Maths for computer scientists Books

This textbook discusses the design and implementation of basic algebraic graph algorithms, and algebraic graph algorithms for complex networks, employing matroids whenever possible. The text describes the design of a simple parallel matrix algorithm kernel that can be used for parallel processing of algebraic graph algorithms. Example code is presented in pseudocode, together with case studies in Python and MPI. The text assumes readers have a background in graph theory and/or graph algorithms.

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Book Synopsis The greatly expanded and updated 3rd edition of this textbook offers the reader a comprehensive introduction to the concepts of logic functions and equations and their applications across computer science and engineering. The authors’ approach emphasizes a thorough understanding of the fundamental principles as well as numerical and computer-based solution methods. The book provides insight into applications across propositional logic, binary arithmetic, coding, cryptography, complexity, logic design, and artificial intelligence.Updated throughout, some major additions for the 3rd edition include: a new chapter about the concepts contributing to the power of XBOOLE; a new chapter that introduces into the application of the XBOOLE-Monitor XBM 2; many tasks that support the readers in amplifying the learned content at the end of the chapters; solutions of a large subset of these tasks to confirm learning success; challenging tasks that need the power of the XBOOLE software for their solution. The XBOOLE-monitor XBM 2 software is used to solve the exercises; in this way the time-consuming and error-prone manipulation on the bit level is moved to an ordinary PC, more realistic tasks can be solved, and the challenges of thinking about algorithms leads to a higher level of education.Table of ContentsPart I Theoretical Foundations 1. Basic Algebraic Structures 2. Logic Functions 3. Logic Equations 4. Boolean Differential Calculus 5. Sets, Lattices, and Classes Logic Functions Part II Applications 6. Logics, Arithmetic, and Special Functions 7. SAT-Problems 8. Extremely Complex Problems 9. Combinational Circuits 10. Sequential Circuits References Index

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Book SynopsisThis book constitutes the refereed proceedings of the First Workshop on Monte Carlo Search, MCS 2020, organized in conjunction with IJCAI 2020. The event was supposed to take place in Yokohama, Japan, in July 2020, but due to the Covid-19 pandemic was held virtually on January 7, 2021. The 9 full papers of the specialized project were carefully reviewed and selected from 15 submissions. The following topics are covered in the contributions: discrete mathematics in computer science, games, optimization, search algorithms, Monte Carlo methods, neural networks, reinforcement learning, machine learning.Table of ContentsThe αµ Search Algorithm for the Game of Bridge.- Stabilized Nested Rollout Policy Adaptation.- zoNNscan: A Boundary-Entropy Index for Zone Inspection of Neural Models.- Ordinal Monte Carlo Tree Search.- Monte Carlo Game Solver.- Generalized Nested Rollout Policy Adaptation.- Monte Carlo Inverse Folding.- Monte Carlo Graph Coloring.- Enhancing Playout Policy Adaptation for General Game Playing.

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Book SynopsisThis textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective "deep" comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github. The ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.Table of ContentsWhy Deep Generative Modeling?.- Autoregressive Models.- Flow-based Models.- Latent Variable Models.- Hybrid Modeling.- Energy-based Models.- Generative Adversarial Networks.- Deep Generative Modeling for Neural Compression.- Useful Facts from Algebra and Calculus.- Useful Facts from Probability Theory and Statistics.- Index.

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Book SynopsisThis textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective "deep" comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github. The ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.Table of ContentsWhy Deep Generative Modeling?.- Autoregressive Models.- Flow-based Models.- Latent Variable Models.- Hybrid Modeling.- Energy-based Models.- Generative Adversarial Networks.- Deep Generative Modeling for Neural Compression.- Useful Facts from Algebra and Calculus.- Useful Facts from Probability Theory and Statistics.- Index.

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Book SynopsisThis SpringerBrief provides the first systematic review of the existing works of cohesive subgraph search (CSS) over large heterogeneous information networks (HINs). It also covers the research breakthroughs of this area, including models, algorithms and comparison studies in recent years. This SpringerBrief offers a list of promising future research directions of performing CSS over large HINs.The authors first classify the existing works of CSS over HINs according to the classic cohesiveness metrics such as core, truss, clique, connectivity, density, etc., and then extensively review the specific models and their corresponding search solutions in each group. Note that since the bipartite network is a special case of HINs, all the models developed for general HINs can be directly applied to bipartite networks, but the models customized for bipartite networks may not be easily extended for other general HINs due to their restricted settings. The authors also analyze and compare these cohesive subgraph models (CSMs) and solutions systematically. Specifically, the authors compare different groups of CSMs and analyze both their similarities and differences, from multiple perspectives such as cohesiveness constraints, shared properties, and computational efficiency. Then, for the CSMs in each group, the authors further analyze and compare their model properties and high-level algorithm ideas.This SpringerBrief targets researchers, professors, engineers and graduate students, who are working in the areas of graph data management and graph mining. Undergraduate students who are majoring in computer science, databases, data and knowledge engineering, and data science will also want to read this SpringerBrief.Table of Contents1. Introduction2. Preliminaries3. CSS on Bipartite Networks4. CSS on Other General HINs5. Comparison Analysis6. Related Work on CSMs and solutions7. Future Work and Conclusion

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Book SynopsisThis book is about the harmonious synthesis of functional programming and numerical computation. It shows how the expressiveness of OCaml allows for fast and safe development of data science applications. Step by step, the authors build up to use cases drawn from many areas of Data Science, Machine Learning, and AI, and then delve into how to deploy at scale, using parallel, distributed, and accelerated frameworks to gain all the advantages of cloud computing environments.To this end, the book is divided into three parts, each focusing on a different area. Part I begins by introducing how basic numerical techniques are performed in OCaml, including classical mathematical topics (interpolation and quadrature), statistics, and linear algebra. It moves on from using only scalar values to multi-dimensional arrays, introducing the tensor and Ndarray, core data types in any numerical computing system. It concludes with two more classical numerical computing topics, the solution of Ordinary Differential Equations (ODEs) and Signal Processing, as well as introducing the visualization module we use throughout this book. Part II is dedicated to advanced optimization techniques that are core to most current popular data science fields. We do not focus only on applications but also on the basic building blocks, starting with Algorithmic Differentiation, the most crucial building block that in turn enables Deep Neural Networks. We follow this with chapters on Optimization and Regression, also used in building Deep Neural Networks. We then introduce Deep Neural Networks as well as topic modelling in Natural Language Processing (NLP), two advanced and currently very active fields in both industry and academia. Part III collects a range of case studies demonstrating how you can build a complete numerical application quickly from scratch using Owl. The cases presented include computer vision and recommender systems. This book aims at anyone with a basic knowledge of functional programming and a desire to explore the world of scientific computing, whether to generally explore the field in the round, to build applications for particular topics, or to deep-dive into how numerical systems are constructed. It does not assume strict ordering in reading – readers can simply jump to the topic that interests them most. Table of ContentsPart I: Numerical Techniques.- 1. Introduction.- 2. Numerical Algorithms.- 3. Statistics.- 4. Linear Algebra.- 5. N-Dimensional Arrays.- 6. Ordinary Differential Equations.- 7. Signal Processing.- Part II: Advanced Data Analysis Techniques.- 8. Algorithmic Differentiation.- 9. Optimisation.- 10. Regression.- 11. Neural Network.- 12. Vector Space Modelling.- Part III: Use Cases.- 13. Case Study: Image Recognition.- 14. Case Study: Instance Segmentation.- 15. Case Study: Neural Style Transfer.- 16. Case Study: Recommender System.

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Book SynopsisUsing a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with "Programming Tips" that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own computers. Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are used to implement and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, interpretability, and regularization. Many abstract mathematical ideas, such as modes of convergence in probability, are explained and illustrated with concrete numerical examples. This book is suitable for anyone with undergraduate-level experience with probability, statistics, or machine learning and with rudimentary knowledge of Python programming.Table of ContentsIntroduction.- Part 1 Getting Started with Scientific Python.- Installation and Setup.- Numpy.- Matplotlib.- Ipython.- Jupyter Notebook.- Scipy.- Pandas.- Sympy.- Interfacing with Compiled Libraries.- Integrated Development Environments.- Quick Guide to Performance and Parallel Programming.- Other Resources.- Part 2 Probability.- Introduction.- Projection Methods.- Conditional Expectation as Projection.- Conditional Expectation and Mean Squared Error.- Worked Examples of Conditional Expectation and Mean Square Error Optimization.- Useful Distributions.- Information Entropy.- Moment Generating Functions.- Monte Carlo Sampling Methods.- Useful Inequalities.- Part 3 Statistics.- Python Modules for Statistics.- Types of Convergence.- Estimation Using Maximum Likelihood.- Hypothesis Testing and P-Values.- Confidence Intervals.- Linear Regression.- Maximum A-Posteriori.- Robust Statistics.- Bootstrapping.- Gauss Markov.- Nonparametric Methods.- Survival Analysis.- Part 4 Machine Learning.- Introduction.- Python Machine Learning Modules.- Theory of Learning.- Decision Trees.- Boosting Trees.- Logistic Regression.- Generalized Linear Models.- Regularization.- Support Vector Machines.- Dimensionality Reduction.- Clustering.- Ensemble Methods.- Deep Learning.- Notation.- References.- Index.

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Book SynopsisThis book constitutes the proceedings of the 43rd International Conference on Application and Theory of Petri Nets and Concurrency, PETRI NETS 2022, which was held virtually in June 2021. The 19 full papers presented in this volume were carefully reviewed and selected from 35 submissions. The papers are categorized into the following topical sub-headings: application of concurrency to system design; timed models; tools; applications; synthesis; petri nets architecture; and process mining.

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Book SynopsisThis book constitutes the thoroughly refereed proceedings of the 8th International Conference on Mathematics and Computation in Music, MCM 2022, held in Atlanta, GA, USA, in June 2022. The 29 full papers and 8 short papers presented were carefully reviewed and selected from 45 submissions. The papers feature research that combines mathematics or computation with music theory, music analysis, composition, and performance. They are organized in Mathematical Scale and Rhythm Theory: Combinatorial, Graph Theoretic, Group Theoretic and Transformational Approaches; Categorical and Algebraic Approaches to Music; Algorithms and Modeling for Music and Music-Related Phenomena; Applications of Mathematics to Musical Analysis; Mathematical Techniques and MicrotonalityTable of ContentsAlgebraic structures.- artificial intelligence.- clustering and computational analysis of music.- computational music theory.- fourier transforms.- machine learning.- mathematical analysis of music.- mathematical models of music.- mathematical music theory.- music cognition.- music formalization semantics.- signal processing.- software for musical processing.- algorithm analysis and problem complexity.

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Book SynopsisThis book constitutes the proceedings of the 19th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2022, which was held in Los Angeles, CA, USA, in June 2022.The 28 regular papers presented were carefully reviewed and selected from a total of 60 submissions. The conference program included a Master Class on the topic "Bridging the Gap between Machine Learning and Optimization”.Table of ContentsA Two-Phase Hybrid Approach for the Hybrid Flexible Flowshop with Transportation Times.- A SAT Encoding to compute Aperiodic Tiling Rhythmic Canons.- Transferring Information across Restarts in MIP.- Towards Copeland Optimization in Combinatorial Problems.- Coupling Different Integer Encodings for SAT.- Model-Based Algorithm Configuration with Adaptive Capping and Prior Distributions.- Shattering Inequalities for Learning Optimal Decision Trees.- Learning Pseudo-Backdoors for Mixed Integer Programs.- Leveraging Integer Linear Programming to Learn Optimal Fair Rule Lists.- Solving the Job Shop Scheduling Problem extended with AGVs – Classical and Quantum Approaches.- Stochastic Decision Diagrams.- Improving the robustness of EPS to solve the TSP.- Efficient operations between MDDs and constraints.- Deep Policy Dynamic Programming for Vehicle Routing Problems.- Learning a Propagation Complete Formula.- A FastMap-Based Algorithm for Block Modeling.- Packing by Scheduling: Using Constraint Programming to Solve a Complex 2D Cutting Stock Problem.- Dealing with the product constraint.- Multiple-choice knapsack constraint in graphical models.- A Learning Large Neighborhood Search for the Staff Rerostering Problem.- Practically Uniform Solution Sampling in Constraint Programming.- Training Thinner and Deeper Neural Networks: Jumpstart Regularization.- Hybrid Offline/Online Optimization for Energy Management via Reinforcement Learning.- Enumerated Types and Type Extensions for MiniZinc.- A parallel algorithm for generalized arc-consistent filtering for the Alldifferent constraint.- Analyzing the Reachability Problem in Choice Networks.- Model-based Approaches to Multi-Attribute Diverse Matching.

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Book SynopsisThis book constitutes the refereed proceedings of the 15th International Conference on Graph Transformation, ICGT 2022, which took place Nantes, France in July 2022.The 10 full papers and 1 tool paper presented in this book were carefully reviewed and selected from 19 submissions. The conference focuses on describing new unpublished contributions in the theory and applications of graph transformation as well as tool presentation papers that demonstrate main new features and functionalities of graph-based tools.Table of ContentsTheoretical Advances.- Application Domains.- Tool Presentation.

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Book SynopsisThis book constitutes the proceedings of the 24th International Workshop on Computer Algebra in Scientific Computing, CASC 2022, which took place in Gebze, Turkey, in August 2022. The 20 full papers included in this book were carefully reviewed and selected from 32 submissions. They focus on the theory of symbolic computation and its implementation in computer algebra systems as well as all other areas of scientific computing with regard to their benefit from or use of computer algebra methods and software. Table of ContentsSurvey on Generalizations of the Intermediate Value Theorem and Applications (Invited Talk).- On Truncated Series Involved in Exponential-Logarithmic Solutions of Truncated LODEs.- Subresultant Chains Using B´ezout Matrices.- Application of Symbolic-Numerical Modeling Tools for Analysis of Gyroscopic Stabilization of Gyrostat Equilibria.- Computer Science for Continuous Data: Vision, Theory, and Practice of a Computer (Algebra) ANALYSIS System.- Computational Aspects of Equivariant Hilbert Series of Canonical Rings for Algebraic Curves.- Symbolic-Numeric Algorithm for Calculations in Geometric Collective Model of Atomic Nuclei.- Analyses and Implementations of Chordality-Preserving Top-Down Algorithms for Triangular Decomposition.- Accelerated Subdivision for Clustering Roots of Polynomials Given by Evaluation Oracles.- On Equilibrium Positions in the Problem of the Motion of a System of Two Bodies in a Uniform Gravity Field.- An Interpolation Algorithm for Computing Dixon Resultants.- Distance Evaluation to the Set of Matrices with Multiple Eigenvalues.- On Boundary Conditions Parametrized by Analytic Functions.- Computing the Integer Hull of Convex Polyhedral Sets.- A Comparison of Algorithms for Proving Positivity of Linearly Recurrent Sequences.- Stability Analysis of Periodic Motion of the Swinging Atwood Machine.- New Heuristic to Choose a Cylindrical Algebraic Decomposition Variable Ordering Motivated by Complexity Analysis.- An Implementation of Parallel Number-Theoretic Transform Using Intel AVX-512 Instructions.- Locating the Closest Singularity in a Polynomial Homotopy.- A General Method of Finding New Symplectic Schemes for Hamiltonian Mechanics.- A Mechanical Method for Isolating Locally Optimal Points of Certain Radical Functions.

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Book SynopsisThis book constitutes the proceedings of the 16th International Conference on Algorithmic Aspects in Information and Management, AAIM 2022, which was held online during August 13-14, 2022. The conference was originally planned to take place in Guangzhou, China, but changed to a virtual event due to the COVID-19 pandemic.The 41 regular papers included in this book were carefully reviewed and selected from 59 submissions. Table of ContentsAn improvement of the bound on the odd chromatic number of 1-planar graphs.- AoI Minimizing of Wireless Rechargeable Sensor Network based on Trajectory Optimization of Laser-Charged UAV.- Monotone k-Submodular Knapsack Maximization: An Analysis of the Greedy+Singleton Algorithm.- The constrained parallel-machine scheduling problem with divisible processing times and penalties.- Energy-constrained Geometric Covering Problem.- Fast searching on $k$-combinable graphs.- Three Algorithms for Converting Control Flow Statements from Python to XD-M.- Class Ramsey numbers involving induced graphs.- An Approximation Algorithm for the Clustered Path Travelling Salesman Problem.- Hyperspectral Image Reconstruction for SD-CASSI systems based on Residual Attention Network.- Improved Approximation Algorithm for the Asymmetric Prize-Collecting TSP.- Injective edge coloring of power graphs and necklaces.- Guarantees for Maximization of $k$-Submodular Functions with a Knapsack and a Matroid Constraint.- Incremental SDN Deployment to Achieve Load Balance in ISP Networks.- Approximation scheme for single-machine rescheduling with job delay and rejection.- Defense of Scapegoating Attack in Network Tomography.- A Binary Search Double Greedy Algorithm for Non-monotone DR-submodular Maximization.- Streaming Adaptive Submodular Maximization.- Constrained Stochastic Submodular Maximization with State-Dependent Costs.- Online early work maximization problem on two hierarchical machines with buffer or rearrangements.- Polynomial time algorithm for k-vertex-edge dominating problem in interval graphs.- Adaptive Competition-based Diversified-profit Maximization with Online Seed Allocation.- Collaborative Service Caching in Mobile Edge Nodes.- A Decentralized Auction Framework with Privacy Protection in Mobile Crowdsourcing.- On-line single machine scheduling with release dates and submodular rejection penalties.- Obnoxious Facility Location Games with Candidate Locations.- Profit Maximization for Multiple Products in Community-based Social Networks.- MCM: A Robust Map Matching Method by Tracking Multiple Road Candidates.- Security on Ethereum: Ponzi Scheme Detection in Smart Contract.- Cyclically orderable generalized Petersen graphs.- The r-dynamic chromatic number of planar graphs without special short cycles.- Distance Labeling of the Halved Folded $n$-Cube.- Signed network embedding based on muti-attention mechanism.- Balanced Graph Partitioning based on Mixed 0-1 Linear Programming and Iteration Vertex Relocation Algorithm.- Partial inverse min-max spanning tree problem under the weighted bottleneck Hamming distance.- Mixed Metric Dimension of Some Plane Graphs.- The Optimal Dynamic Rationing Policy in the Stock-Rationing Queue.- Pilot Pattern Design with Branch and Bound in PSA-OFDM System.- Bicriteria Algorithms for Maximizing the Difference Between Submodular Function and Linear Function under Noise.- On the Transversal Number of k-Uniform Connected Hypergraphs.- Total coloring of planar graphs without some adjacent cycles.

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Book SynopsisThis textbook aims to point out the most important principles of data analysis from the mathematical point of view. Specifically, it selected these questions for exploring: Which are the principles necessary to understand the implications of an application, and which are necessary to understand the conditions for the success of methods used? Theory is presented only to the degree necessary to apply it properly, striving for the balance between excessive complexity and oversimplification. Its primary focus is on principles crucial for application success. Topics and features: Focuses on approaches supported by mathematical arguments, rather than sole computing experiences Investigates conditions under which numerical algorithms used in data science operate, and what performance can be expected from them Considers key data science problems: problem formulation including optimality measure; learning and generalization in relationships to training set size and number of free parameters; and convergence of numerical algorithms Examines original mathematical disciplines (statistics, numerical mathematics, system theory) as they are specifically relevant to a given problem Addresses the trade-off between model size and volume of data available for its identification and its consequences for model parametrization Investigates the mathematical principles involves with natural language processing and computer vision Keeps subject coverage intentionally compact, focusing on key issues of each topic to encourage full comprehension of the entire book Although this core textbook aims directly at students of computer science and/or data science, it will be of real appeal, too, to researchers in the field who want to gain a proper understanding of the mathematical foundations “beyond” the sole computing experience.Table of Contents1. Data Science and its Tasks.- 2. Application Specific Mappings and Measuring the Fit to Data.- 3. Data Processing by Neural Networks.- 4. Learning and Generalization.- 5. Numerical Algorithms for Network Learning.- 6. Specific Problems of Natural Language Processing.- 7. Specific Problems of Computer Vision.

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Book SynopsisThis book constitutes the proceedings of the 30th International Symposium on Graph Drawing and Network Visualization, GD 2022, held in Tokyo, Japan, during September 13-16, 2022. The 25 full papers, 7 short papers, presented together with 2 invited talks, one report on graph drawing contest, and one obituary in these proceedings were carefully reviewed and selected from 70 submissions. The abstracts of 5 posters presented at the conference can be found in the back matter of the volume. The contributions were organized in topical sections as follows: properties of drawings of complete graphs; stress-based visualizations of graphs; planar and orthogonal drawings; drawings and properties of directed graphs; beyond planarity; dynamic graph visualization; linear layouts; and contact and visibility graph representations. Table of ContentsProperties of Drawings of Complete Graphs.- Stress-based Visualizations of Graphs.- Planar and Orthogonal Drawings.- Drawings and Properties of Directed Graphs.- Beyond Planarity.- Dynamic Graph Visualization.- Linear Layouts.- Contact and Visibility Graph Representations.

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Book SynopsisThis book constitutes the refereed proceedings of the Third Southern African Conference on Artificial Intelligence Research, SACAIR 2022, held in Stellenbosch, South Africa, in December 2022. The 26 papers presented were thoroughly reviewed and selected from the 73 submissions. They are organized on the topical sections on​ algorithmic, data driven and symbolic AI; socio-technical and human-centered AI; responsible and ethical AI. Table of ContentsAlgorithmic, Data Driven and Symbolic AI.- Socio-technical and human-centered AI.- Responsible and Ethical AI.

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Book SynopsisThis book constitutes the refereed proceedings of the 17th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2022, held in Cartagena de Indias, Colombia, in November 2022. The 33 full and 4 short papers presented were carefully reviewed and selected from 67 submissions. The papers are organized in the following topical sections: applications of AI; ethics and smart city; green and sustainable AI; machine learning; natural language processing; robotics and computer vision; simulation and forecasting.Table of ContentsApplications of AI.- Ethics and Smart City.- Green and Sustainable AI.- Machine Learning.- Natural Language Processing.- Robotics and Computer Vision.- Simulation and Forecasting.

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Book SynopsisThis book constitutes the thoroughly refereed post-workshop proceedings of the 8th International Workshop on the Arithmetic of Finite Field, WAIFI 2022, held in Chengdu, China, in August – September 2022.The 19 revised full papers and 3 invited talks presented were carefully reviewed and selected from 25 submissions. The papers are organized in topical sections: structures in finite fields; efficient finite field arithmetic; coding theory; cryptography; sequences.Table of ContentsStructures in Finite Fields.- On a conjecture on irreducible polynomials over finite fields with restricted coefficients.- On two applications of polynomials xk – cx – d over finite fields and more.- Efficient Finite Field Arithmetic.- Polynomial Constructions of Chudnovsky-Type Algorithms for Multiplication in Finite Fields with Linear Bilinear Complexity.- Reduction-free Multiplication for Finite Fields and Polynomial Rings.- Finite Field Arithmetic in Large Characteristic for Classical and Post-Quantum Cryptography.- Fast enumeration of superspecial hyperelliptic curves of genus 4 with automorphism group V4.- Coding theory.- Two Classes of Constacyclic Codes with Variable Parameters.- Near MDS Codes with Dimension 4 and Their Application in Locally Recoverable Codes.- Optimal possibly nonlinear 3-PIR codes of small size.- PIR codes from combinatorial structures.- The Projective General Linear Group PGL(2, 5m) and Linear Codes of Length 5m + 1.- Private Information Retrieval Schemes Using Cyclic Codes.- Two Classes of Optimal Few-Weight Codes over Fq + uFq.- Explicit Non-Malleable Codes from Bipartite Graphs.- Cryptography.- Algebraic Relation of Three MinRank Algebraic Modelings.- Decomposition of Dillon's APN permutation with efficient hardware implementation.- New Versions of Miller-loop Secured against Side-Channel Attacks.- A Class of Power Mappings with Low Boomerang Uniformity.- New Classes of Bent Functions via the Switching Method.- Sequences.- Correlation measure of binary sequence families with trace representation.- Linear complexity of generalized cyclotomic sequences with period pnqm.- On the 2-adic complexity of cyclotomic binary sequences with period p2 and 2p2.

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Book SynopsisThis book constitutes the refereed proceedings of the 13th International Conference on Advances in Optimization and Applications, OPTIMA 2022, held in Petrovac, Montenegro, during September 26–30, 2022. The 13 full papers included in this book were carefully reviewed and selected from 26 submissions. They were organized in topical sections as follows: ​mathematical programming; global optimization; discrete and combinatorial optimization; optimization and data analysis; game theory and mathematical economics; and applications.Table of Contents​Mathematical Programming.- A Derivative-Free Nonlinear Least Squares Solver.- Gradient-Type Methods for Optimization Problems with Polyak- Lojasiewicz Condition: Early Stopping and Adaptivity to Inexactness Parameter.- Global Optimization.- An Improved Genetic Algorithm for the Resource-Constrained Project Scheduling Problem.- Nonlocal Optimization Methods for Nonlinear Controlled Systems with Terminal Constraints.- Discrete and Combinatorial Optimization.- Three-Bar Charts Packing Problem.- An 11/7 – Approximation Algorithm for Single Machine Scheduling Problem with Release and Delivery Times.- Optimization and Data Analysis.- Decentralized Strongly-Convex Optimization with Affine Constraints: Primal and Dual Approaches.- Game Theory and Mathematical Economics.- Analysis of the Model of Optimal Expansion of a Firm.- Comparative Analysis of the Efficiency of Financing the State Budget through Emissions, Taxes and Public Debt.- Applications.- Construction of Optimal Feedback for Zooplankton Diel Vertical Migration.- Synthesis of Trajectory Planning Algorithms Using Evolutionary Optimization Algorithms.- Application of Attention Technique for Digital Pre-Distortion.- Forecasting with Using Quasilinear Recurrence Equation.

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Book SynopsisThis book constitutes the conference proceedings of the 48th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2023, held in Nový Smokovec, Slovakia, during January 15–18, 2023.The 22 full papers presented together with 2 best papers and 2 best students papers in this book were carefully reviewed and selected from 43 submissions.This workshop focuses on graphs problems and optimization; graph drawing and visualization; NP-hardness and fixed parameter tractability; communication and temporal graphs; complexity and learning; and robots and strings. Table of ContentsThe Complexity of Finding Tangles.- A spectral algorithm for finding maximum cliques in dense random intersection graphs.- Solving Cut-Problems in Quadratic Time for Graphs With Bounded Treewidth.- More Effort Towards Multiagent Knapsack.- Dominance Drawings for DAGs with Bounded Modular Width.- Morphing Planar Graph Drawings Through 3D.- Visualizing Multispecies Coalescent Trees: Drawing Gene Trees Inside Species Trees.- Parameterized Approaches to Orthogonal Compaction.- Hardness of bounding influence via graph modification.- On the Parameterized Complexity of $s$-club Cluster Deletion Problems.- Balanced Substructures in Bicolored Graphs.- On the Complexity of Scheduling Problems With a Fixed Number of Parallel Identical Machines.- On the 2-Layer Window Width Minimization Problem.- Sequentially Swapping Tokens: Further on Graph Classes.- On the Preservation of Properties when Changing Communication Models.- Introduction to Routing Problems with Mandatory Transitions .- Multi-Parameter Analysis of Finding Minors and Subgraphs in Edge-Periodic Temporal Graphs.- Lower Bounds for Monotone $q$-Multilinear Boolean Circuits.- A faster algorithm for determining the linear feasibility of systems of BTVPI constraints.- Quantum complexity for vector domination problem.- Learning through Imitation by using Formal Verification.- Delivery to Safety with Two Cooperating Robots.- Space-Efficient STR-IC-LCS Computation.- The k-center Problem for Classes of Cyclic Words.

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Book SynopsisThis volume constitutes short papers and DETECT 2022 workshop papers, presented during the 11th International Conference on Model and Data Engineering, MEDI 2022, held in Cairo, Egypt, in November 2022.The 11 short papers presented were selected from the total of 65 submissions. This volume also contains the 4 accepted papers from the DETECT 2022 workshop, held at MEDI 2022. The volume focuses on advances in data management and modelling, including topics such as data models, data processing, database theory, database systems technology, and advanced database applications.Table of ContentsImage processing and diagnosis.- Machine Learning and Optimization.- Machine Learning and Optimization.- Modelling.- Database systems.- Applications.- DETECT Workshop: modeling, verification and testing of dependable critical systems.

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Book SynopsisThe once esoteric idea of embedding scientific computing into a probabilistic framework, mostly along the lines of the Bayesian paradigm, has recently enjoyed wide popularity and found its way into numerous applications. This book provides an insider’s view of how to combine two mature fields, scientific computing and Bayesian inference, into a powerful language leveraging the capabilities of both components for computational efficiency, high resolution power and uncertainty quantification ability. The impact of Bayesian scientific computing has been particularly significant in the area of computational inverse problems where the data are often scarce or of low quality, but some characteristics of the unknown solution may be available a priori. The ability to combine the flexibility of the Bayesian probabilistic framework with efficient numerical methods has contributed to the popularity of Bayesian inversion, with the prior distribution being the counterpart of classical regularization. However, the interplay between Bayesian inference and numerical analysis is much richer than providing an alternative way to regularize inverse problems, as demonstrated by the discussion of time dependent problems, iterative methods, and sparsity promoting priors in this book. The quantification of uncertainty in computed solutions and model predictions is another area where Bayesian scientific computing plays a critical role. This book demonstrates that Bayesian inference and scientific computing have much more in common than what one may expect, and gradually builds a natural interface between these two areas.Table of ContentsInverse problems and subjective computing.- Linear algebra.- Continuous and discrete multivariate distributions.- Introduction to sampling.- The praise of ignorance: randomness as lack of certainty.- Enter subject: Construction of priors.- Posterior densities, ill-conditioning, and classical regularization.- Conditional Gaussian densities.- Iterative linear solvers and priorconditioners.- Hierarchical models and Bayesian sparsity.- Sampling: the real thing.- Dynamic methods and learning from the past.- Bayesian filtering and Gaussian densities.-

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Book SynopsisThis book constitutes selected and revised papers from the 22nd International Conference on Mathematical Modeling and Supercomputer Technologies, MMST 2022, held in Nizhny Novgorod, Russia, in November 2022. The 20 full papers and 5 short papers presented in the volume were thoroughly reviewed and selected from the 48 submissions. They are organized in topical secions on ​computational methods for mathematical models analysis; computation in optimization and optimal control; supercomputer simulation. Table of ContentsComputational methods for mathematical models analysis.- Computation in optimization and optimal control.- Supercomputer simulation.

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Book SynopsisThis book constitutes the refereed proceedings of the 16th International Conference on Learning and Intelligent Optimization, LION 16, which took place in Milos Island, Greece, in June 2022.The 36 full papers and 3 short papers presented in this volume were carefully reviewed and selected from 60 submissions. LION deals with automatic solver configuration, parallel methods, intelligent optimization, nature-inspired algorithms, hard combinatorial optimization problems, DC learning, computational intelligence, and others. The contributions were organized in topical sections as follows: Invited Papers; Contributed Papers.Table of Contents​Invited Papers.- Optimal Scheduling of the Leaves of a Tree and the SVO Frequencies of Languages.- From Design of Experiments to Combinatorics of Disasters: A Conceptual Framework for Disaster Exercises.- Separating two polyhedra utilizing alternative theorems and penalty function.- Contributed Papers. -A Composite Index Method for Optimization Benchmarking.- Optimal Energy Management of Microgrid Using Multi-objective Optimisation Approach.- A Stochastic Alternating Balance k-Means Algorithm for Fair Clustering.- Binary Black Widow Optimization Algorithm for Feature Selection Problems.- Learning to Solve a Stochastic Orienteering Problem with Time Windows.- ML-based approach for accelerating global search algorithm for solving multicriteria problems .- The Skewed Kruskal algorithm.- Bounds for sparse solutions of K-SVCR multi-class classification model.- Integer Linear Programming in Solving an Optimization Problem at the Mixing Department of the Metallurgical Production.- Realtime Gray-Box Algorithm Configuration.- Dynamic urban solid waste management system for smart cities.- Single MCMC Chain Parallelisation on Decision Trees.- Single MCMC Chain Parallelisation on Decision Trees.- Competitive supply allocation in a distribution network under overproduction.- Safe-exploration of control policies from safe-experience via Gaussian Processes.- Bayesian Optimization in Wasserstein Spaces.- Network Vulnerability Analysis in Wasserstein Spaces.- BERT Self-Learning Approach with Limited Labels for Document Classification.- Autonomous Learning Optimization for Deep Learning.- Optimizing Data Augmentation Policy through Random Unidimensional Search.- Evaluating Student Behaviour on the MathE Platform - Clustering Algorithms Approaches.- Unsupervised Training for Neural TSP Solver.- Comparing surrogate models for tuning optimization algorithms.- Search and Score-based Waterfall Auction Optimization.- Survey on KNN Methods in Data Science.- Constrained Shortest Path and Hierarchical Structures.- Investigation of Graph Neural Networks for Instance Segmentation of Industrial Point Cloud Data.- Fitness landscape ruggedness impact on PSO in dealing with three variants of the travelling salesman problem.- A Multi-UAVs’ Provider Model for the provision of 5G Service Chains: a game theoretic approach.- Metabolic Syndrome Risk Forecasting on Elderly with ML Techniques.- Airport Digital Twins for Resilient Disaster Management Response.- Strategies for Surviving Aggressive Multiparty Repeated Standoffs.- A Hybridization of GRASP and UTASTAR for Solving the Vehicle Routing Problem with Pickups and Deliveries and 3D Loading Constraints.- Packing hypertrees and the k-cut problem in Hypergraphs.- Maximizing the Eigenvalue-Gap and Promoting Sparsity of Doubly Stochastic Matrices with PSO.- Value of Information in the Mean-Square Case and its Application to the Analysis of Financial Time-Series Forecast.

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Book SynopsisThis book constitutes the refereed proceedings of the 18th European Workshop on Computer Performance Engineering, EPEW 2022, held in Santa Pola, Spain, in September 2022.The 14 papers presented in this volume together with one invited talk were carefully reviewed and selected from 14 submissions. The papers presented at the workshop reflect the diversity of modern performance engineering. The sessions covered a wide range of topics including robustness analysis, machine learning, edge and cloud computing, as well as more traditional topics on stochastic modelling, techniques and tools.Table of ContentsRobustness analysis.- Applications.- Stochastic modelling.- Machine learning.- Edge-cloud computing.- Modelling paradigms and tools.

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Book SynopsisThis book constitutes the proceedings of the 9th International Conference on Algorithms and Discrete Applied Mathematics, CALDAM 2023, which was held in Gandhinagar, India, during February 9-11, 2023.The 32 papers presented in this volume were carefully reviewed and selected from 67 submissions. The papers were organized in topical sections named: algorithms and optimization; computational geometry; game theory; graph coloring; graph connectivity; graph domination; graph matching; graph partition and graph covering.Table of ContentsStable Approximation Schemes.- A whirlwind tour of intersection graph enumeration.- Graph modification problems with forbidden minors.- Algorithms & Optimization Efficient reductions and algorithms for Subset Product.- Optimal length cutting plane refutations of integer programs.- Fault-Tolerant Dispersion Resource management in device-to-device communications.- Computational Geometry Algorithms for k-Dispersion for Points in Convex Position in the Plane.- Arbitrary oriented color spanning region for line segments.- Games with a Simple Rectilinear Obstacle in Plane.- Diverse Fair Allocations: Complexity and Algorithms.- Graph Coloring New bounds and constructions for neighbor-locating colorings of graphs.- D K 5-list coloring toroidal 6-regular triangulations in linear time.- On Locally Identifying Coloring of Graphs.- On Structural Parameterizations of Star Coloring.- Reddy Perfectness of G-generalized join of graphs.- Coloring of a superclass of 2K2-free graphs.- The Weak (2,2)-Labelling Problem for graphs with forbidden induced structures.- Graph Connectivity Short cycles dictate dichotomy status of the Steiner tree problem on Bisplit graphs.- Some insights on dynamic maintenance of Gomory-Hu tree in cactus graphs and general graphs.- Monitoring edge-geodetic sets in graphs.- Cyclability, Connectivity and Circumference.- Graph Domination On three domination-based identification problems in block graphs.- Graph modification problems with forbidden minors.- Computational Aspects of Double Dominating Sequences in Graph.- Relation between broadcast domination and multipacking numbers on chordal graphs.- Pushing Cops and Robber on Oriented Graphs.- Mind the Gap: Edge Facility Location Problems in Theory and Practice.- Complexity Results on Cosecure Domination in Graphs.- Kusum and Arti Pandey Graph Matching Latin Hexahedra and Related Combinatorial Structures.- Minimum Maximal Acyclic Matching in Proper Interval Graphs.- Graph Partition & Graph Covering Transitivity on subclasses of chordal graphs.- Maximum subgraph problem for 3-regular Knödel graphs and its wirelength.- Covering using Bounded Size Subgraphs.- Axiomatic characterization of the the toll walk function of some graph classes.- Structural Parameterization of Alliance Problems.

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Book SynopsisThe multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022.The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions. The volumes are organized in topical sections as follows: Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; . Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track. Table of ContentsSupervised learning.- Probabilistic inference.- Optimal transport.- Optimization.- Quantum, hardware.- Sustainability.

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Book SynopsisThis book constitutes the refereed proceedings of the 12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2022 held in Leiden, The Netherlands, during March 20-24, 2023. The 44 regular papers presented in this book were carefully reviewed and selected from 65 submissions. The papers are divided into the following topical sections: Algorithm Design and Engineering; Machine Learning and Multi-criterion Optimization; Benchmarking and Performance Assessment; Indicator Design and Complexity Analysis; Applications in Real World Domains; and Multi-Criteria Decision Making and Interactive Algorithms..Table of Contents​Algorithm Design and Engineering.- Visual Exploration of the Effect of Constraint Handling in Multiobjective Optimization.- A Two-stage Algorithm for Integer Multiobjective Simulation Optimization.- RegEMO: Sacrificing Pareto-Optimality for Regularity in Multi-objective Problem-Solving.- Cooperative coevolutionary NSGA-II with Linkage Measurement Minimization for Large-scale Multi-objective Optimization.- Data-Driven Evolutionary Multi-Objective Optimization Based on Multiple-Gradient Descent for Disconnected Pareto Fronts.- Eliminating Non-dominated Sorting from NSGA-III.- Scalability of Multi-Objective Evolutionary Algorithms for Solving Real-World Complex Optimization Problems.- Machine Learning and Multi-criterion Optimization.- Multi-Objective Learning using HV Maximization.- Sparse Adversarial Attack via Bi-Objective Optimization.- Investigating Innovized Progress Operators with Different Machine Learning Methods.- End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location.- Online Learning Hyper-Heuristics in Multi-Objective Evolutionary Algorithms.- Surrogate-assisted Multi-objective Optimization via Genetic Programming based Symbolic Regression.- Learning to Predict Pareto-optimal Solutions From Pseudo-weights.- A Relation Surrogate Model for Expensive Multiobjective Continuous and Combinatorial Optimization.- Pareto Front Upconvert by Iterative Estimation Modeling and Solution Sampling.- Pareto Front Upconvert by Iterative Estimation Modeling and Solution Sampling.- Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables.- Feature-based Benchmarking of Distance-based Multi/Many-objective Optimisation Problems: A Machine Learning Perspective.- Benchmarking and Performance Assessment.- Partially Degenerate Multi-Objective Test Problems.- Peak-A-Boo! Generating Multi-Objective Multiple Peaks Benchmark Problems with Precise Pareto Sets.- MACO: A Real-world inspired Benchmark for Multi-objective Evolutionary Algorithms.- A scalable test suite for bi-objective multidisciplinary optimisation.- Performance Evaluation of Multi-Objective Evolutionary Algorithms using Artificial and Real-World Problems.- A Novel Performance Indicator based on the Linear Assignment Problem.- A Test Suite for Multi-objective Multi-fidelity Optimization.- Indicator Design and Complexity Analysis.- Diversity enhancement via magnitude.- Two-Stage Greedy Approximated Hypervolume Subset Selection for Large-Scale Problems.- Two-Stage Greedy Approximated Hypervolume Subset Selection for Large-Scale Problems.- On the Computational Complexity of Efficient Non-Dominated Sort using Binary Search.- Applications in Real World Domains.- Evolutionary Algorithms with Machine Learning Models for Multiobjective Optimization in Epidemics Control.- Joint Price Optimization across a Portfolio of Fashion E-commerce Products.- Improving MOEA/D with Knowledge Discovery. Application to a Bi-Objective Routing Problem.- The Prism-Net Search Space Representation for Multi-Objective Building Spatial Design.- Selection Strategies for a Balanced Multi- or Many-Objective Molecular Optimization and Genetic Diversity: a Comparative Study.- A Multi-objective Evolutionary Framework for Identifying Dengue Stage-Specific Differentially Co-expressed and Functionally Enriched Gene Modules.- A Multi-objective Evolutionary Framework for Identifying Dengue Stage-Specific Differentially Co-expressed and Functionally Enriched Gene Modules. -Multiobjective Optimization of Evolutionary Neural Networks for Animal Trade Movements Prediction.- Transfer of Multi-Objectively Tuned CMA-ES Parameters to a Vehicle Dynamics Problem.- Multi-Criteria Decision Making and Interactive Algorithms.- Preference-Based Nonlinear Normalization for Multiobjective Optimization.- Incorporating preference information interactively in NSGA-III by the adaptation of reference vectors.- A Systematic Way of Structuring Real-World Multiobjective Optimization Problems.- IK-EMOViz: An Interactive Knowledge-based Evolutionary Multi-objective Optimization Framework.- An Interactive Decision Tree-Based Evolutionary Multi-Objective Algorithm.

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Book SynopsisThis book constitutes the proceedings of the 20th International Conference on Relational and Algebraic Methods in Computer Science, RAMiCS 2023, which took place in Augsburg, Germany, during April 3–6, 2023. The 17 papers presented in this book were carefully reviewed and selected from 26 submissions. They deal with the development and dissemination of relation algebras, Kleene algebras, and similar algebraic formalisms. Topics covered range from mathematical foundations to applications as conceptual and methodological tools in computer science and beyond. Apart from the submitted articles, this volume features the abstracts of the presentations of the three invited speakers. Table of ContentsAmalgamation Property for Some Varieties of BL-algebras Generated by one Finite Set of BL-chains with Finitely-many Components.- Comer Schemes, Relation Algebras, and the Flexible Atom Conjecture.- A General Method for Representing Sets of Relations by Vectors.- Contextuality in Distributed Systems.- The Structure of Locally Integral Involutive Po-monoids and Semirings.- Compatibility of Refining and Controlling Plant Automata with Bisimulation Quotients.- Dependences Between Domain Constructions in Heterogeneous Relation Algebras.- Normal Forms for Elements of the *-Continuous Kleene Algebras K (x) C2’.- Representable and Diagonally rRpresentable Weakening Relation Algebras.- Completeness and the Finite Model Property for Kleene Algebra, Reconsidered.- What Else is Undecidable About Loops.- Implication Algebras and Implication Semigroups of Binary Relations.- On the Complexity of Kleene Algebra with Domain.- Enumerating, Cataloguing and Classifying all Quantales on up to Nine Elements.- Duoidally Enriched Freyd Categories.- Towards a Theory of Conversion Relations for Prefixed Units of Measure.- Relational Algebraic Approach to the Real Numbers - The Additive Group.

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Book SynopsisThis book constitutes the refereed proceedings of the 24th International Conference on Integer Programming and Combinatorial Optimization, IPCO 2023, held in Madison, WI, USA, during June 21–23, 2023. The 33 full papers presented were carefully reviewed and selected from 119 submissions. IPCO is under the auspices of the Mathematical Optimization Society, and it is an important forum for presenting present recent developments in theory, computation, and applications. The scope of IPCO is viewed in a broad sense, to include algorithmic and structural results in integer programming and combinatorial optimization as well as revealing computational studies and novel applications of discrete optimization to practical problems.

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Book SynopsisThis volume constitutes the proceedings of the 9th International Conference on Variable Neighborhood Search, ICVNS 2023, held in Abu Dhabi, United Arab Emirates, in October 2022.The 11 full papers presented in this volume were carefully reviewed and selected from 29 submissions. The papers describe recent advances in methods and applications of variable neighborhood search.Table of ContentsA metaheuristic approach for solving Monitor Placement Problem.- A VNS-based heuristic for the minimum number of resources under a perfect schedule.- BVNS for Overlapping Community Detection.- A Simulation-Based Variable Neighborhood Search Approach for Optimizing Cross-Training Policies.- Multi-Objective Variable Neighborhood Search for improving software modularity.- An Effective VNS for Delivery Districting.- BVNS for the Minimum Sitting Arrangement problem in a cycle.- Assigning Multi-Skill Confgurations to Multiple Servers with a Reduced VNS.- Multi-Round Infuence Maximization: A Variable Neighborhood Search Approach.- A VNS based heuristic for a 2D Open Dimension Problem.- BVNS for the bi-objective multi row equal facility layout problem.

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Book SynopsisThis book constitutes the proceedings of the 6th International Conference on Geometric Science of Information, GSI 2023, held in St. Malo, France, during August 30-September 1, 2023. The 125 full papers presented in this volume were carefully reviewed and selected from 161 submissions. They cover all the main topics and highlights in the domain of geometric science of information, including information geometry manifolds of structured data/information and their advanced applications. The papers are organized in the following topics: geometry and machine learning; divergences and computational information geometry; statistics, topology and shape spaces; geometry and mechanics; geometry, learning dynamics and thermodynamics; quantum information geometry; geometry and biological structures; geometry and applications.Table of ContentsGeometry and machine learning.- Divergences and computational information geometry.- Statistics, topology and shape spaces.- Geometry and mechanics.- Geometry, learning dynamics and thermodynamics.- Quantum information geometry.- Geometry and biological structures.- Geometry and applications.

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Book SynopsisThis book constitutes the proceedings of the 6th International Conference on Geometric Science of Information, GSI 2023, held in St. Malo, France, during August 30-September 1, 2023. The 125 full papers presented in this volume were carefully reviewed and selected from 161 submissions. They cover all the main topics and highlights in the domain of geometric science of information, including information geometry manifolds of structured data/information and their advanced applications. The papers are organized in the following topics: geometry and machine learning; divergences and computational information geometry; statistics, topology and shape spaces; geometry and mechanics; geometry, learning dynamics and thermodynamics; quantum information geometry; geometry and biological structures; geometry and applications.Table of ContentsGeometry and machine learning.- Divergences and computational information geometry.- Statistics, topology and shape spaces.- Geometry and mechanics.- Geometry, learning dynamics and thermodynamics.- Quantum information geometry.- Geometry and biological structures.- Geometry and applications.

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Book SynopsisThis book constitutes the refereed proceedings of the 17th International Joint Conference on Theoretical Computer Science-Frontier of Algorithmic Wisdom (IJTCS-FAW 2023), consisting of the 17th International Conference on Frontier of Algorithmic Wisdom (FAW) and the 4th International Joint Conference on Theoretical Computer Science (IJTCS), held in Macau, China, during August 14–18, 2023.FAW started as the Frontiers of Algorithmic Workshop in 2007 at Lanzhou, China, and was held annually from 2007 to 2021 and published archival proceedings. IJTCS, the International joint theoretical Computer Science Conference, started in 2020, aimed to bring in presentations covering active topics in selected tracks in theoretical computer science. To accommodate the diversified new research directions in theoretical computer science, FAW and IJTCS joined their forces together to organize an event for information exchange of new findings and work of enduring value in the field. The 21 full papers included in this book were carefully reviewed and selected from 34 submissions. They were organized in topical sections as follows: algorithmic game theory; algorithms and data structures; combinatorial optimization; and computational economics.Table of ContentsUnderstanding the Relationship Between Core Constraints and Core-Selecting Payment Rules in Combinatorial Auctions.- An Improved Analysis of the Greedy+Singleton Algorithm for k-Submodular Knapsack Maximization.- Generalized Sorting with Predictions Revisited.- Eliciting Truthful Reports with Partial Signals in Repeated Games.- On the NP-hardness of two scheduling problems under linear constraints.- On the Matching Number of k-Uniform Connected Hypergraphs with Maximum Degree.- Max-Min Greedy Matching Problem: Hardness for the Adversary and Fractional Variant.- Approximate Core Allocations for Edge Cover Games.- Random Approximation Algorithms for Monotone k-Submodular Function Maximization with Size Constraints.- Additive Approximation Algorithms for Sliding Puzzle.- Differential Game Analysis for Cooperation Models in Automotive Supply Chain under Low-Carbon Emission Reduction Policies.- Adaptivity Gap for Influence Maximization with Linear Threshold Model on Trees.- Physically Verifying the First Nonzero Term in a Sequence: Physical ZKPs for ABC End View and Goishi Hiroi.- Mechanism Design in Fair Sequencing.- Red-Blue Rectangular Annulus Cover Problem.- Applying Johnson's Rule in Scheduling Multiple Parallel Two-Stage Flowshops.- The Fair k-Center with Outliers Problem: FPT and Polynomial Approximations.- Constrained Graph Searching on Trees.- EFX Allocations Exist for Binary Valuations.- Maximize Egalitarian Welfare for Cake Cutting.- Stackelberg Strategies on Epidemic Containment Games.

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Book SynopsisThis volume constitutes the thoroughly refereed proceedings of the 49th International Workshop on Graph-Theoretic Concepts in Computer Science, WG 2023. The 33 full papers presented in this volume were carefully reviewed and selected from a total of 116 submissions. The WG 2022 workshop aims to merge theory and practice by demonstrating how concepts from graph theory can be applied to various areas in computer science, or by extracting new graph theoretic problems from applications.Table of ContentsProportionally Fair Matching with Multiple Groups.- Reconstructing Graphs from Connected Triples.- Parameterized Complexity of Vertex Splitting to Pathwidth at most 1.- Odd Chromatic Number of Graph Classes.- Deciding the Erdos-P osa property in 3-connected digraphs.- New Width Parameters for Independent Set: One-sided-mim-width and Neighbor-depth.- Computational Complexity of Covering Colored Mixed Multigraphswith Degree Partition Equivalence Classes of Size at Most Two.- Cutting Barnette graphs perfectly is hard.- Metric dimension parameterized by treewidth in chordal graphs.- Efficient Constructions for the Gyori-Lovasz Theorem on Almost Chordal Graphs.- Generating faster algorithms for d-Path Vertex Cover.- A new width parameter of graphs based on edge cuts: -edge-crossing width.- Snakes and Ladders: a Treewidth Story.- Parameterized Results on Acyclic Matchings with Implications for Related Problems.- P-matchings Parameterized by Treewidth.- Algorithms and hardness for Metric Dimension on digraphs.- Degreewidth : a New Parameter for Solving Problems on Tournaments.- Approximating Bin Packing with Con ict Graphs via Maximization Techniques.- i-Metric Graphs: Radius, Diameter and all Eccentricities.- Maximum edge colouring problem on graphs that exclude a xed minor.- Bounds on Functionality and Symmetric Di erence { Two Intriguing Graph Parameters.- Cops and Robbers on Multi-layer Graphs.- Parameterized Complexity of Broadcasting in Graphs.- Turan's Theorem Through Algorithmic Lens.- On the Frank number and nowhere-zero ows on graphs.- On the minimum number of arcs in 4-dicritical oriented graphs.- Tight Algorithms for Connectivity Problems Parameterized byModular-Treewidth.

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Book SynopsisThis book constitutes the refereed proceedings of the 11th International Conference on Code-Based Cryptography, CBCrypto 2023, held in Lyon, France, during April 22–23, 2023. The 8 full papers included in this book were carefully reviewed and selected from 28 submissions. The conference offers a wide range of many important aspects of code-based cryptography such as cryptanalysis of existing schemes, the proposal of new cryptographic systems and protocols as well as improved decoding algorithms.

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Book SynopsisThese two volumes constitute the proceedings of the 10th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2023, held in Kanazawa, Japan, during November 2-4, 2023. The 58 full papers presented were carefully reviewed and selected from 107 submissions. The papers deal with all aspects of research results, ideas, and experiences of application among researchers and practitioners involved with all aspects of uncertainty modelling and management.Table of ContentsUncertainty Management and Decision Making.- Optimization and Statistical Methods.- Economic Application

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Book SynopsisThis book constitutes the refereed proceedings of the 4th Southern African Conference on Artificial Intelligence Research, SACAIR 2023, held in Muildersdrift, South Africa, in December 2023. The 22 full papers presented in these proceedings were carefully reviewed and selected from 66 submissions. The papers are organized in the following topical sections: Responsible and Ethical AI Track; Socio-Technical and Human-Centered AI Track; Algorithmic, and Data Driven and Symbolic AI.Table of ContentsResponsible and Ethical AI track.- Emerging AI Discourses and Policies in the EU: Implications for Evolving AI Governance.- Intergenerational Justice as Driver for Responsible AI.- AI Literacy: A Primary Good.- Exploring the ethical and societal concerns of Generative AI in Internet of Things (IoT) environments.- Warfare in the Age of AI: A Critical Evaluation of ArkinÕs Case for Ethical Autonomy in Unmanned Systems.- Socio-technical and human-centered AI track.- The decision criteria used by large organisations in South Africa for adopting Artificial Intelligence.- Let’s play games: Using no-code AI to reduce human cognitive load during AI solution development.- Algorithmic, Data Driven and Symbolic AI.- Unit-Based Genetic Algorithmic Approach for Optimal Multipurpose Batch Plant Scheduling.- Investigating the extent and usability of webtext available in South Africa’s official languages.- Voice Conversion for Stuttered Speech, Instruments, Unseen Languages and Textually Described Voices.- Extending Defeasible Reasoning Beyond Rational Closure.- Sequence Based Deep Neural Networks for Channel Estimation in Vehicular Communication Systems.- Comparative Study of Image Resolution Techniques in the Detection of Cancer Using Neural Networks.- Investigating Frequent Pattern-based Models for Improving Community Policing in South Africa.- Financial Inclusion in Sub-Saharan Emerging Markets: The Application of Deep Learning to Improve Determinants.- Viability of Convolutional Variational Autoencoders for Lifelong Class Incremental Similarity Learning.- PuoBERTa: Training and evaluation of a curated language model for Setswana.- Hierarchical Text Classification using Language Models with Global Label-wise Attention Mechanisms.- Multimodal Misinformation Detection in a South African Social Media Environment.- Improving Semi-Supervised Learning in Generative Adversarial Networks.- Impacts of Architectural Enhancements on Sequential Recommendation Models.- A comparative study of over-sampling techniques as applied to seismic events.

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Book SynopsisThis book constitutes the proceedings of the 49th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2024, held in Cochem, Germany, in February 2024. The 33 full papers presented in this book were carefully reviewed and selected from 81 submissions. The book also contains one invited talk in full paper length. They focus on original research and challenges in foundations of computer science including algorithms, AI-based methods, computational complexity, and formal models.Table of ContentsThe Information Extraction Framework of Document Spanners - A Very Informal Survey.- Generalized Distance Polymatrix Games.- Relaxed agreement forests.- On the Computational Complexity of Generalized Common Shape Puzzles.- Fractional Bamboo Trimming and Distributed Windows Scheduling.- New support size bounds and proximity bounds for integer linear programming.- On the Parameterized Complexity of Minus Domination.- Exact and Parameterized Algorithms for Choosability.- Parameterized Algorithms for Covering by Arithmetic Progressions.- Row-column combination of Dyck words.- Group Testing in Arbitrary Hypergraphs and Related Combinatorial Structures.- On the parameterized complexity of the Perfect Phylogeny problem.- Data reduction for directed feedback vertex set on graphs without long induced cycles.- Visualization of Bipartite Graphs in Limited Window Size.- Outerplanar and Forest Storyplans.- The Complexity of Cluster Vertex Splitting and Company.- Morphing Graph Drawings in the Presence of Point Obstacles.- Word-Representable Graphs from a Word’s Perspective.- Removable Online Knapsack with Bounded Size Items.- The Complexity of Online Graph Games.- Faster Winner Determination Algorithms for (Colored) Arc Kayles.- Automata Classes Accepting Languages Whose Commutative Closure is Regular.- Shortest Characteristic Factors of a Deterministic Finite Automaton and Computing Its Positive Position Run by Pattern Set Matching.- Query Learning of Minimal Deterministic Symbolic Finite Automata Separating Regular Languages.- Apportionment with Thresholds: Strategic Campaigns Are Easy in the Top-Choice But Hard in the Second-Chance Mode.- Local Certification of Majority Dynamics.- Complexity of Spherical Equations in Finite Groups.- Positive Characteristic Sets for Relational Pattern Languages.- Algorithms and Turing Kernels for Detecting and Counting Small Patterns in Unit Disk Graphs.- The Weighted HOM-Problem over Fields.- Combinatorics of block-parallel automata networks.- On the piecewise complexity of words and periodic words.- Distance Labeling for Families of Cycles.- On the induced problem for fixed-template CSPs.

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Book SynopsisPython Essentials.- Number Theory.- Rational Arithmetic.- Matrix Algebra.- Polynomial Algebra.- Polynomial Applications.- Multivariate Rational Algebra.- Differentiation.- Integration.

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Book SynopsisOptimized Scheduling of Medical Appointment Sequences using Constraint Programming.- An integrated optimisation method for aluminium hot rolling.- Determining the Most Promising Selective Backbone Size for Partial Knowledge Compilation.- Leveraging Quantum Computing for Accelerated Classical Algorithms in Power Systems Optimization.- Hybridizing Machine Learning and Optimization for Planning Satellite Observations.- Algorithm Configuration in Sequential Decision-Making.- Self-Supervised Penalty-Based Learning for Robust Constrained Optimization.- Revisiting Pseudo-Boolean Encodings from an Integer Perspective.- Multi-task Representation Learning for Mixed Integer Linear Programming.- Breaking the Symmetries of Indistinguishable Objects.- Tackling Symmetry Breaking as a Symbolic Set Cover.- Modeling and Solving the Generalized Test Laboratory Scheduling Problem.- Parallelising Lazy Clause Generation with Trail Sharing.- Learning Primal Heuristics for 0–1 Knapsack Interdiction Problems.- Bounded-Error Policy Optimization for Mixed Discrete-Continuous MDPs via Constraint Generation in Nonlinear Programming.- Minimising Source-Plate Swaps for Robotised Compound Dispensing in Microplates.

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Book Synopsis Scientific Computing for Scientists and Engineers is designed to teach undergraduate students relevant numerical methods and required fundamentals in scientific computing. Most problems in science and engineering require the solution of mathematical problems, most of which can only be done on a computer. Accurately approximating those problems requires solving differential equations and linear systems with millions of unknowns, and smart algorithms can be used on computers to reduce calculation times from years to minutes or even seconds. This book explains: How can we approximate these important mathematical processes? How accurate are our approximations? How efficient are our approximations? Scientific Computing for Scientists and Engineers covers: An introduction to a wide range of numerical methods for linear systems, eigenvalue problems, differential equations, numerical integration, and nonlinear problems; Scientific computing fundamentals like floating point representation of numbers and convergence; Analysis of accuracy and efficiency; Simple programming examples in MATLAB to illustrate the algorithms and to solve real life problems; Exercises to reinforce all topics.

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Book SynopsisThis book analyzes the generation of the arrow-categories of a given category, which is a foundational and distinguishable Category Theory phenomena, in analogy to the foundational role of sets in the traditional set-based Mathematics, for defi nition of natural numbers as well. This inductive transformation of a category into the infinite hierarchy of the arrowcategories is extended to the functors and natural transformations. The author considers invariant categorial properties (the symmetries) under such inductive transformations. The book focuses in particular on Global symmetry (invariance of adjunctions) and Internal symmetries between arrows and objects in a category (in analogy to Field Theories like Quantum Mechanics and General Relativity). The second part of the book is dedicated to more advanced applications of Internal symmetry to Computer Science: for Intuitionistic Logic, Untyped Lambda Calculus with Fixpoint Operators, Labeled Transition Systems in Process Algebras and Modal logics as well as Data Integration Theory.

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Book SynopsisThis book provides an introduction to hypergraphs, its aim being to overcome the lack of recent manuscripts on this theory. In the literature hypergraphs have many other names such as set systems and families of sets. This work presents the theory of hypergraphs in its most original aspects, while also introducing and assessing the latest concepts on hypergraphs. The variety of topics, their originality and novelty are intended to help readers better understand the hypergraphs in all their diversity in order to perceive their value and power as mathematical tools. This book will be a great asset to upper-level undergraduate and graduate students in computer science and mathematics. It has been the subject of an annual Master's course for many years, making it also ideally suited to Master's students in computer science, mathematics, bioinformatics, engineering, chemistry, and many other fields. It will also benefit scientists, engineers and anyone else who wants to understand hypergraphs theory.Trade ReviewFrom the reviews:“This book addresses the mathematics and theory of hypergraphs. The target audience includes graduate students and researchers with an interest in math and computer science (CS). … I expect readers of this book will be motivated to advance this field, which in turn can advance other sciences.” (Hsun-Hsien Chang, Computing Reviews, January, 2014)“The aim of this book is to introduce the basic concepts of hypergraphs, to present the knowledge of the theory and applications of hypergraphs in other fields. … This book is useful for anyone who wants to understand the basics of hypergraph theory. It is mainly for math and computer science majors, but it may also be useful for other fields which use the theory. … appropriate for both researchers and graduate students. It is very well-written and proofs are stated in a clear manner.” (Somayeh Moradi, zbMATH, Vol. 1269, 2013)Table of ContentsHypergraphs: basic concepts.- Hypergraphs: first properties.- Hypergraph coloring.- Some particular hypergraphs.- Reduction-contraction of Hypergraph.- Dirhypergraphs: basic concepts.- Applications of hypergraph theory : a brief overview.

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Book SynopsisAdapted from a modular undergraduate course on computational mathematics, Concise Computer Mathematics delivers an easily accessible, self-contained introduction to the basic notions of mathematics necessary for a computer science degree. The text reflects the need to quickly introduce students from a variety of educational backgrounds to a number of essential mathematical concepts. The material is divided into four units: discrete mathematics (sets, relations, functions), logic (Boolean types, truth tables, proofs), linear algebra (vectors, matrices and graphics), and special topics (graph theory, number theory, basic elements of calculus). The chapters contain a brief theoretical presentation of the topic, followed by a selection of problems (which are direct applications of the theory) and additional supplementary problems (which may require a bit more work). Each chapter ends with answers or worked solutions for all of the problems.Trade ReviewFrom the reviews:“The book is ideally suited as an adjunct to a course in computer mathematics or as a refresher for someone with some background in computer mathematics. … The book fulfills its purpose of providing a distilled treatment of the mathematics most commonly used in computer science. It is of most value to computer science students who need a place to find a succinct treatment of the topics covered.” (Marlin Thomas, Computing Reviews, April, 2014)“Each of the chapters opens with a short summary followed by a set of essential problems and then a set of supplementary problems. … it would be very useful for someone that needs a quick and effective review that includes problems.” (Charles Ashbacher, MAA Reviews, January, 2014)Table of ContentsSets and NumbersRelations and DatabasesFunctionsBoolean Algebra, Logic and QuantifiersNormal Forms, Proof and ArgumentVectors and Complex NumbersMatrices and ApplicationsMatrix Transformations for Computer GraphicsElements of Graph TheoryElements of Number Theory and CryptographyElements of CalculusElementary Numerical Methods

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