This book provides an introduction to reciprocal recommendation. It starts with theory, and then moves on to concrete examples of the most successful algorithms in the field. Researchers and developers with a little background in machine learning will find many of the algorithms are straightforward to implement, and code samples are included to help with this.
In addition to accessible algorithms, the book also examines some more cutting-edge research such as the recent interest in applying matching theory to reciprocal recommendation. These parts will be of interest both to...
This book provides an introduction to reciprocal recommendation. It starts with theory, and then moves on to concrete examples of the most successf...
Moving Target Defense (MTD) has been proposed as an innovative defense framework, which aims to introduce the dynamics, diversity and randomization into static network by the shuffling, heterogeneity and redundancy. It is born to solve the problem that traditional security solutions respond and defend against security threats after attacks occurrence, which will lead to the defender always having disadvantages in attack-defense confrontation. This book explores the challenges and solutions related to moving target defense in the cloud-edge-terminal networks.
This book fills this gap...
Moving Target Defense (MTD) has been proposed as an innovative defense framework, which aims to introduce the dynamics, diversity and randomization...
This book is divided into five chapters. Chapter 1 introduces the background of the research, the content positioning, and some related open source resources. Chapter 2 discusses some benchmarking issues related to the field of embodied intelligence and mobile robotics. Chapter 3 introduces robot perception, especially the object detection and tracking based on 3D lidar with contemporary characteristics. Chapter 4 introduces robot learning, especially robot online learning methods with strong embodied intelligence features. Chapter 5 summarizes the book and provides prospects for future...
This book is divided into five chapters. Chapter 1 introduces the background of the research, the content positioning, and some related open source...
Federated Learning for Smart Mobility: Towards Secure, Efficient, and Sustainable Transportation explores how federated learning (FL) reshapes the future of intelligent transportation and the Internet of Things (IoT). As data privacy and communication efficiency become pressing challenges, FL offers a distributed and privacy-preserving paradigm for model training across vehicles, sensors, and edge devices without sharing raw data.
This SpringerBrief provides a concise yet comprehensive overview of FL s role in building next-generation smart mobility systems. It covers the...
Federated Learning for Smart Mobility: Towards Secure, Efficient, and Sustainable Transportation explores how federated learning (FL) resh...
