This open access book gives an overview of cutting-edge work on a new paradigm called the “sublinear computation paradigm,” which was proposed in the large multiyear academic research project “Foundations of Innovative Algorithms for Big Data.” That project ran from October 2014 to March 2020, in Japan. To handle the unprecedented explosion of big data sets in research, industry, and other areas of society, there is an urgent need to develop novel methods and approaches for big data analysis. To meet this need, innovative changes in algorithm theory for big data are being pursued. For example, polynomial-time algorithms have thus far been regarded as “fast,” but if a quadratic-time algorithm is applied to a petabyte-scale or larger big data set, problems are encountered in terms of computational resources or running time. To deal with this critical computational and algorithmic bottleneck, linear, sublinear, and constant time algorithms are required. The sublinear computation paradigm is proposed here in order to support innovation in the big data era. A foundation of innovative algorithms has been created by developing computational procedures, data structures, and modelling techniques for big data. The project is organized into three teams that focus on sublinear algorithms, sublinear data structures, and sublinear modelling. The work has provided high-level academic research results of strong computational and algorithmic interest, which are presented in this book. The book consists of five parts: Part I, which consists of a single chapter on the concept of the sublinear computation paradigm; Parts II, III, and IV review results on sublinear algorithms, sublinear data structures, and sublinear modelling, respectively; Part V presents application results. The information presented here will inspire the researchers who work in the field of modern algorithms.

In the evolving environment of bioinformatics, genomics, and computational biology, academic scholars are facing a challenging challenge – keeping informed about the latest research trends and findings. With unprecedented advancements in sequencing technologies, computational algorithms, and machine learning, these fields have become indispensable tools for drug discovery, disease research, genome sequencing, and more. As scholars strive to decode the language of DNA, predict protein structures, and navigate the complexities of biological data analysis, the need for a comprehensive and up-to-date resource becomes paramount. The Research Anthology on Bioinformatics, Genomics, and Computational Biology is a collection of a carefully curated selection of chapters that serves as the solution to the pressing challenge of keeping pace with the dynamic advancements in these critical disciplines. This anthology is designed to address the informational gap by providing scholars with a consolidated and authoritative source that sheds light on critical issues, innovative theories, and transformative developments in the field. It acts as a single reference point, offering insights into conceptual, methodological, technical, and managerial issues while also providing a glimpse into emerging trends and future opportunities.

The 47 full papers and 24 short papers included in this book were carefully reviewed and selected from 245 submissions. These articles cater to the most contemporary and happening topics in the fields of AI that range from Intelligent Recommendation Systems, Game Theory, Computer Vision, Reinforcement Learning, Social Networks, and Generative AI to Conversational and Large Language Models. They are organized into four areas of research: Theoretical contributions, Cognitive Computing models, Computational Intelligence based algorithms, and AI Applications.

The International Conference on Security, Surveillance & Artificial Intelligence (ICSSAI2023) was held in West Bengal, India during December 1–2, 2023. The conference was organized by the Techno India University, one of the renowned universities in the state of West Bengal which is committed for generating, disseminating and preserving knowledge.

This book constitutes the refereed proceedings of the 8th International Conference on Decision and Game Theory for Security, GameSec 2017, held in Vienna, Austria, in October 2017. The 24 revised full papers presented together with 4 short papers were carefully reviewed and selected from 71 submissions.The papers address topics such as Game theory and mechanism design for security and privacy; Pricing and economic incentives for building dependable and secure systems; Dynamic control, learning, and optimization and approximation techniques; Decision making and decision theory for cybersecurity and security requirements engineering; Socio-technological and behavioral approaches to security; Risk assessment and risk management; Security investment and cyber insurance; Security and privacy for the Internet-of-Things (IoT), cyber-physical systems, resilient control systems; New approaches for security and privacy in cloud computing and for critical infrastructure; Security and privacy of wireless and mobile communications, including user location privacy; Game theory for intrusion detection; and Empirical and experimental studies with game-theoretic or optimization analysis for security and privacy.

The three-volume set LNCS 13747, LNCS 13748 and LNCS 13749 constitutes the refereed proceedings of the 20th International Conference on Theory of Cryptography, TCC 2022, held in Chicago, IL, USA, in November 2022. The total of 60 full papers presented in this three-volume set was carefully reviewed and selected from 139 submissions. They cover topics on post-quantum cryptography; interactive proofs; quantum cryptography; secret-sharing and applications; succinct proofs; identity-based encryption and functional encryption; attribute-based encryption and functional encryption; encryption; multi-party computation; protocols: key agreement and commitments; theory: sampling and friends; lattices; anonymity, verfiability and robustness; ORAM, OT and PIR; and theory.

New and classical results in computational complexity, including interactive proofs, PCP, derandomization, and quantum computation. Ideal for graduate students.

Post-Moore's law scaling is creating a disruptive shift in simulation workflows, as saving the entirety of raw data to persistent storage becomes expensive. We are moving away from a post-process centric data analysis paradigm towards a concurrent analysis framework, in which raw simulation data is processed as it is computed. Algorithms must adapt to machines with extreme concurrency, low communication bandwidth, and high memory latency, while operating within the time constraints prescribed by the simulation. Furthermore, in- put parameters are often data dependent and cannot always be prescribed. The study of sublinear algorithms is a recent development in theoretical computer science and discrete mathematics that has significant potential to provide solutions for these challenges. The approaches of sublinear algorithms address the fundamental mathematical problem of understanding global features of a data set using limited resources. These theoretical ideas align with practical challenges of in-situ and in-transit computation where vast amounts of data must be processed under severe communication and memory constraints. This report details key advancements made in applying sublinear algorithms in-situ to identify features of interest and to enable adaptive workflows over the course of a three year LDRD. Prior to this LDRD, there was no precedent in applying sublinear techniques to large-scale, physics based simulations. This project has definitively demonstrated their efficacy at mitigating high performance computing challenges and highlighted the rich potential for follow-on re- search opportunities in this space.

The big data tsunami changes the perspective of industrial and academic research in how they address both foundational questions and practical applications. This calls for a paradigm shift in algorithms and the underlying mathematical techniques. There is a need to understand foundational strengths and address the state of the art challenges in big data that could lead to practical impact. The main goal of this book is to introduce algorithmic techniques for dealing with big data sets. Traditional algorithms work successfully when the input data fits well within memory. In many recent application situations, however, the size of the input data is too large to fit within memory. Models of Computation for Big Data, covers mathematical models for developing such algorithms, which has its roots in the study of big data that occur often in various applications. Most techniques discussed come from research in the last decade. The book will be structured as a sequence of algorithmic ideas, theoretical underpinning, and practical use of that algorithmic idea. Intended for both graduate students and advanced undergraduate students, there are no formal prerequisites, but the reader should be familiar with the fundamentals of algorithm design and analysis, discrete mathematics, probability and have general mathematical maturity.

This book constitutes the thoroughly refereed proceedings of the 9th Theory of Cryptography Conference, TCC 2012, held in Taormina, Sicily, Italy, in March 2012. The 36 revised full papers presented were carefully reviewed and selected from 131 submissions. The papers are organized in topical sections on secure computation; (blind) signatures and threshold encryption; zero-knowledge and security models; leakage-resilience; hash functions; differential privacy; pseudorandomness; dedicated encryption; security amplification; resettable and parallel zero knowledge.