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This book is a collection of insightful and unique state-of the-art papers presented at the Computing Conference which took place in London on June 22–23, 2023. A total of 539 papers were received out of which 193 were selected for presenting after double-blind peer-review. The book covers a wide range of scientific topics including IoT, Artificial Intelligence, Computing, Data Science, Networking, Data security and Privacy, etc. The conference was successful in reaping the advantages of both online and offline modes. The goal of this conference is to give a platform to researchers with fundamental contributions and to be a premier venue for academic and industry practitioners to share new ideas and development experiences. We hope that readers find this book interesting and valuable. We also expect that the conference and its publications will be a trigger for further related research and technology improvements in this important subject.

This book constitutes the proceedings of the 20th International Conference on Web Information Systems and Applications, WISA 2023, held in Chengdu, China, in September 2023. The 43 full papers and 9 short papers presented in this book were carefully reviewed and selected from 213 submissions. The papers are grouped in topical sections on Data Mining and Knowledge Discovery, Recommender Systems, Natural Language Processing, Security, Privacy and Trust, Blockchain, Parallel and Distributed Systems and Database for Artificial Intelligence..

This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.

This Open-Access-book addresses the issue of translating mathematical expressions from LaTeX to the syntax of Computer Algebra Systems (CAS). Over the past decades, especially in the domain of Sciences, Technology, Engineering, and Mathematics (STEM), LaTeX has become the de-facto standard to typeset mathematical formulae in publications. Since scientists are generally required to publish their work, LaTeX has become an integral part of today's publishing workflow. On the other hand, modern research increasingly relies on CAS to simplify, manipulate, compute, and visualize mathematics. However, existing LaTeX import functions in CAS are limited to simple arithmetic expressions and are, therefore, insufficient for most use cases. Consequently, the workflow of experimenting and publishing in the Sciences often includes time-consuming and error-prone manual conversions between presentational LaTeX and computational CAS formats. To address the lack of a reliable and comprehensive translation tool between LaTeX and CAS, this thesis makes the following three contributions. First, it provides an approach to semantically enhance LaTeX expressions with sufficient semantic information for translations into CAS syntaxes. Second, it demonstrates the first context-aware LaTeX to CAS translation framework LaCASt. Third, the thesis provides a novel approach to evaluate the performance for LaTeX to CAS translations on large-scaled datasets with an automatic verification of equations in digital mathematical libraries. This is an open access book.

This proceedings presents the result of the 8th International Conference in Network Analysis, held at the Higher School of Economics, Moscow, in May 2018. The conference brought together scientists, engineers, and researchers from academia, industry, and government. Contributions in this book focus on the development of network algorithms for data mining and its applications. Researchers and students in mathematics, economics, statistics, computer science, and engineering find this collection a valuable resource filled with the latest research in network analysis. Computational aspects and applications of large-scale networks in market models, neural networks, social networks, power transmission grids, maximum clique problem, telecommunication networks, and complexity graphs are included with new tools for efficient network analysis of large-scale networks. Machine learning techniques in network settings including community detection, clustering, and biclustering algorithms are presented with applications to social network analysis.

Civic virtues were central to early modern Nürnberg’s visual culture. These essays explore Nürnberg as a location from which to study the intersection of art and power. The imperial city was awash in emblems, and they informed most aspects of everyday life. The intent of this volume is to focus new attention on the town hall emblems, while simultaneously expanding the purview of emblem studies, moving from strict iconological approaches to collaborations across methodologies and disciplines.