Dataset 143 -- 8.2.4 Classification Using SVM 143 -- 8.3 Experimental Results 145 -- 8.3.1 Face Detection 146 -- 8.3.2 Feature Extraction 146 -- 8.3.3 Recognize Face Image 147 -- 8.4 Conclusion 151 -- References 152 -- 9 Intelligent Robot for Automatic Detection of Defects in Pre-Stressed Multi-Strand Wires and Medical Gas Pipe Line System Using ANN and IoT 155 S K Rajesh Kanna, O. …”
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XIVth International Congress on Mathematical Physics : Lisbon, 28 July - 2 August 2003

Table of Contents: “…Quantum dynamical entropies and quantum algorithmic complexities / Fabio Benatti (U. Trieste). …”
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Insight into fuzzy modeling by Novák, Vilém, 1951-, Perfilieva, Irina, 1953-, Dvořák, Antonín (Mathematician)

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The Dictionary of Critical Social Sciences. by Young, T. R.

Table of Contents: “…Cruel and Unusual Punishment -- Cult of the Leader -- Cult of the Self -- Cultural Conflict -- Cultural Diffusion -- Cultural Lag -- Cultural Relativism -- Cultural Revolution -- Culture -- Culture (of Resistance) -- Custody -- Custody of a Child -- Custom -- Cybernetics -- Cyberspace -- D -- Darwin, Charles (1809-1882) -- Data -- Days of Grace -- Death of God -- Death of the Subject -- Death Penalty -- Decentered Subject -- Decentering -- Decertification -- Decidability -- Decode -- Deconstruction -- Deduction/Deductive Logic -- De facto -- Defection of the Intelligentsia -- Deficit Spending -- Definition -- Definition of the Situation -- Degradation Routines -- Deism -- De jure -- Deleuze, Gilles (1925-1995) -- Delinquency -- Demagogue -- Democracy -- Democratic Self-Management -- Demographic Transition Theory -- Demography -- Demonization -- Deontology -- Dependency Theory -- Dependent Variable -- Depoliticization -- Depression -- Dereification -- Derrida, Jacques -- Descartes, René (1596-1650) -- Desire -- Deskilling -- Deskilling of America -- Determinism -- Developing Countries -- Development/Underdevelopment -- Deviance -- Deviance, Primary -- Deviant -- Dewey, John (1859-1952) -- Dialectic -- Dialectic Materialism -- Dialogical Pedagogy -- Dictatorship -- Dictatorship of the Proletariat -- Dictionary -- Differance -- Difference -- Differential Association -- Differentiation -- Diminishing Returns, Law of -- Dinks -- Dionysian/Dionysus -- Disaster -- Disciplinary Society -- Discourse -- Discrimination -- Discursive Formations -- Disemployment -- Disengagement Theory -- Displacement -- Dissipative Structure -- Dividends -- Divine Right (of Kings) -- Division of Labor -- Dogma/Dogmatism -- Double Bind -- Dracon/Draconian -- Dramaturgical Analysis -- Dramaturgical Society -- Dramaturgy -- Dream Work -- Dühring, Eugen (1833-1901).…”
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Big data : concepts, technology and architecture by Balusamy, Balamurugan, R, Nandhini Abirami, Kadry, Seifedine, 1977-, Gandomi, Amir Hossein

Table of Contents: “…143</p> <p>5.2.3HDFS Architecture. 143</p> <p>5.2.4HDFS Read/Write Operation. 146</p> <p>5.2.5Rack Awareness. 148</p> <p>5.2.6Features of HDFS. 149</p> <p>5.2.6.1Cost-effective. 149</p> <p>5.2.6.2Distributed storage. 149</p> <p>5.2.6.3Data Replication. 149</p> <p>5.3 Hadoop Computation. 149</p> <p>5.3.1MapReduce. 149</p> <p>5.3.1.1Mapper. 151</p> <p>5.3.1.2Combiner. 151</p> <p>5.3.1.3 Reducer. 152</p> <p>5.3.1.4 JobTracker and TaskTracker. 153</p> <p>5.3.2 MapReduce Input Formats. 154</p> <p>5.3.3 MapReduce Example. 156</p> <p>5.3.4 MapReduce Processing. 157</p> <p>5.3.5 MapReduce Algorithm.. 160</p> <p>5.3.6 Limitations of MapReduce. 161</p> <p>5.4Hadoop 2.0. 161</p> <p>5.4.1Hadoop 1.0 limitations. 162</p> <p>5.4.2 Features of Hadoop 2.0. 163</p> <p>5.4.3 Yet Another Resource Negotiator (YARN). 164</p> <p>5.4.3 Core components of YARN.. 165</p> <p>5.4.3.1 ResourceManager. 165</p> <p>5.4.3.2 NodeManager. 166</p> <p>5.4.4 YARN Scheduler. 169</p> <p>5.4.4.1 <i>FIFO scheduler</i>. 169</p> <p>5.4.4.2 <i>Capacity Scheduler</i>. 170</p> <p>5.4.4.3 <i>Fair Scheduler</i>. 170</p> <p>5.4.5 Failures in YARN.. 171</p> <p>5.4.5.1ResourceManager failure. 171</p> <p>5.4.5.2 ApplicationMaster failure. 172</p> <p>5.4.5.3 NodeManagerFailure. 172</p> <p>5.4.5.4 Container Failure. 172</p> <p>5.3 HBASE. 173</p> <p>5.4 Apache Cassandra. 176</p> <p>5.5 SQOOP. 177</p> <p>5.6 Flume. 179</p> <p>5.6.1 Flume Architecture. 179</p> <p>5.6.1.1 Event. 180</p> <p>5.6.1.2 Agent. 180</p> <p>5.7 Apache Avro. 181</p> <p>5.8 Apache Pig. 182</p> <p>5.9 Apache Mahout. 183</p> <p>5.10 Apache Oozie. 183</p> <p>5.10.1 Oozie Workflow.. 184</p> <p>5.10.2 Oozie Coordinators. 186</p> <p>5.10.3 Oozie Bundles. 187</p> <p>5.11 Apache Hive. 187</p> <p>5.11 Apache Hive. 187</p> <p>Hive Architecture. 189</p> <p>Hadoop Distributions. 190</p> <p>Chapter 5refresher. 191</p> <p>Conceptual short questions with answers. 194</p> <p>Frequently asked Interview Questions. 199</p> <p>Chapter Objective. 200</p> <p>6.1 Terminologies of Big Data Analytics. 201</p> <p><i>Data Warehouse</i>. 201</p> <p><i>Business Intelligence</i>. 201</p> <p><i>Analytics</i>. 202</p> <p>6.2 Big Data Analytics. 202</p> <p>6.2.1 Descriptive Analytics. 204</p> <p>6.2.2 Diagnostic Analytics. 205</p> <p>6.2.3 Predictive Analytics. 205</p> <p>6.2.4 Prescriptive Analytics. 205</p> <p>6.3 Data Analytics Lifecycle. 207</p> <p>6.3.1 Business case evaluation and Identify the source data. 208</p> <p>6.3.2 Data preparation. 209</p> <p>6.3.3 Data Extraction and Transformation. 210</p> <p>6.3.4 Data Analysis and visualization. 211</p> <p>6.3.5 Analytics application. 212</p> <p>6.4 Big Data Analytics Techniques. 212</p> <p>6.4.1 Quantitative Analysis. 212</p> <p>6.4.3 Statistical analysis. 214</p> <p>6.4.3.1 A/B testing. 214</p> <p>6.4.3.2 Correlation. 215</p> <p>6.4.3.3 Regression. 218</p> <p>6.5 Semantic Analysis. 220</p> <p>6.5.1 Natural Language Processing. 220</p> <p>6.5.2 Text Analytics. 221</p> <p>6.7 Big Data Business Intelligence. 222</p> <p>6.7.1 Online Transaction Processing (OLTP). 223</p> <p>6.7.2 Online Analytical Processing (OLAP). 223</p> <p>6.7.3 Real-Time Analytics Platform (RTAP). 224</p> <p>6.6Big Data Real Time Analytics Processing. 225</p> <p>6.7 Enterprise Data Warehouse. 227</p> <p>Chapter 6 Refresher. 228</p> <p>Concept…”
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Visual Inspection Technology in the Hard Disc Drive Industry. by Muneesawang, Paisarn

Table of Contents: “…Introduction / Suchart Yammen / Paisarn Muneesawang -- 1.2. Algorithm for corrosion detection / Suchart Yammen / Paisarn Muneesawang -- 1.2.1. …”
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Fundamentals of Fluid Power Control. by Watton, John

Table of Contents: “…Control-Volume Flow Continuity -- PRV Flow -- Force Balance at the Spindle -- 5.13.3 Frequency Response from a Linearized Transfer Function Analysis -- 5.14 Servovalve Dynamics -- First-Stage, Armature, and Flapper-Nozzle -- Flapper-Nozzle and Resistance Bridge Flow Characteristic -- Force Balance at the Spool -- 5.15 An Open-Loop Servovalve-Motor Drive with Line Dynamics Modeled by Lumped Approximations -- Servovalve, Dynamics Included, Underlapped Spool -- Lines, Laminar Mean Flow, Two Lump Approximations per Line, Negligible Motor Internal Volume -- Motor Flow and Torque Equations -- 5.16 Transmission Line Dynamics -- 5.16.1 Introduction -- Servovalve-Cylinder with Short Lines and Significant Actuator Volumes -- Servovalve-Motor with Long Lines and Negligible Actuator Volumes -- 5.16.2 Lossless Line Model for Z and Y -- 5.16.3 Average and Distributed Line Friction Models for Z and Y -- 5.16.4 Frequency-Domain Analysis -- 5.16.5 Servovalve-Reflected Linearized Coefficients -- 5.16.6 Modeling Systems with Nonlossless Transmission Lines, the Modal Analysis Method -- 5.16.7 Modal Analysis Applied to a Servovalve-Motor Open-Loop Drive -- 5.17 The State-Space Method for Linear Systems Modeling -- 5.17.1 Modeling Principles -- 5.17.2 Some Further Aspects of the Time-Domain Solution -- 5.17.3 The Transfer Function Concept in State Space -- 5.18 Data-Based Dynamic Modeling -- 5.18.1 Introduction -- 5.18.2 Time-Series Modeling -- 5.18.3 The Group Method of Data Handling (GMDH) Algorithm -- 5.18.4 Artificial Neural Networks -- 5.18.5 A Comparison of Time-Series, GMDH, and ANN Modeling of a Second-Order Dynamic System -- 5.18.6 Time-Series Modeling of a Position Control System -- 5.18.7 Time-Series Modeling for Fault Diagnosis -- 5.18.8 Time-Series Modeling of a Proportional PRV -- 5.18.9 GMDH Modeling of a Nitrogen-Filled Accumulator.…”
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Engineering autonomous vehicles and robots : the DragonFly modular-based approach by Liu, Shaoshan

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Power system monitoring and control by Bevrani, Hassan

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