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Tabla de Contenidos: “…-- 7.2 Creating your own file formats for data storage -- 7.2.1 Writing data to disk with serde and the bincode format -- 7.3 Implementing a hexdump clone -- 7.4 File operations in Rust -- 7.4.1 Opening a file in Rust and controlling its file mode -- 7.4.2 Interacting with the filesystem in a type-safe manner with std::fs::Path -- 7.5 Implementing a key-value store with a log-structured, append-only storage architecture -- 7.5.1 The key-value model -- 7.5.2 Introducing actionkv v1: An in-memory key-value store with a command-line interface -- 7.6 Actionkv v1: The front-end code -- 7.6.1 Tailoring what is compiled with conditional compilation -- 7.7 Understanding the core of actionkv: The libactionkv crate -- 7.7.1 Initializing the ActionKV struct -- 7.7.2 Processing an individual record -- 7.7.3 Writing multi-byte binary data to disk in a guaranteed byte order -- 7.7.4 Validating I/O errors with checksums -- 7.7.5 Inserting a new key-value pair into an existing database -- 7.7.6 The full code listing for actionkv -- 7.7.7 Working with keys and values with HashMap and BTreeMap -- 7.7.8 Creating a HashMap and populating it with values -- 7.7.9 Retrieving values from HashMap and BTreeMap -- 7.7.10 How to decide between HashMap and BTreeMap -- 7.7.11 Adding a database index to actionkv v2.0 -- Summary -- 8 Networking -- 8.1 All of networking in seven paragraphs -- 8.2 Generating an HTTP GET request with reqwest -- 8.3 Trait objects -- 8.3.1 What do trait objects enable? …”

Tabla de Contenidos: “…4.2.11.2.6 OpenIot middleware platform and Virtual Development Kit -- 4.3 IoT Projects' Pilots and Demonstrators -- 4.4 Summary -- 4.5 List of Contributors -- Bibliography -- Chapter 5 - Industrial Internet of Thingsand the Innovation Processesin Smart Manufacturing -- 5.1 IIoT for Manufacturing: Key Enabler for 4th Industrial Revolution -- 5.2 IoT in the Factories of the Future PPP and Digital Manufacturing:The EFFRA Perspective -- 5.2.1 IoT &amp -- Cyber-Physical Production Systems -- 5.2.2 CPPS Architectures Design Drivers for Scalable, Adaptive and Smart Manufacturing Systems -- 5.3 Product Design and Engineering in the IoT Era:The LINKEDDESIGN Project -- 5.3.1 IoT-Enabled Closed Loop Framework -- 5.3.2 Discussion -- 5.4 Workplaces of the Future and IoT:The FITMAN Project -- 5.4.1 FITMAN Smart Factory Platform (IoT) -- 5.4.2 Safe &amp -- HealthyWorkforce:TRW Use Case -- 5.5 Osmosis Membranes for IoT Real-Digital-Virtual Worlds Interconnection:The OSMOSE Project -- 5.5.1 The IoT Data Gaps -- 5.5.2 The Liquid Enterprise -- 5.5.3 Osmotic Context Management -- 5.6 IoT Enhanced Learning for Complex SystemsMaintenance:The TELLME Project -- 5.6.1 The Need for IoT Enhanced Learning in Aerospace -- 5.6.2 IoT Enhanced Learning for Avoidance of Foreign ObjectDebris (FOD) -- 5.6.3 IoT Enhanced Learning for Non-StandardWorkplaceEnvironmental Condition -- 5.6.4 FutureWork -- 5.7 IoT-Driven Manufacturing Innovation Ecosystems -- 5.8 Industrial Internet of Things:The US IGNITEPerspective -- 5.8.1 Background on US IGNITE and the GENI/FIRE Initiatives -- 5.8.2 Cyber Physical Tools and Frameworks -- 5.9 Research, Innovation Challenges for IoT Adoption in Manufacturing:The SMART 2013/37 EC Study -- 5.9.1 The Study IoT and Cloud Research and Innovation Strategy -- 5.9.2 The Main Market Trends -- 5.9.3 The IoT and Cloud Research and InnovationChallenges…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- Biography -- List of Contributors -- Chapter 1 Artificial Intelligence and Cybersecurity: Tale of Healthcare Applications -- 1.1 Introduction -- 1.2 A Brief History of AI -- 1.3 AI in Healthcare -- 1.4 Morality and Ethical Association of AI in Healthcare -- 1.5 Cybersecurity, AI, and Healthcare -- 1.6 Future of AI and Healthcare -- 1.7 Conclusion -- References -- Chapter 2 Data Analytics for Smart Cities: Challenges and Promises -- 2.1 Introduction -- 2.2 Role of Machine Learning in Smart Cities -- 2.3 Smart Cities Data Analytics Framework -- 2.3.1 Data Capturing -- 2.3.2 Data Analysis -- 2.3.2.1 Big Data Algorithms and Challenges -- 2.3.2.2 Machine Learning Process and Challenges -- 2.3.2.3 Deep Learning Process and Challenges -- 2.3.2.4 Learning Process and Emerging New Type of Data Problems -- 2.3.3 Decision‐Making Problems in Smart Cities -- 2.3.3.1 Traffic Decision‐Making System -- 2.3.3.2 Safe and Smart Environment -- 2.4 Conclusion -- References -- Chapter 3 Embodied AI‐Driven Operation of Smart Cities: A Concise Review -- 3.1 Introduction -- 3.2 Rise of the Embodied AI -- 3.3 Breakdown of Embodied AI -- 3.3.1 Language Grounding -- 3.3.2 Language Plus Vision -- 3.3.3 Embodied Visual Recognition -- 3.3.4 Embodied Question Answering -- 3.3.5 Interactive Question Answering -- 3.3.6 Multi‐agent Systems -- 3.4 Simulators -- 3.4.1 MINOS -- 3.4.2 Habitat -- 3.5 Future of Embodied AI -- 3.5.1 Higher Intelligence -- 3.5.2 Evolution -- 3.6 Conclusion -- References -- Chapter 4 Analysis of Different Regression Techniques for Battery Capacity Prediction -- 4.1 Introduction -- 4.2 Data Preparation -- 4.2.1 Dataset -- 4.2.2 Feature Extraction -- 4.2.3 Noise Addition -- 4.3 Experiment Design and Machine Learning Algorithms -- 4.4 Result and Analysis -- 4.5 Threats to Validity -- 4.6 Conclusions…”