The Association for Computing Machinery (ACM) is the world’s largest computing society, providing a global platform for research, collaboration, and innovation in computing.
Its ACM Books series covers a broad spectrum of computer science topics, appealing to practitioners, researchers, educators, and students alike. The series includes four key types of publications: graduate-level textbooks, research monographs, practitioner-level professional guides, and works exploring the history and societal impact of computing.
New and Notable Titles
Digital Dreams Have Become Nightmares: What We Must Do
This book offers a compelling discussion of the digital dreams that have come true, their often unintended side effects (nightmares), and what must be done to counteract the nightmares. It is intended as an impetus to further conversation not only in homes and workplaces, but in academic courses and even legislative debates. Equally importantly, the book is a presentation of what digital technology professionals need to know about these topics and the actions they should undertake individually and in support of other citizens, societal initiatives, and government. The book closes with a positive call to action, outlining ways to address the challenges through ethical career choices, careful analysis, thoughtful design, research, citizen engagement, legislation/regulation, and careful consideration of how bad actors may use technology. Readers of Digital Dreams Have Become Nightmares should become more knowledgeable, wiser, and also cautiously optimistic, determined to affect positive changes through their design, creation, and use of technology.
Formal Methods for Safe Autonomy: Data-driven Verification, Synthesis, and Applications
There are significant financial and legal implications for ensuring design correctness and safety in autonomous systems. This book introduces new verification and synthesis algorithms to provide certifiable trusts for real-world autonomous systems. On the theoretical front, the techniques are armed with soundness, precision, and relative completeness guarantees. On the experimental side, this book shows that techniques can be successfully applied on a sequence of real-world problems, including a suite of Toyota engine control modules verified for the first time, satellite control systems, and autonomous driving and ADAS-based maneuvers.
Insights throughout the book provide a level of assurance that can be provided by formal methods for today’s autonomous systems. Verification and synthesis for typical models of real-world autonomous systems are challenging due to their high dimensionality, nonlinearities, and nondeterministic and hybrid nature. In addressing these challenges, several chapters present data-driven algorithmic verification via reachability analysis of complex hybrid systems as well as controller synthesis for dynamic systems under disturbance.
Information Retrieval
In the last decade, deep learning and word embeddings have made significant impacts on information retrieval (IR) by adding techniques based in neural networks and language models. At the same time, certain search modalities such as neural IR and conversational search have become more popular. This book, written by international academic and industry experts, brings the field up to date with detailed discussions of these new approaches and techniques. The book is organized in three sections: Foundations, Adaptations and Concerns, and Verticals.
