“…Understand malware analysis and its practical implementation About This Book Explore the key concepts of malware analysis and memory forensics using real-world examples Learn the art of detecting, analyzing, and investigating malware threats Understand adversary tactics and techniques Who This Book Is For This book is for incident responders, cyber-security investigators, system administrators, malware analyst, forensic practitioners, student, or curious security professionals interested in learning malware analysis and memory forensics. …”

“…It explores the development of computational solutions and novel models to handle complex real-world signals such as speech, music, biomedical data, and multimedia. …”

“…In addition, the book provides advanced techniques to create ‘real world’ designs that fit the device required and which are fast and reliable to implement. …”

“…Drawing on operational examples and real-world case studies, it promises to provide both cyber security professionals and cyber technologies designers with the conceptual models and practical methodologies they need to succeed. …”

Tabla de Contenidos: “…8.3.3 Algorithms to achieve algorithmic fairness -- 8.4 Open problems and conclusion -- Acknowledgments -- References -- 9 Meta-federated learning -- 9.1 Introduction -- 9.2 Background -- 9.2.1 Federated learning -- 9.2.2 Secure aggregation -- 9.2.3 Robust aggregation rules and defenses -- 9.3 Problem definition and threat model -- 9.4 Meta-federated learning -- 9.5 Experimental evaluation and discussion -- 9.5.1 Datasets and experiment setup -- 9.5.2 Utility of meta-FL -- 9.5.3 Robustness of meta-FL -- 9.6 Conclusion -- References -- 10 Graph-aware federated learning -- 10.1 Introduction -- 10.2 Decentralized federated learning -- 10.3 Multi-center federated learning -- 10.4 Graph-knowledge based federated learning -- 10.4.1 Applications of BiG-FL -- 10.4.2 Algorithm design for BiG-FL -- 10.5 Numerical evaluation of GFL models -- 10.5.1 Results on synthetic data -- 10.5.2 Results on real-world data for NLP -- 10.6 Summary -- References -- 11 Vertical asynchronous federated learning: algorithms and theoretic guarantees -- 11.1 Introduction -- 11.1.1 This chapter -- 11.1.2 Related work -- 11.2 Vertical federated learning -- 11.2.1 Problem statement -- 11.2.2 Asynchronous client updates -- 11.2.3 Types of flexible update rules -- 11.3 Convergence analysis -- 11.3.1 Convergence under bounded delay -- 11.3.2 Convergence under stochastic unbounded delay -- 11.4 Perturbed local embedding for smoothness -- 11.4.1 Local perturbation -- 11.4.2 Enforcing smoothness -- 11.5 Numerical tests -- 11.5.1 VAFL for federated logistic regression -- 11.5.2 VAFL for federated deep learning -- 11.5.2.1 Training on ModelNet40 dataset -- 11.5.2.2 Training on MIMIC-III dataset -- Acknowledgments -- References -- 12 Hyperparameter tuning for federated learning - systems and practices -- 12.1 Introduction -- 12.2 Systems resources -- 12.3 Cross-device FL hyperparameters…”

Tabla de Contenidos: “…-- 12.5 Worked Example - HALE UAV -- 12.6 Cells, Batteries, and Real Life -- 12.6.1 Cycle Life, Charge, and Discharge Rates -- 12.6.2 Payload -- 12.6.3 Avionics -- 12.6.4 Temperature -- 12.6.5 End‐of‐Life Performance -- 12.6.6 Protection -- 12.6.7 Balancing - Useful Capacity -- 12.6.8 Summary of Real‐World Issues -- 12.7 A Quick Aside on Regenerative Fuel Cells -- 12.8 So What Do We Need from Our Battery Suppliers? …”

Tabla de Contenidos: “…-- 1.3.7 Advantages and Disadvantages of AI in Post COVID Era -- 1.4 Conclusion -- References -- 2 Healthcare System 4.0 Perspectives on COVID-19 Pandemic -- 2.1 Introduction -- 2.2 Key Techniques of HCS 4.0 for COVID-19 -- 2.2.1 Artificial Intelligence (AI) -- 2.2.2 The Internet of Things (IoT) -- 2.2.3 Big Data -- 2.2.4 Virtual Reality (VR) -- 2.2.5 Holography -- 2.2.6 Cloud Computing -- 2.2.7 Autonomous Robots -- 2.2.8 3D Scanning -- 2.2.9 3D Printing Technology -- 2.2.10 Biosensors -- 2.3 Real World Applications of HCS 4.0 for COVID-19 -- 2.4 Opportunities and Limitations -- 2.5 Future Perspectives -- 2.6 Conclusion -- References -- 3 Analysis and Prediction on COVID-19 Using Machine Learning Techniques -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Types of Machine Learning…”

Tabla de Contenidos: “…7.3.2 Limited Adaptability to Dynamic Environments -- 7.3.3 Lack of Real-Time Decision-Making -- 7.3.4 Limited Perception and Situational Awareness -- 7.3.5 Computational Complexity and Processing Power -- 7.3.6 Calibration and Validation -- 7.4 Proposed Framework -- 7.4.1 Digital Twin Creation -- 7.4.2 Sensor Fusion and Data Acquisition -- 7.4.3 Environmental Analysis -- 7.4.4 Decision-Making and Control -- 7.4.5 Communication and Synchronization -- 7.4.6 Validation and Calibration -- 7.4.7 Iterative Improvement -- 7.5 Benefits and Feasibility -- 7.5.1 Improved Adaptability -- 7.5.2 Real-Time Decision-Making -- 7.5.3 Enhanced Safety -- 7.5.4 Feasibility Considerations -- 7.6 Conclusion and Future Directions -- References -- Chapter 8 Execution of Fully Automated Coal Mining Face With Transparent Digital Twin Self-Adaptive Mining System -- 8.1 Introduction -- 8.2 Simulation Methods in Digital Twins -- 8.2.1 Computational Fluid Dynamics -- 8.2.1.1 Software Tools That are Being Used in Today's Domain for CFD -- 8.2.1.2 Real-World Applications of CFD -- 8.2.2 Multibody Dynamics -- 8.2.3 Kinematics for Multibody Systems -- 8.3 Literature Review -- 8.3.1 Classification of MBD Simulations -- 8.3.2 Finite Element Analysis -- 8.4 Proposed Work -- 8.5 Conclusion -- References -- Chapter 9 MGF-Based BER and Channel Capacity Analysis of Fisher Snedecor Composite Fading Model -- 9.1 Introduction -- 9.2 Fisher Snedecor Composite Fading Model -- 9.3 Performance Analysis Using MGF -- 9.3.1 ABER -- 9.3.1.1 BDPSK and NBFSK -- 9.3.1.2 BPSK and BFSK -- 9.3.1.3 MAM -- 9.3.1.4 Square MQAM -- 9.3.1.5 MPSK -- 9.3.2 NMFSK -- 9.3.3 Adaptive Channel Capacity -- 9.3.3.1 ORA -- 9.3.3.2 CIFR -- 9.4 Numerical Results -- 9.5 Conclusion -- References…”

Tabla de Contenidos: “…Support vector machine and kernel methods takeaways -- Summary -- Part 3. Working in the real world -- Chapter 11. Documentation and deployment -- 11.1. …”

“…Systems Engineering for Aerospace: A Practical Approach applies insights gained from systems engineering to real-world industry problems. The book describes how to measure and manage an aircraft program from start to finish. …”

“…This book provides all the detailed, real-world, ground-level information you need to plan, install, configure, and reliably operate Asterisk in any environment. …”

“…Ethics in Action brings together thirty-four psychologists and eight collaborating professionals from allied disciplines, including nursing, social work, emergency services, and veterinary medicine, to share wisdom gained from facing ethical questions in real-world practice. These knowledgeable contributors share their experiences working with new Canadians, religious minorities, Indigenous communities, and more. …”

“…We cover some of the latest additions to the IBM SAN portfolio, discuss general SAN design considerations, and build these considerations into a selection of real-world case studies. We realize that there are many ways to design a SAN and put all the components together. …”

Tabla de Contenidos: “…The notion of data batches -- 2.2.8. Real-world examples of data tensors -- 2.2.9. Vector data -- 2.2.10. …”

Tabla de Contenidos: “…6.4 Containerklassen: Generics und Varianz -- 6.5 Die Klasse Optional&lt -- T&gt -- -- 6.5.1 Grundlagen zur Klasse Optional -- 6.5.2 Weiterführendes Beispiel und Diskussion -- 6.5.3 Verkettete Methodenaufrufe -- 6.5.4 Erweiterungen in der Klasse Optional&lt -- T&gt -- in JDK 9 -- 6.5.5 Erweiterung in Optional&lt -- T&gt -- in JDK 10 und 11 -- 6.6 Fallstricke im Collections-Framework -- 6.6.1 Wissenswertes zu Arrays -- 6.6.2 Wissenswertes zu Stack, Queue und Deque -- 6.7 Weiterführende Literatur -- 7 Das Stream-API -- 7.1 Grundlagen zu Streams -- 7.1.1 Streams erzeugen - Create Operations -- 7.1.2 Intermediate und Terminal Operations im Überblick -- 7.1.3 Zustandslose Intermediate Operations -- 7.1.4 Zustandsbehaftete Intermediate Operations -- 7.1.5 Terminal Operations -- 7.1.6 Wissenswertes zur Parallelverarbeitung -- 7.1.7 Neuerungen im Stream-API in JDK 9 -- 7.1.8 Neuerungen im Stream-API in JDK 10 -- 7.2 Filter-Map-Reduce -- 7.2.1 Herkömmliche Realisierung -- 7.2.2 Filter-Map-Reduce mit JDK 8 -- 7.3 Praxisbeispiele -- 7.3.1 Aufbereiten von Gruppierungen und Histogrammen -- 7.3.2 Maps nach Wert sortieren -- 8 Datumsverarbeitung seit JDK 8 -- 8.1 Überblick über die neu eingeführten Typen -- 8.1.1 Neue Aufzählungen, Klassen und Interfaces -- 8.1.2 Die Aufzählungen DayOfWeek und Month -- 8.1.3 Die Klassen MonthDay, YearMonth und Year -- 8.1.4 Die Klasse Instant -- 8.1.5 Die Klasse Duration -- 8.1.6 Die Aufzählung ChronoUnit -- 8.1.7 Die Klassen LocalDate, LocalTime und LocalDateTime -- 8.1.8 Die Klasse Period -- 8.1.9 Die Klasse ZonedDateTime -- 8.1.10 Zeitzonen und die Klassen ZoneId und ZoneOffset -- 8.1.11 Die Klasse Clock -- 8.1.12 Formatierung und Parsing -- 8.2 Datumsarithmetik -- 8.2.1 Einstieg in die Datumsarithmetik -- 8.2.2 Real-World-Example: Gehaltszahltag -- 8.3 Interoperabilität mit Legacy-Code -- 9 Applikationsbausteine…”

Tabla de Contenidos: “…Conventional Cryptography -- 14.3 Quantum Key Distribution Protocols -- 14.3.1 Overview and BB84 Protocol -- 14.3.2 The B92 Protocol -- 14.3.3 E91 Protocol -- 14.3.4 SARG04 Protocol -- 14.4 Impact of the Sifting and Distillation Steps on the Key Size -- 14.5 Cryptanalysis -- 14.6 Quantum Key Distribution in the Real World -- References -- Chapter 15 Security Issues in Vehicular Ad Hoc Networks and Quantum Computing -- 15.1 Introduction -- 15.2 Overview of VANET Security -- 15.2.1 Security of VANET -- 15.2.2 Attacks are Classified…”

Tabla de Contenidos: “…. -- The Development of an Intelligent Agent to Detect and Non-Invasively Characterize Lung -- Lesions on CT Scans: Ready for the "Real World"? -- Reprinted from: Diagnostics 2023, 15, 357, doi:10.3390/cancers15020357 385 -- Jang Yoo, Jaeho Lee, Miju Cheon, Sang-Keun Woo, Myung-Ju Ahn, Hong Ryull Pyo, -- Yong Soo Choi, et al. -- Predictive Value of 18F-FDG PET/CT Using Machine Learning for Pathological Response to -- Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell -- Lung Cancer -- Reprinted from: Cancers 2022, 14, 1987, doi:10.3390/cancers14081987 399 -- Gi Hwan Kim, Yong Mee Cho, So-Woon Kim, Ja-Min Park, Sun Young Yoon, Gowun Jeong, -- Dong-Myung Shin, et al.…”

“…Prepare for Microsoft Exam 70-740—and help demonstrate your real-world mastery of Windows Server 2016 installation, storage, and compute features and capabilities. …”

Tabla de Contenidos: “…10.3.2 Post-Filters 257 -- 10.3.3 Post-Filter Types 257 -- 10.3.4 Estimating Post-Filters with Machine Learning 259 -- 10.3.5 Post-Filter Design Based on Spatial Parameters 259 -- References 261 -- 11 Cross-Spectrum-Based Post-Filter Utilizing Noisy and Robust Beamformers 265 /Symeon Delikaris-Manias and Ville Pulkki -- 11.1 Introduction 265 -- 11.2 Notation and Signal Model 267 -- 11.2.1 Virtual Microphone Design Utilizing Pressure Microphones 268 -- 11.3 Estimation of the Cross-Spectrum-Based Post-Filter 269 -- 11.3.1 Post-Filter Estimation Utilizing Two Static Beamformers 270 -- 11.3.2 Post-Filter Estimation Utilizing a Static and an Adaptive Beamformer 272 -- 11.3.3 Smoothing Techniques 277 -- 11.4 Implementation Examples 279 -- 11.4.1 Ideal Conditions 279 -- 11.4.2 Prototype Microphone Arrays 281 -- 11.5 Conclusions and Further Remarks 283 -- 11.6 Source Code 284 -- References 287 -- 12 Microphone-Array-Based Speech Enhancement Using Neural Networks /Pasi Pertila 291 -- 12.1 Introduction 291 -- 12.2 Time–Frequency Masks for Speech Enhancement Using Supervised Learning 293 -- 12.2.1 Beamforming with Post-Filtering 293 -- 12.2.2 Overview of Mask Prediction 294 -- 12.2.3 Features for Mask Learning 295 -- 12.2.4 Target Mask Design 297 -- 12.3 Artificial Neural Networks 298 -- 12.3.1 Learning the Weights 299 -- 12.3.2 Generalization 301 -- 12.3.3 Deep Neural Networks 305 -- 12.4 Mask Learning: A Simulated Example 305 -- 12.4.1 Feature Extraction 306 -- 12.4.2 Target Mask Design 306 -- 12.4.3 Neural Network Training 307 -- 12.4.4 Results 308 -- 12.5 Mask Learning: A Real-World Example 310 -- 12.5.1 Brief Description of the Third CHiME Challenge Data 310 -- 12.5.2 Data Processing and Beamforming 312 -- 12.5.3 Description of Network Structure, Features, and Targets 312 -- 12.5.4 Mask Prediction Results and Discussion 314 -- 12.5.5 Speech Enhancement Results 316 -- 12.6 Conclusions 318 -- 12.7 Source Code 318 -- 12.7.1 Matlab Code for Neural-Network-Based Sawtooth Denoising Example 318.…”

“…Authors Dave Milner, Bart McDonough, and Paul Stork bring to the table decades of experience in real-world development of solutions for customers—expertise that is the practical result of what works in real-world customer environments. …”