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This textbook is intended as a textbook for one-semester, introductory computer science courses aimed at undergraduate students from all disciplines. Self-contained and with no prerequisites, it focuses on elementary knowledge and thinking models. The content has been tested in university classrooms for over six years, and has been used in summer schools to train university and high-school teachers on teaching introductory computer science courses using computational thinking. This book introduces computer science from a computational thinking perspective. In computer science the way of thinking is characterized by three external and eight internal features, including automatic execution, bit-accuracy and abstraction. The book is divided into chapters on logic thinking, algorithmic thinking, systems thinking, and network thinking. It also covers societal impact and responsible computing material – from ICT industry to digital economy, from the wonder of exponentiation to wonder of cyberspace, and from code of conduct to best practices for independent work. The book's structure encourages active, hands-on learning using the pedagogic tool Bloom's taxonomy to create computational solutions to over 200 problems of varying difficulty. Students solve problems using a combination of thought experiment, programming, and written methods. Only 300 lines of code in total are required to solve most programming problems in this book.

Search structures support the fundamental data storage primitives on key-value pairs: insert a pair, delete by key, search by key, and update the value associated with a key. Concurrent search structures are parallel algorithms to speed access to search structures on multicore and distributed servers. These sophisticated algorithms perform fine-grained synchronization between threads, making them notoriously difficult to design correctly. Indeed, bugs have been found both in actual implementations and in the designs proposed by experts in peer-reviewed publications. The rapid development and deployment of these concurrent algorithms has resulted in a rift between the algorithms that can be verified by the state-of-the-art techniques and those being developed and used today. The goal of this book is to show how to bridge this gap in order to bring the certified safety of formal verification to high-performance concurrent search structures. Similar techniques and frameworks can be applied to concurrent graph and network algorithms beyond search structures.

The open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency.

This book is Open Access under a CC BY licence. The LNCS 10805 and 10806 proceedings set constitutes the proceedings of the 24th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2018, which took place in Thessaloniki, Greece, in April 2018, held as part of the European Joint Conference on Theory and Practice of Software, ETAPS 2018. The total of 43 full and 11 short papers presented in these volumes was carefully reviewed and selected from 154submissions. The papers are organized in topical sections as follows: Part I: theorem proving; SAT and SMT I; deductive verification; software verification and optimization; model checking; and machine learning. Part II: concurrent and distributed systems; SAT and SMT II; security and reactive systems; static and dynamic program analysis; hybrid and stochastic systems; temporal logic and mu-calculus; 7th Competition on Software Verification – SV-COMP.

This book is Open Access under a CC BY licence.The LNCS 10805 and 10806 proceedings set constitutes the proceedings of the 24th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2018, which took place in Thessaloniki, Greece, in April 2018, held as part of the European Joint Conference on Theory and Practice of Software, ETAPS 2018.The total of 43 full and 11 short papers presented in these volumes was carefully reviewed and selected from 154submissions. The papers are organized in topical sections as follows:Part I: theorem proving; SAT and SMT I; deductive verification; software verification and optimization; model checking; and machine learning. Part II: concurrent and distributed systems; SAT and SMT II; security and reactive systems; static and dynamic program analysis; hybrid and stochastic systems; temporal logic and mu-calculus; 7th Competition on Software Verification – SV-COMP.

Edited in collaboration with FoLLI, the Association of Logic, Language and Information this book constitutes the refereed proceedings of the 20th Workshop on Logic, Language, Information and Communication, WoLLIC 2013, held in Darmstadt, Germany, in August 2013. The 17 contributed papers presented together with 6 invited lectures were carefully reviewed and selected from 30 submissions. The scope of the workshop spans the theoretical and practical aspects of formal logic, computing and programming theory, and natural language and reasoning.

The use of mathematical logic as a formalism for artificial intelligence was recognized by John McCarthy in 1959 in his paper on Programs with Common Sense. In a series of papers in the 1960's he expanded upon these ideas and continues to do so to this date. It is now 41 years since the idea of using a formal mechanism for AI arose. It is therefore appropriate to consider some of the research, applications and implementations that have resulted from this idea. In early 1995 John McCarthy suggested to me that we have a workshop on Logic-Based Artificial Intelligence (LBAI). In June 1999, the Workshop on Logic-Based Artificial Intelligence was held as a consequence of McCarthy's suggestion. The workshop came about with the support of Ephraim Glinert of the National Science Foundation (IIS-9S2013S), the American Association for Artificial Intelligence who provided support for graduate students to attend, and Joseph JaJa, Director of the University of Maryland Institute for Advanced Computer Studies who provided both manpower and financial support, and the Department of Computer Science. We are grateful for their support. This book consists of refereed papers based on presentations made at the Workshop. Not all of the Workshop participants were able to contribute papers for the book. The common theme of papers at the workshop and in this book is the use of logic as a formalism to solve problems in AI.

The general problem addressed in this book is a large and important one: how to usefully deal with huge storehouses of complex information about real-world situations. Every one of the major modes of interacting with such storehouses – querying, data mining, data analysis – is addressed by current technologies only in very limited and unsatisfactory ways. The impact of a solution to this problem would be huge and pervasive, as the domains of human pursuit to which such storehouses are acutely relevant is numerous and rapidly growing. Finally, we give a more detailed treatment of one potential solution with this class, based on our prior work with the Probabilistic Logic Networks (PLN) formalism. We show how PLN can be used to carry out realworld reasoning, by means of a number of practical examples of reasoning regarding human activities inreal-world situations.