Lecture Notes in Computer Science Series

The LNCS volume 10996 constitutes the proceedings of the 13th Chinese Conference on Biometric Recognition, held in Urumchi, China, in August 2018.   The 79 regular papers presented in this book were carefully reviewed and selected from 112 submissions. The papers cover a wide range of topics such as Biometrics, Speech recognition, Activity recognition and understanding, Online handwriting recognition, System forensics, Multi-factor authentication, Graphical and visual passwords.

This book constitutes the proceedings of the 16th International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2019, held in Thessaloniki, Greece, in June 2019. The 34 full papers presented together with 9 short papers were carefully reviewed and selected from 94 submissions. The conference brings together interested researchers from Constraint Programming (CP), Artificial Intelligence (AI), and Operations Research (OR) to present new techniques or applications and to provide an opportunity for researchers in one area to learn about techniques in the others. A main objective of this conference series is also to give these researchers the opportunity to show how the integration of techniques from different fields can lead to interesting results on large and complex problems.

The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.

This book constitutes the refereed proceedings of the 23rd Symposium on Formal Methods, FM 2019, held in Porto, Portugal, in the form of the Third World Congress on Formal Methods, in October 2019. The 44 full papers presented together with 3 invited presentations were carefully reviewed and selected from 129 submissions. The papers are organized in topical sections named: Invited Presentations; Verification; Synthesis Techniques; Concurrency; Model Checking Circus; Model Checking; Analysis Techniques; Specification Languages; Reasoning Techniques; Modelling Languages; Learning-Based Techniques and Applications; Refactoring and Reprogramming; I-Day Presentations.      

This volume was published in honor of Stefania Gnesi’s 65th birthday. The Festschrift volume contains 32 papers written by close collaborators and friends of Stefania and was presented to her on October 8, 2019 one-day colloquium held in Porto, Portugal, The Festschrift consists of eight sections, seven of which reflect the main research areas to which Stefania has contributed. Following a survey of Stefania's legacy in research and a homage by her thesis supervisor, these seven sections are ordered according to Stefania's life cycle in research, from software engineering to formal methods and tools, and back: Software Engineering; Formal Methods and Tools; Requirements Engineering; Natural Language Processing; Software Product Lines; Formal Verification; and Applications.

This book constitutes the refereed proceedings of the 13th International Conference on Artificial General Intelligence, AGI 2020, held in St. Petersburg, Russia, in September 2020. The 30 full papers and 8 short papers presented in this book were carefully reviewed and selected from 60 submissions. The papers cover topics such as AGI architectures, artificial creativity and AI safety, transfer learning, AI unification and benchmarks for AGI.

This book constitutes the proceedings of the 14th International Conference on Language and Automata Theory and Applications, LATA 2020, which was planned to be held in Milan, Italy, in March 2020. Due to the corona pandemic, the actual conference was postponed and will be held together with LATA 2021. The 26 full papers presented in this volume were carefully reviewed and selected from 59 submissions. They were organized in topical sections named: algebraic structures; automata; complexity; grammars; languages; trees and graphs; and words and codes. The book also contains 6 invited papers in full-paper length. 

This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”  

The two volume set, LNCS 12308 + 12309, constitutes the proceedings of the 25th European Symposium on Research in Computer Security, ESORICS 2020, which was held in September 2020. The conference was planned to take place in Guildford, UK. Due to the COVID-19 pandemic, the conference changed to an online format. The total of 72 full papers included in these proceedings was carefully reviewed and selected from 366 submissions. The papers were organized in topical sections named: database and Web security; system security; network security; software security; machine learning security; privacy; formal modelling; applied cryptography; analyzing attacks; post-quantum cryptogrphy; security analysis; and blockchain.