This book constitutes - in conjunction with the two-volume set LNCS 10954 and LNCS 10955 - the refereed proceedings of the 14th International Conference on Intelligent Computing, ICIC 2018, held in Wuhan, China, in August 2018. The 275 full papers and 72 short papers of the three proceedings volumes were carefully reviewed and selected from 632 submissions. The papers are organized in topical sections such as Evolutionary Computation and Learning; Neural Networks; Pattern Recognition; Image Processing; Information Security; Virtual Reality and Human-Computer Interaction; Business Intelligence and Multimedia Technology; Biomedical Informatics Theory and Methods; Swarm Intelligence and Optimization; Natural Computing; Quantum Computing; Intelligent Computing in Computer Vision; Fuzzy Theory and Algorithms; Machine Learning; Systems Biology; Intelligent Systems and Applications for Bioengineering; Evolutionary Optimization: Foundations and Its Applications to Intelligent Data Analytics; Swarm Evolutionary Algorithms for Scheduling and Combinatorial Optimization; Swarm Intelligence and Applications in Combinatorial Qoptimization; Advances in Metaheuristic Optimization Algorithm; Advances in Image Processing and Pattern Techniques; Bioinformatics.

These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: exploiting subprograms in genetic programming, schema frequencies in GP, Accessible AI, GP for Big Data, lexicase selection, symbolic regression techniques, co-evolution of GP and LCS, and applying ecological principles to GP. It also covers several chapters on best practices and lessons learned from hands-on experience. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.

This book constitutes the proceedings of the 23rd Ada-Europe International Conference on Reliable Software Technologies, Ada-Europe 2018, held in Lisbon, Portugal, in June 2018. The 10 papers presented in this volume were carefully reviewed and selected from 27 submissions. They were organized in topical sections named: safety and security; Ada 202X; handling implicit overhead; real-time scheduling; and new application domains.

This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification.

This book presents practical optimization techniques used in image processing and computer vision problems. Ill-posed problems are introduced and used as examples to show how each type of problem is related to typical image processing and computer vision problems. Unconstrained optimization gives the best solution based on numerical minimization of a single, scalar-valued objective function or cost function. Unconstrained optimization problems have been intensively studied, and many algorithms and tools have been developed to solve them. Most practical optimization problems, however, arise with a set of constraints. Typical examples of constraints include: (i) pre-specified pixel intensity range, (ii) smoothness or correlation with neighboring information, (iii) existence on a certain contour of lines or curves, and (iv) given statistical or spectral characteristics of the solution. Regularized optimization is a special method used to solve a class of constrained optimization problems. The term regularization refers to the transformation of an objective function with constraints into a different objective function, automatically reflecting constraints in the unconstrained minimization process. Because of its simplicity and efficiency, regularized optimization has many application areas, such as image restoration, image reconstruction, optical flow estimation, etc. Optimization plays a major role in a wide variety of theories for image processing and computer vision. Various optimization techniques are used at different levels for these problems, and this volume summarizes and explains these techniques as applied to image processing and computer vision.

This three volume set (CCIS 853-855) constitutes the proceedings of the 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2017, held in Cadiz, Spain, in June 2018. The 193 revised full papers were carefully reviewed and selected from 383 submissions. The papers are organized in topical sections on advances on explainable artificial intelligence; aggregation operators, fuzzy metrics and applications; belief function theory and its applications; current techniques to model, process and describe time series; discrete models and computational intelligence; formal concept analysis and uncertainty; fuzzy implication functions; fuzzy logic and artificial intelligence problems; fuzzy mathematical analysis and applications; fuzzy methods in data mining and knowledge discovery; fuzzy transforms: theory and applications to data analysis and image processing; imprecise probabilities: foundations and applications; mathematical fuzzy logic, mathematical morphology; measures of comparison and entropies for fuzzy sets and their extensions; new trends in data aggregation; pre-aggregation functions and generalized forms of monotonicity; rough and fuzzy similarity modelling tools; soft computing for decision making in uncertainty; soft computing in information retrieval and sentiment analysis; tri-partitions and uncertainty; decision making modeling and applications; logical methods in mining knowledge from big data; metaheuristics and machine learning; optimization models for modern analytics; uncertainty in medicine; uncertainty in Video/Image Processing (UVIP).

This three volume set (CCIS 853-855) constitutes the proceedings of the 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2017, held in Cadiz, Spain, in June 2018. The 193 revised full papers were carefully reviewed and selected from 383 submissions. The papers are organized in topical sections on advances on explainable artificial intelligence; aggregation operators, fuzzy metrics and applications; belief function theory and its applications; current techniques to model, process and describe time series; discrete models and computational intelligence; formal concept analysis and uncertainty; fuzzy implication functions; fuzzy logic and artificial intelligence problems; fuzzy mathematical analysis and applications; fuzzy methods in data mining and knowledge discovery; fuzzy transforms: theory and applications to data analysis and image processing; imprecise probabilities: foundations and applications; mathematical fuzzy logic, mathematical morphology; measures of comparison and entropies for fuzzy sets and their extensions; new trends in data aggregation; pre-aggregation functions and generalized forms of monotonicity; rough and fuzzy similarity modelling tools; soft computing for decision making in uncertainty; soft computing in information retrieval and sentiment analysis; tri-partitions and uncertainty; decision making modeling and applications; logical methods in mining knowledge from big data; metaheuristics and machine learning; optimization models for modern analytics; uncertainty in medicine; uncertainty in Video/Image Processing (UVIP).

This three volume set (CCIS 853-855) constitutes the proceedings of the 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2017, held in Cadiz, Spain, in June 2018. The 193 revised full papers were carefully reviewed and selected from 383 submissions. The papers are organized in topical sections on advances on explainable artificial intelligence; aggregation operators, fuzzy metrics and applications; belief function theory and its applications; current techniques to model, process and describe time series; discrete models and computational intelligence; formal concept analysis and uncertainty; fuzzy implication functions; fuzzy logic and artificial intelligence problems; fuzzy mathematical analysis and applications; fuzzy methods in data mining and knowledge discovery; fuzzy transforms: theory and applications to data analysis and image processing; imprecise probabilities: foundations and applications; mathematical fuzzy logic, mathematical morphology; measures of comparison and entropies for fuzzy sets and their extensions; new trends in data aggregation; pre-aggregation functions and generalized forms of monotonicity; rough and fuzzy similarity modelling tools; soft computing for decision making in uncertainty; soft computing in information retrieval and sentiment analysis; tri-partitions and uncertainty; decision making modeling and applications; logical methods in mining knowledge from big data; metaheuristics and machine learning; optimization models for modern analytics; uncertainty in medicine; uncertainty in Video/Image Processing (UVIP).

This book provides a general overview of multiple instance learning (MIL), defining the framework and covering the central paradigms. The authors discuss the most important algorithms for MIL such as classification, regression and clustering. With a focus on classification, a taxonomy is set and the most relevant proposals are specified. Efficient algorithms are developed to discover relevant information when working with uncertainty. Key representative applications are included. This book carries out a study of the key related fields of distance metrics and alternative hypothesis. Chapters examine new and developing aspects of MIL such as data reduction for multi-instance problems and imbalanced MIL data. Class imbalance for multi-instance problems is defined at the bag level, a type of representation that utilizes ambiguity due to the fact that bag labels are available, but the labels of the individual instances are not defined. Additionally, multiple instance multiple label learning is explored. This learning framework introduces flexibility and ambiguity in the object representation providing a natural formulation for representing complicated objects. Thus, an object is represented by a bag of instances and is allowed to have associated multiple class labels simultaneously. This book is suitable for developers and engineers working to apply MIL techniques to solve a variety of real-world problems. It is also useful for researchers or students seeking a thorough overview of MIL literature, methods, and tools.

This book presents the theoretical details and computational performances of algorithms used for solving continuous nonlinear optimization applications imbedded in GAMS. Aimed toward scientists and graduate students who utilize optimization methods to model and solve problems in mathematical programming, operations research, business, engineering, and industry, this book enables readers with a background in nonlinear optimization and linear algebra to use GAMS technology to understand and utilize its important capabilities to optimize algorithms for modeling and solving complex, large-scale, continuous nonlinear optimization problems or applications. Beginning with an overview of constrained nonlinear optimization methods, this book moves on to illustrate key aspects of mathematical modeling through modeling technologies based on algebraically oriented modeling languages. Next, the main feature of GAMS, an algebraically oriented language that allows for high-level algebraic representation of mathematical optimization models, is introduced to model and solve continuous nonlinear optimization applications. More than 15 real nonlinear optimization applications in algebraic and GAMS representation are presented which are used to illustrate the performances of the algorithms described in this book. Theoretical and computational results, methods, and techniques effective for solving nonlinear optimization problems, are detailed through the algorithms MINOS, KNITRO, CONOPT, SNOPT and IPOPT which work in GAMS technology.

The two-volume set LNAI 10841 and LNAI 10842 constitutes the refereed proceedings of the 17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018, held in Zakopane, Poland in June 2018. The 140 revised full papers presented were carefully reviewed and selected from 242 submissions. The papers included in the first volume are organized in the following three parts: neural networks and their applications; evolutionary algorithms and their applications; and pattern classification.

This book constitutes the thoroughly refereed revised selected papers of the 10th International Conference on Bioinspired Optimization Models and Their Applications, BIOMA 2018, held in Paris, France, in May 2018. The 27 revised full papers were selected from 53 submissions and present papers in all aspects of bioinspired optimization research such as new algorithmic developments, high-impact applications, new research challenges, theoretical contributions, implementation issues, and experimental studies.

Philipp Meisen introduces a model, a query language, and a similarity measure enabling users to analyze time interval data. The introduced tools are combined to design and realize an information system. The presented system is capable of performing analytical tasks (avoiding any type of summarizability problems), providing insights, and visualizing results processing millions of intervals within milliseconds using an intuitive SQL-based query language. The heart of the solution is based on several bitmap-based indexes, which enable the system to handle huge amounts of time interval data.

This book constitutes the refereed proceedings of the 9th International Joint Conference on Automated Reasoning, IJCAR 2018, held in Oxford, United Kingdom, in July 2018, as part of the Federated Logic Conference, FLoC 2018. In 2018, IJCAR unites CADE, TABLEAUX, and FroCoS, the International Symposium on Frontiers of Combining Systems, and, for the fourth time, is part of the Federated Logic Conference. The 38 revised full research papers and 8 system descriptions presented together with two invited talks were carefully reviewed and selected from 108 submissions. The papers focus on topics such as logics, deductive systems, proof-search methods, theorem proving, model checking, verification, formal methods, and program analysis.

This book describes the computational challenges posed by the progression toward nanoscale electronic devices and increasingly short design cycles in the microelectronics industry, and proposes methods of model reduction which facilitate circuit and device simulation for specific tasks in the design cycle. The goal is to develop and compare methods for system reduction in the design of high dimensional nanoelectronic ICs, and to test these methods in the practice of semiconductor development. Six chapters describe the challenges for numerical simulation of nanoelectronic circuits and suggest model reduction methods for constituting equations. These include linear and nonlinear differential equations tailored to circuit equations and drift diffusion equations for semiconductor devices. The performance of these methods is illustrated with numerical experiments using real-world data. Readers will benefit from an up-to-date overview of the latest model reduction methods in computational nanoelectronics.

This book surveys the state-of-the-art in the theory of combinatorial games, that is games not involving chance or hidden information. Enthusiasts will find a wide variety of exciting topics, from a trailblazing presentation of scoring to solutions of three piece ending positions of bidding chess. Theories and techniques in many subfields are covered, such as universality, Wythoff Nim variations, misere play, partizan bidding (a.k.a. Richman games), loopy games, and the algebra of placement games. Also included are an updated list of unsolved problems, extremely efficient algorithms for taking and breaking games, a historical exposition of binary numbers and games by David Singmaster, chromatic Nim variations, renormalization for combinatorial games, and a survey of temperature theory by Elwyn Berlekamp, one of the founders of the field. The volume was initiated at the Combinatorial Game Theory Workshop, January 2011, held at the Banff International Research Station.

This book constitutes the refereed proceedings of the 6th International Conference on Abstract State Machines, Alloy, B, TLA, VDM, and Z, ABZ 2016, held in Southampton, UK, in June 2018. The 20 full and 11 short papers presented in this volume were carefully reviewed and selected from 60 submissions. They record the latest research developments in state-based formal methods Abstract State Machines, Alloy, B, Circus, Event-B, TLS+, VDM and Z.

This book constitutes the proceedings of the 13th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2018, held in Tianjin, China, in June 2018. The 59 full papers and 18 short papers presented in this book were carefully reviewed and selected from 197 submissions. The papers cover various topics such as cognitive radio networks; wireless sensor networks; cyber-physical systems; distributed and localized algorithm design and analysis; information and coding theory for wireless networks; localization; mobile cloud computing; topology control and coverage; security and privacy; underwater and underground networks; vehicular networks; internet of things; information processing and data management; programmable service interfaces; energy-efficient algorithms; system and protocol design; operating system and middle-ware support; and experimental test-beds, models and case studies.

This proceedings volume contains selected papers that were presented in the 3rd International Symposium on Big data and Cloud Computing Challenges, 2016 held at VIT University, India on March 10 and 11. New research issues, challenges and opportunities shaping the future agenda in the field of Big Data and Cloud Computing are identified and presented throughout the book, which is intended for researchers, scholars, students, software developers and practitioners working at the forefront in their field. This book acts as a platform for exchanging ideas, setting questions for discussion, and sharing the experience in Big Data and Cloud Computing domain.

This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various analysis methods for the convergence, stabilizing, self-stabilizing property of algorithms, and introduces the deterministic discrete-time systems method to analyze the convergence of PCA/MCA algorithms. Readers should be familiar with numerical analysis and the fundamentals of statistics, such as the basics of least squares and stochastic algorithms. Although it focuses on neural networks, the book only presents their learning law, which is simply an iterative algorithm. Therefore, no a priori knowledge of neural networks is required. This book will be of interest and serve as a reference source to researchers and students in applied mathematics, statistics, engineering, and other related fields.

This book provides a comprehensive overview of the field of pattern mining with evolutionary algorithms. To do so, it covers formal definitions about patterns, patterns mining, type of patterns and the usefulness of patterns in the knowledge discovery process. As it is described within the book, the discovery process suffers from both high runtime and memory requirements, especially when high dimensional datasets are analyzed. To solve this issue, many pruning strategies have been developed. Nevertheless, with the growing interest in the storage of information, more and more datasets comprise such a dimensionality that the discovery of interesting patterns becomes a challenging process. In this regard, the use of evolutionary algorithms for mining pattern enables the computation capacity to be reduced, providing sufficiently good solutions. This book offers a survey on evolutionary computation with particular emphasis on genetic algorithms and genetic programming. Also included is an analysis of the set of quality measures most widely used in the field of pattern mining with evolutionary algorithms. This book serves as a review of the most important evolutionary algorithms for pattern mining. It considers the analysis of different algorithms for mining different type of patterns and relationships between patterns, such as frequent patterns, infrequent patterns, patterns defined in a continuous domain, or even positive and negative patterns. A completely new problem in the pattern mining field, mining of exceptional relationships between patterns, is discussed. In this problem the goal is to identify patterns which distribution is exceptionally different from the distribution in the complete set of data records. Finally, the book deals with the subgroup discovery task, a method to identify a subgroup of interesting patterns that is related to a dependent variable or target attribute. This subgroup of patterns satisfies two essential conditions: interpretability and interestingness.

This reference and handbook describes theory, algorithms and applications of the Global Positioning System (GPS/Glonass/Galileo/Compass). It is primarily based on source-code descriptions of the KSGsoft program developed at the GFZ in Potsdam. The theory and algorithms are extended and verified for a new development of a multi-functional GPS/Galileo software. Besides the concepts such as the unified GPS data processing method, the diagonalisation algorithm, the adaptive Kalman filter, the general ambiguity search criteria, and the algebraic solution of variation equation reported in the first edition, the equivalence theorem of the GPS algorithms, the independent parameterisation method, and the alternative solar radiation model reported in the second edition, the modernisation of the GNSS system, the new development of the theory and algorithms, and research in broad applications are supplemented in this new edition. Mathematically rigorous, the book begins with the introduction, the basics of coordinate and time systems and satellite orbits, as well as GPS observables, and deals with topics such as physical influences, observation equations and their parameterisation, adjustment and filtering, ambiguity resolution, software development and data processing and the determination of perturbed orbits.

Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R. You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers. You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence. What You Will Learn Get an in-depth understanding of all the major machine learning and deep learning algorithms Fully appreciate the pitfalls to avoid while building models Implement machine learning algorithms in the cloud Follow a hands-on approach through case studies for each algorithm Gain the tricks of ensemble learning to build more accurate models Discover the basics of programming in R/Python and the Keras framework for deep learning Who This Book Is For Business analysts/ IT professionals who want to transition into data science roles. Data scientists who want to solidify their knowledge in machine learning.

The control-flow issues presented in this textbook are extremely relevant in modern computer languages and programming styles. In addition to the basic control-flow mechanisms, virtually all new computer languages provide some form of exceptional control flow to support robust programming introduced in this textbook. Also, concurrency capabilities are appearing with increasing frequency in both new and old programming languages, and are covered in this book. Understanding Control Flow: With Concurrent Programming Using C++ starts with looping, and works through each of the basic control-flow concepts, examining why each is fundamental and where it is useful. Time is spent on each concept according to its level of difficulty. Examples and exercises are also provided in this textbook. New programming methodologies are requiring new forms of control flow, and new programming languages are supporting these methodologies with new control structures, such as the concurrency constructs discussed in this textbook. Most computers now contain multi-threading and multi-cores, while multiple processors and distributed systems are ubiquitous - all of which require advanced programming methodologies to take full advantage of the available parallelism summarized in this textbook. Advance forms of control flow are becoming basic programming skills needed by all programmers, not just graduate students working in the operating systems or database disciplines. This textbook is designed for advanced-level students studying computer science and engineering. Professionals and researchers working in this field, specifically programming and software engineering, will find this book useful as a reference.

This stimulating textbook presents a broad and accessible guide to the fundamentals of discrete mathematics, highlighting how the techniques may be applied to various exciting areas in computing. The text is designed to motivate and inspire the reader, encouraging further study in this important skill. Features: provides an introduction to the building blocks of discrete mathematics, including sets, relations and functions; describes the basics of number theory, the techniques of induction and recursion, and the applications of mathematical sequences, series, permutations, and combinations; presents the essentials of algebra; explains the fundamentals of automata theory, matrices, graph theory, cryptography, coding theory, language theory, and the concepts of computability and decidability; reviews the history of logic, discussing propositional and predicate logic, as well as advanced topics; examines the field of software engineering, describing formal methods; investigates probability and statistics.