Search Results - (((((((ant OR want) OR semantic) OR west) OR cantor) OR anne) OR shared) OR hints) algorithms.

The bitcoin big bang : how alternative currencies are about to change the world by Kelly, Brian, 1971-

Table of Contents: “…; What Is Cryptography?; Still Want to Be a Miner?; Do We Need Another Bitcoin?; 7 Nautiluscoin-0 to 1 Million in 60 Days; Creating the Coin; Did It Work?…”
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Building Machine Learning Systems with Python. by Richert, Willi

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Ranking the Liveability of the World's Major Cities : the Global Liveable Cities Index (GLCI). by Tan, Khee Giap

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Data clustering in C++ : an object-oriented approach by Gan, Guojun, 1979-

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Iaeng transactions on electrical engineering. Vol. 1, Special issue of the International Multiconference of Engineers and Computer Scientists 2012

Table of Contents: “…Pawar -- Web services for chronic pain monitoring / Nuno Goncalo Coelho Costa Pombo, Pedro Jose Guerra de Araujo and Joaquim Manuel Vieira da Silva Viana -- Parallel binomial American option pricing on CPU-GPU hybrid platform / Nan Zhang, Chi-Un Lei and Ka Lok Man -- The subsystem grouping scheme using use case dependency graph and domain-specific semantic model for large complex systems / Nanchaya Khrueahong and Wiwat Vatanawood -- MOBM: a methodology for building practical domain ontologies from database information / Minyoung Ra [and others] -- A trial of the dynamic weighted sum method for multi-objective optimization / Hong Zhang -- A multi-agent platform to manage distributed and heterogeneous knowledge by using semantic web / Inaya Lahoud [and others] -- An intelligent train marshaling based on the processing time considering group layout of freight cars / Yoichi Hirashima -- A web-based multilingual intelligent tutor system based on Jackson's learning styles profiler and expert systems / H. …”
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Human Behavior Analysis for Library and Information Science. by Greifender, Elke

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Handbook for pandemic and mass-casualty planning and response

Table of Contents: “…Burn Injuries during Mass-Casualty IncidentsBlast Injuries; Section 3: Communication and Information Sharing; Information Sharing in Practice and for Practice: Mass-Casualty Cases; Communication between Emergency Medical Services and Media during Mass-Casualty Events in Croatia; Just-in-Time Information; Collaboration Topologies for Interdisciplinary and Interlevel Information Exchange; Networks and Their Role in Counteracting Contemporary Global Threats: A New Model; Architecture of R & D Project Management Systems at Medical Institutions; Lessons Learned; Lessons Learned; Subject Index…”
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High performance computing on complex environments by Jeannot, Emmanuel

Table of Contents: “…Chapter 4: Parallel Algorithms for Parabolic Problems on Graphs in Neuroscience4.1 Introduction; 4.2 Formulation of the Discrete Model; 4.3 Parallel Algorithms; 4.4 Computational Results; 4.5 Conclusions; Acknowledgments; References; Part III: Communication and Storage Considerations in High-Performance Computing; Chapter 5: An Overview of Topology Mapping Algorithms and Techniques in High-Performance Computing; 5.1 Introduction; 5.2 General Overview; 5.3 Formalization of the Problem; 5.4 Algorithmic Strategies for Topology Mapping; 5.5 Mapping Enforcement Techniques; 5.6 Survey of Solutions.…”
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Data fusion support to activity-based intelligence by Antony, Richard T.

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Traumatic brain injury

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Emergency triage : telephone triage and advice

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Stochastic structural dynamics : application of finite element methods by To, Cho W. S.

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Advanced wireless networks : technology and business models by Glisic, Savo G.

Table of Contents: “…16.1 Introduction 523 -- 16.2 Layering as Optimization Decomposition 524 -- 16.3 Cross-Layer Optimization 533 -- 16.4 Optimization Problem Decomposition Methods 543 -- References 554 -- 17 Network Information Theory 557 -- 17.1 Capacity of Ad Hoc Networks 557 -- 17.2 Information Theory and Network Architectures 569 -- 17.3 Cooperative Transmission in Wireless Multihop Ad Hoc Networks 577 -- References 584 -- 18 Stability of Advanced Network Architectures 585 -- 18.1 Stability of Cooperative Cognitive Wireless Networks 585 -- 18.2 System Model 586 -- 18.4 Optimal Control Policy 592 -- 18.5 Achievable Rates 594 -- 18.6 Stabilizing Transmission Policies 598 -- References 605 -- 19 Multi-Operator Spectrum Sharing 607 -- 19.1 Business Models for Spectrum Sharing 607 -- 19.2 Spectrum Sharing in Multi-hop Networks 638 -- References 656 -- 20 Large Scale Networks and Mean Field Theory 659 -- 20.1 MFT for Large Heterogeneous Cellular Networks 659 -- 20.2 Large Scale Network Model Compression 664 -- 20.3 Mean Field Theory Model of Large Scale DTN Networks 668 -- 20.4 Mean Field Modeling of Adaptive Infection Recovery in Multicast DTN Networks 674 -- 20.5 Mean Field Theory for Scale-Free Random Networks 701 -- 20.6 Spectrum Sharing and MFT 709 -- 20.7 Modeling Dynamics of Complex System 711 -- Appendix A.20 Iterative Algorithm to Solve Systems of Nonlinear ODEs (DiNSE-Algorithm) 721 -- Appendix B.20 Infection Rate of Destinations for DNCM 722 -- Appendix C.20 Infection Rate for Basic Epidemic Routing 722 -- References 722 -- 21 mmWave Networks 726 -- 21.1 mmWave Technology in Subcellular Architecture 726 -- 21.2 Microeconomics of Dynamic mmWave Networks 737 -- References 747 -- 22 Cloud Computing in Wireless Networks 750 -- 22.1 Technology Background 750 -- 22.2 System Model 752 -- 22.3 System Optimization 756 -- 22.4 Dynamic Control Algorithm 758 -- 22.5 Achievable Rates 761 -- 22.6 Stabilizing Control Policies 763 -- References 769 -- 23 Wireless Networks and Matching Theory 771.…”
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Optimal structural analysis by Kaveh, A. (Ali), 1948-

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Iterative learning control for multi-agent systems coordination by Yang, Shiping, 1987-, Xu, Jian-Xin, Li, Xuefang, 1985-, Shen, Dong, 1982-

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Hack proofing your network

Table of Contents: “…</br><br> Knowing What To Expect in the Rest of This Book</br><br> Understanding the Current Legal Climate</br><br> Summary</br><br> Frequently Asked Questions</br><br>Chapter 2 The Laws of Security</br><br> Introduction</br><br> Knowing the Laws of Security</br><br> Client-Side Security Doesn't Work</br><br> You Cannot Securely Exchange Encryption Keys without a Shared Piece of Information</br><br> Malicious Code Cannot Be 100 Percent Protected against</br><br> Any Malicious Code Can Be Completely Morphed to Bypass Signature Detection</br><br> Firewalls Cannot Protect You 100 Percent from Attack</br><br> Social Engineering</br><br> Attacking Exposed Servers</br><br> Attacking the Firewall Directly</br><br> Client-Side Holes</br><br> Any IDS Can Be Evaded</br><br> Secret Cryptographic Algorithms Are Not Secure</br><br> If a Key Is Not Required, You Do Not Have Encryption-You Have Encoding</br><br> Passwords Cannot Be Securely Stored on the Client Unless There Is Another Password to Protect Them</br><br> In Order for a System to Begin to Be Considered Secure, It Must Undergo an Independent Security Audit</br><br> Security through Obscurity Does Not Work</br><br> Summary </br><br> Solutions Fast Track</br><br> Frequently Asked Questions</br><br>Chapter 3 Classes of Attack</br><br> Introduction</br><br> Identifying and Understanding the Classes of Attack </br><br> Denial of Service</br><br> Information Leakage</br><br> Regular File Access</br><br> Misinformation</br><br> Special File/Database Access</br><br> Remote Arbitrary Code Execution</br><br> Elevation of Privileges</br><br> Identifying Methods of Testing for Vulnerabilities</br><br> Proof of Concept</br><br> Standard Research Techniques</br><br> Summary</br><br> Solutions Fast Track</br><br> Frequently Asked Questions</br><br>Chapter 4 Methodology</br><br> Introduction</br><br> Understanding Vulnerability Research Methodologies</br><br> Source Code Research</br><br> Binary Research</br><br> The Importance of Source Code Reviews</br><br> Searching Error-Prone Functions</br><br> Reverse Engineering Techniques</br><br> Disassemblers, Decompilers, and Debuggers</br><br> Black Box Testing</br><br> Chips</br><br> Summary</br><br> Solutions Fast Track</br><br> Frequently Asked Questions</br><br>Chapter 5 Diffing</br><br> Introduction</br><br> What Is Diffing?…”
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How to implement market models using VBA by Goossens, Francois, 1960-

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Advances in time series forecasting. Volume 2

Table of Contents: “…INTRODUCTION -- CLASSICAL TIME SERIES FORECASTING MODELS -- ARTIFICIAL NEURAL NETWORKS FOR FORECASTING TIME SERIES -- A NEW ARTIFICIAL NEURAL NETWORK WITH DETERMINISTIC COMPONENTS -- APPLICATIONS -- CONCLUSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- A Fuzzy Time Series Approach Based on Genetic Algorithm with Single Analysis Process -- Ozge Cagcag Yolcu* -- INTRODUCTION -- FUZZY TIME SERIES -- RELATED METHODS -- Genetic Algorithm (GA) -- Single Multiplicative Neuron Model -- PROPOSED METHOD -- APPLICATIONS -- CONCLUSION AND DISCUSSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- Forecasting Stock Exchanges with Fuzzy Time Series Approach Based on Markov Chain Transition Matrix -- Cagdas Hakan Aladag1,* and Hilal Guney2 -- INTRODUCTION -- FUZZY TIME SERIES -- TSAUR 'S FUZZY TIME SERIES MARKOV CHAIN MODEL -- THE IMPLEMENTATION -- CONCLUSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- A New High Order Multivariate Fuzzy Time Series Forecasting Model -- Ufuk Yolcu* -- INTRODUCTION -- RELATED METHODOLOGY -- The Fuzzy C-Means (FCM) Clustering Method -- Single Multiplicative Neuron Model Artificial Neural Network (SMN-ANN) -- Fuzzy Time Series -- THE PROPOSED METHOD -- APPLICATIONS -- CONCLUSIONS AND DISCUSSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- Fuzzy Functions Approach for Time Series Forecasting -- Ali Z. …”
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Automatic Text Summarization by Torres-Moreno, Juan-Manuel

Table of Contents: “…State-of-the-art approaches; 3.4. Latent semantic analysis; 3.5. Graph-based approaches; 3.6. …”
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Microwave and millimeter wave circuits and systems : emerging design, technologies, and applications

Table of Contents: “…1.1.7 MBF Model -- the Memoryless PA Behavioural Model of ChoiceAcknowledgements; References; 2 Artificial Neural Network in Microwave Cavity Filter Tuning; 2.1 Introduction; 2.2 Artificial Neural Networks Filter Tuning; 2.2.1 The Inverse Model of the Filter; 2.2.2 Sequential Method; 2.2.3 Parallel Method; 2.2.4 Discussion on the ANN's Input Data; 2.3 Practical Implementation -- Tuning Experiments; 2.3.1 Sequential Method; 2.3.2 Parallel Method; 2.4 Influence of the Filter Characteristic Domain on Algorithm Efficiency; 2.5 Robots in the Microwave Filter Tuning; 2.6 Conclusions; Acknowledgement…”
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