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Rebel Tongues: Dialects, Slang, and Fusion Languages in Global Literature
'Rebel Tongues' explores how dialects, slang, and hybrid languages shape identity, resist oppression, and bridge cultural divides in global literature. Drawing on works by authors like Chinua Achebe and Salman Rushdie, this book reveals how linguistic diversity challenges norms, empowers marginalized voices, and transforms literary narratives. A powerful examination of language as both a cultural tool and a form of resistance.
Federated Learning
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated lear...
Graph Representation Learning
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical sy...
Representation Learning for Natural Language Processing
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
Innovations in Bio-Inspired Computing and Applications
This book highlights recent research on bio-inspired computing and its various innovative applications in Information and Communication Technologies. It presents 50 high-quality papers from the 9th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2018) and 7th World Congress on Information and Communication Technologies (WICT 2018), which was held at Toc H Institute of Science and Technology (TIST) on December 17–19, 2018. IBICA-WICT 2018 was a premier conference and brought together researchers, engineers and practitioners whose work involved bio-inspired computing, computational intelligence and their applications in information security, real-world contexts etc. Including contributions by authors from 22 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering.
Robotics
See 'Artificial Intelligence & Robotics: 5 Overviews' for complete annotation.
Metric Learning
Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data....
Human Learning: Biology, Brain, and Neuroscience
Human learning is studied in a variety of ways. Motor learning is often studied separately from verbal learning. Studies may delve into anatomy vs function, may view behavioral outcomes or look discretely at the molecular and cellular level of learning. All have merit but they are dispersed across a wide literature and rarely are the findings integrated and synthesized in a meaningful way. Human Learning: Biology, Brain, and Neuroscience synthesizes findings across these levels and types of learning and memory investigation.Divided into three sections, each section includes a discussion by the editors integrating themes and ideas that emerge across the chapters within each section. Section 1...
