This edited book presents scientific results of the 20th IEEE/ACIS International Summer Semi-Virtual Conference on Computer and Information Science (ICIS 2021) held on June 23–25, 2021 in Shanghai, China. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The conference organizers selected the best papers from those papers accepted for presentation at the conference. The papers were chosen based on review scores submitted by members of the program committee and underwent further rigorous rounds of review. From this second round of review, 13 of the conference’s most promising papers are then published in this Springer (SCI) book and not the conference proceedings. We impatiently await the important contributions that we know these authors will bring to the field of computer and information science.

In an age defined by unparalleled technological advancements, globalization, and the looming specter of environmental and societal crises, the need for a holistic and sustainable approach to accounting practices has never been more pressing. Academic scholars stand witness to the challenges posed by the new era, characterized by transformative shifts across industry, education, community, and society at large. These shifts, driven by rapid advancements in Artificial Intelligence (AI), present a double-edged sword. While AI offers unprecedented opportunities for innovation, it also amplifies the urgency of addressing sustainability concerns. Today's society grapples with the immense responsibility of achieving the Sustainable Development Goals (SDGs) outlined in Agenda 2030. It is imperative to not only understand but harness the power of AI to drive sustainability, enhance the quality of life, and ensure sustainable growth on both local and global scales. Artificial Intelligence Approaches to Sustainable Accounting serves as a beacon of knowledge, providing a comprehensive exploration of the intersection between AI, accounting, and sustainability. This book represents a vital solution to the challenges faced by academic scholars and practitioners alike. Within its pages lies a transdisciplinary approach that bridges the gap between these critical fields. Discover how AI can elevate accounting to new heights, extending the spectrum of information in organizational decision-making, promoting responsible reporting practices, and bolstering sustainable practices worldwide. This book not only reviews governance and management processes but also offers practical methodologies that empower organizations to embrace sustainability wholeheartedly.

The application of the theory and practice of art to computer science: how aesthetics and art can play a role in computing disciplines.

This edited book presents scientific results of the 1st ACIS International Symposium on Emotional Artificial Intelligence & Metaverse (EAIM) which was held on August 4-6, 2022, in Danang, Vietnam. The aim of this symposium was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. All aspects (theory, applications, and tools) of emotional artificial intelligence and metaverse, the practical challenges encountered along the way, and the solutions adopted to solve them are all explored here in the results of the articles featured in this book. The symposium organizers selected the best papers from those papers accepted for presentation at the symposium. The papers were chosen based on review scores submitted by members of the program committee and underwent further rigorous rounds of review. From this second round of review, 15 of the symposium’s most promising papers are then published in this Springer (SCI) book and not the symposium proceedings. We impatiently await the important contributions that we know these authors will bring to the field of emotional artificial intelligence and metaverse.

Make sure you’re studying with the most up-to-date prep materials! Look for the newest edition of this title, The Princeton Review AP Computer Science A Prep, 2023 (ISBN: 9780593450727, on-sale September 2020). Publisher's Note: Products purchased from third-party sellers are not guaranteed by the publisher for quality or authenticity, and may not include access to online tests or materials included with the original product.

Computer vision has made enormous progress in recent years, and its applications are multifaceted and growing quickly, while many challenges still remain. This book brings together a range of leading researchers to examine a wide variety of research directions, challenges, and prospects for computer vision and its applications. This book highlights various core challenges as well as solutions by leading researchers in the field. It covers such important topics as data-driven AI, biometrics, digital forensics, healthcare, robotics, entertainment and XR, autonomous driving, sports analytics, and neuromorphic computing, covering both academic and industry R&D perspectives. Providing a mix of breadth and depth, this book will have an impact across the fields of computer vision, imaging, and AI. Computer Vision: Challenges, Trends, and Opportunities covers timely and important aspects of computer vision and its applications, highlighting the challenges ahead and providing a range of perspectives from top researchers around the world. A substantial compilation of ideas and state-of-the-art solutions, it will be of great benefit to students, researchers, and industry practitioners.

Advances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method. This book covers recent concrete information, from the basics to advanced level, about graph-based learning, such as neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NPL. It also contains information about language generation based on graphical theories and language models. Features: Presents a comprehensive study of the interdisciplinary graphical approach to NLP Covers recent computational intelligence techniques for graph-based neural network models Discusses advances in random walk-based techniques, semantic webs, and lexical networks Explores recent research into NLP for graph-based streaming data Reviews advances in knowledge graph embedding and ontologies for NLP approaches This book is aimed at researchers and graduate students in computer science, natural language processing, and deep and machine learning.