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The two-volume set CCIS 1896 and 1897 constitutes the refereed post-conference proceedings of the 5th International Conference on Blockchain and Trustworthy Systems, BlockSys 2023, which took place in Haikou, China during August 8-10, 2023. The 45 revised full papers presented in these proceedings were carefully reviewed and selected from 93 submissions. The papers are organized in the following topical sections: Part I: Anomaly detection on blockchain; edge intelligence and metaverse services; blockchain system security; empirical study and surveys; federated learning for blockchain. Part II: AI for blockchain; blockchain applications; blockchain architecture and optimization; protocols and consensus.
This book constitutes the refereed proceedings of the 5th International Conference on Science of Cyber Security, SciSec 2023, held in Melbourne, VIC, Australia, during July 11¿14, 2023. The 21 full papers presented together with 6 short papers were carefully reviewed and selected from 60 submissions. The papers are organized in the topical sections named: ¿ACDroid: Detecting Collusion Applications on Smart Devices; Almost Injective and Invertible Encodings for Jacobi Quartic Curves; Decompilation Based Deep Binary-Source Function Matching.
This volume constitutes selected papers presented at the 10th International Conference on Innovation and New Trends in Information Technology, INTIS 2022, held in Casablanca, Morocco, in May 2022, and 11th International Conference on Innovation and New Trends in Information Technology, INTIS 2023, held in Tangier, Morocco, in May 2023.After the thorough peer review process, 4 papers were selected from the 27 submissions received for INTIS 2022, and 11 papers were selected from the 33 submissions received for INTIS 2023. The presented papers cover the mail topics of data-enabled systems/applications: data source layer, network layer, data layer, learning layer, and reporting layers while considering non-functional properties such as data privacy, security, and ethics.
This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications.
This book constitutes revised selected papers from the refereed proceedings of the 17th Colombian Conference on Computing on Advances in Computing, CCC 2023, held in Medellin, Colombia, during August 10¿11, 2023.The 22 full papers and 11 short papers included in this book were carefully reviewed and selected from 68 submissions. They were organized in topical sections as follows: Industrial Applications - Industry 4.0 - Precision Agriculture, Artificial Intelligence, Distributed systems and large-scale computing, Computational Statistics, Digital Learning - E-learning, Software Engineering, Human Machine Interaction, Image processing and Computer Vision, Robotics in Industry 4.0 and Scientific Applications.
This book constitutes the proceedings of the 24th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023, held in Évora, Portugal, during November 22¿24, 2023.The 45 full papers and 4 short papers presented in this book were carefully reviewed and selected from 77 submissions. IDEAL 2023 is focusing on big data challenges, machine learning, deep learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models, agents and hybrid intelligent systems, and real-world applications of intelligence techniques and AI.The papers are organized in the following topical sections: main track; special session on federated learning and (pre) aggregation in machine learning; special session on intelligent techniques for real-world applications of renewable energy and green transport; and special session on data selection in machine learning.
This book presents a methodology for the real-time scheduling problems of real-time systems (RTS) from the viewpoint of control theory. Generally, any system can be viewed as an RTS if it performs real-time application functions and behaves correctly depending on given logical activities and satisfying specified deadlines for the activities. This monograph provides broad views and detailed introductions to supervisory control theory (SCT) and its application in real-time scheduling and reconfiguration. Based on three popular SCT modelling frameworks, discrete-event system (DES), timed DES (TDES), and state-tree structures (STS), the authors provide RTS modelling frameworks; thereafter, SCT is used to find their safe execution sequences.As the main contribution, we use (untimed) DES events to represent the execution and preemption of each individual RTS task. This modelling formalism brings the possibilities to model the preemptions of tasks¿ executions. Furthermore, in some cases, priorities cannot be assigned to real-time tasks. In order to solve this problem, a matrix-based priority-free conditional-preemption (PFCP) relation is provided, which generalizes fixed-priority (FP) RTS scheduling. As a natural extension, a generalized modular modelling framework is presented to model the task parameters instead of the global real-time task. The modular models are taken to be generic entities, which also considers the exact execution time of real-time tasks. STS are undoubtedly recognized as a computationally efficient SCT framework which manages the state explosion problem significantly. Hence, building on the (untimed) modular RTS models, a novel STS-based RTS modeling framework is formulated, by assigning dynamic priorities as specified optimality criteria, which can be utilized to model sporadic RTS processing both sporadic and (multi-period) periodic tasks, providing a small set of the safe execution sequences which rank at the top.
Descubre el Futuro de los Negocios: La Revolución de la Inteligencia Artificial ¿Te has preguntado cómo la inteligencia artificial está transformando radicalmente la forma en que concebimos los negocios? Este libro te desvelará las respuestas que estás buscando. Desde la automatización de tareas rutinarias hasta la personalización sin precedentes de la experiencia del cliente, explorarás cómo la IA está generando un impacto sin igual en empresas de todas las industrias. Aprenderás a implementar estrategias de inteligencia artificial que marcarán la diferencia, encontrarás herramientas poderosas que te llevarán a la vanguardia y conocerás casos de éxito que te inspirarán a trazar tu propio camino empresarial. Con más de 50 guías y consejos prácticos a tu disposición, estarás armado con las herramientas necesarias para triunfar en tu negocio.¡Prepárate para asumir el liderazgo en la revolución de la IA en el mundo empresarial! No dejes pasar esta oportunidad, obtén tu copia ahora y comienza a construir el futuro de tu empresa hoy mismo. Tu éxito comienza con este libro.
Artificial intelligence (AI) is the study and development of computer systems capable of performing activities normally requiring human intelligence. Among these are such activities as linguistic comprehension, pattern recognition, problem solving, and experiential learning. Scientists, academics, and the general public alike have become fascinated by the idea of AI, which has led to tremendous technological advances. From its origins in ancient mythology to its applications in cutting-edge AI research today, this essay presents a comprehensive review of the field. Myths and Theories from the PastAncient cultures and mythologies frequently feature artificial entities with human-level intellect. Hephaestus, the Greek deity of blacksmiths and craftspeople, was able to accomplish much more with the help of his mechanical workers. Similar accounts of automata and artificial birds that may pass for human are found in ancient Chinese and Egyptian writings. These early ideas paved the way for the modern interest with building artificially intelligent devices. When Artificial Intelligence Began: Alan Turing and the Turing TestThe seminal work of British mathematician and logician Alan Turing in the middle of the 20th century marked the beginning of the contemporary era of artificial intelligence. To assess whether or not a machine is capable of intelligent behavior that is indistinguishable from that of a person, Turing developed a test in 1950. The Turing Test is an important notion in the history of artificial intelligence. Interest in creating machines that could mimic human mind was aroused by Turing's ideas. Expert Systems and the Coming "AI Winter"Expert systems were made possible by breakthroughs in artificial intelligence research in the 1960s and 1970s. These programs have the ability to tackle difficult problems and offer insightful analysis since they were developed to emulate human competence in specific subjects. However, the initial euphoria surrounding AI dissipated in the 1980s due to false expectations, technical constraints, and a lack of financing. The AI winter was a temporary lull in the progress of AI studies. The Explosion of Neural Networks and Machine LearningImprovements in machine learning and neural networks sparked a revival of interest in AI in the latter part of the 20th century. To enable computers to learn from data, recognize patterns, and make predictions without explicit programming, machine learning algorithms were developed. Inspired by the structure of the human brain, neural networks became an essential part in creating advanced AI systems that can do tasks such as image identification and natural language processing.
This book provides a coherent and complete overview of various Question Answering (QA) systems. It covers three main categories based on the source of the data that can be unstructured text (TextQA), structured knowledge graphs (KBQA), and the combination of both. Developing a QA system usually requires using a combination of various important techniques, including natural language processing, information retrieval and extraction, knowledge graph processing, and machine learning.After a general introduction and an overview of the book in Chapter 1, the history of QA systems and the architecture of different QA approaches are explained in Chapter 2. It starts with early close domain QA systems and reviews different generations of QA up to state-of-the-art hybrid models. Next, Chapter 3 is devoted to explaining the datasets and the metrics used for evaluating TextQA and KBQA. Chapter 4 introduces the neural and deep learning models used in QA systems. This chapter includes the required knowledge of deep learning and neural text representation models for comprehending the QA models over text and QA models over knowledge base explained in Chapters 5 and 6, respectively. In some of the KBQA models the textual data is also used as another source besides the knowledge base; these hybrid models are studied in Chapter 7. In Chapter 8, a detailed explanation of some well-known real applications of the QA systems is provided. Eventually, open issues and future work on QA are discussed in Chapter 9.This book delivers a comprehensive overview on QA over text, QA over knowledge base, and hybrid QA systems which can be used by researchers starting in this field. It will help its readers to follow the state-of-the-art research in the area by providing essential and basic knowledge.
This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras.This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.
This book constitutes the refereed proceedings of the 22nd IFIP TC 14 International Conference on Entertainment Computing, ICEC 2023, which was held in Bologna, Italy, during November 15¿17, 2023.The 13 full papers, 5 short papers, 8 work-in-progress papers, 7 interactive entertainment demonstrations, 2 student competition papers, 5 workshop papers and tutorials, and 10 papers from a special section on aesthetics and empowerment were carefully reviewed and selected from 85 submissions. They cover a large range of topics in the following thematic areas: Game Experience; Player Engagement and Analysis; Serious Gameplay; Entertainment Methods and Tools; Extended Reality; Game Design; Interactive Entertainment; Student Game Competition; Workshops and Tutorials; and Aesthetics and Empowerment. .
This book constitutes the refereed proceedings of the XXIInd International Conference on AIxIA 2023 ¿ Advances in Artificial Intelligence, AIxIA 2023, held in Rome, Italy, during November 6¿10, 2023.The 33 full papers included in this book were carefully reviewed and selected from 53 submissions. They were organized in topical sections as follows: Argumentation and Logic Programming, Natural Language Processing, Machine Learning, Hybrid AI and Applications of AI.
A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models.Purchase of the print or Kindle book includes a free eBook in PDF format.Key Features:Interpret real-world data, including cardiovascular disease data and the COMPAS recidivism scoresBuild your interpretability toolkit with global, local, model-agnostic, and model-specific methodsAnalyze and extract insights from complex models from CNNs to BERT to time series modelsBook Description:Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models.Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps.In addition to the step-by-step code, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability.By the end of the book, you'll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.What You Will Learn:Progress from basic to advanced techniques, such as causal inference and quantifying uncertaintyBuild your skillset from analyzing linear and logistic models to complex ones, such as CatBoost, CNNs, and NLP transformersUse monotonic and interaction constraints to make fairer and safer modelsUnderstand how to mitigate the influence of bias in datasetsLeverage sensitivity analysis factor prioritization and factor fixing for any modelDiscover how to make models more reliable with adversarial robustnessWho this book is for:This book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It's also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples.
This book constitutes the proceedings of the 19th International Workshop on Security and Trust Management, STM 2023, co-located with the 28th European Symposium on Research in Computer Security, ESORICS 2023, held in The Hague, The Netherlands, during September 28th, 2023 The 5 full papers together with 4 short papers included in this volume were carefully reviewed and selected from 15 submissions. The workshop presents papers with topics such as security and privacy, trust models, security services, authentication, identity management, systems security, distributed systems security, privacy-preserving protocols.
In ancient games such as chess or go, the most brilliant players can improve by studying the strategies produced by a machine. Robotic systems practice their own movements. In arcade games, agents capable of learning reach superhuman levels within a few hours. How do these spectacular reinforcement learning algorithms work? With easy-to-understand explanations and clear examples in Java and Greenfoot, you can acquire the principles of reinforcement learning and apply them in your own intelligent agents. Greenfoot (M.Klling, King's College London) and the hamster model (D. Bohles, University of Oldenburg) are simple but also powerful didactic tools that were developed to convey basic programming concepts. The result is an accessible introduction into machine learning that concentrates on reinforcement learning. Taking the reader through the steps of developing intelligent agents, from the very basics to advanced aspects, touching on a variety of machine learning algorithms along the way, one is allowed to play along, experiment, and add their own ideas and experiments.
If you are aware of the potential of AI and ChatGPT and you want to learn how to make an income, but you don't know where to start, then this book is for you.In this book, I'll break down the complexities of AI into understandable, easy-to-apply concepts that anyone can grasp. Whether you are an entrepreneur, freelancer, student, or a 9-5 employee, this book can help you adapt and know how to monetize. We provide different business examples where you can use AI to scale quickly.Here are a few things we cover in the book:Various Plugins and Extensions: Discover the utility of various tools to enhance your use of ChatGPT.Unique Business Examples: Real-life examples of successful online businesses that can leverage AI to scale quicklyPersonalized Business Search: Learn techniques to identify and evaluate online business opportunities tailored to your skills, interests, and market trends.7 Life-Altering Hacks: The hacks are specific ways and tools to enhance your productivity, transform your life and improve your lifestyle. They have the power to revolutionize your life.Extensive Prompt Examples: We provide a diverse collection of prompt examples meticulously curated for various aspects of progress.Master Prompt Creation: on top of the examples, you get a practical guide for creating effective prompts, including handling lengthy and complicated ones for different aspects of life and business.Adapt AI to Life's Many Facets: Discover comprehensive strategies to integrate ChatGPT and AI into different aspects of life ( Business, learning productivity, and creativity)This book comes with a bonus gift, which is a downloadable PDF aimed at assisting you in exploring various career paths available to you, both within and outside of college. The PDF is loaded with valuable information about different industries, diverse jobs, and career trajectories that you could potentially use.This book will help you adapt and generate income streams with relevant skills you possess or could develop.You may be speculating whether this book will work for you or not. Let me tell you it will give you a foundation of what to do.If you're thinking..."Idk if it would help me or not": This book isn't just theoretical; We provide plenty of practical insights about AI and income. We provide you with business examples, all the ways to catch the AI trend, personalized business searches, and many other things that are practical. Take it, personalize it for you, and enhance it."I don't have a technical background": You don't need it. This book explains the concepts in a simple, easy-to-understand language, and AI is very simple to use."AI is a passing trend": NOT EVEN. AI is still in its infancy, and it's not going anywhere. On the contrary, it will become a more part of our lives as time passes. By 2050 AI will be even more prominent than it is now."The book's contents might become outdated quickly" The content in this book will still be relevant for years to come; we cover many aspects of how the world is going to look like in 2030 and beyond-expected changes, ways to make money that will still exist, and how to be prepared for the new economy.No matter your stage in life or level of AI knowledge, you stand to gain from this book. If you're ready to step into the world of AI and unlock a prosperous future. Make your move, and add this valuable resource to your cart today.
This book constitutes the refereed proceedings of 7 workshops, held at the 42nd International Conference on Conceptual Modeling, ER 2023, held in Lisbon, Portugal, during November 6-9, 2023.The 28 full and 2 short papers were carefully reviewed and selected out of 53 submissions. Topics of interest span the entire spectrum of conceptual modeling, including research and practice in areas such as theories of concepts and ontologies, techniques for transforming conceptual models into effective implementations, and methods and tools for developing and communicating conceptual models. The following workshops are included in this volume: CMLS ¿ 4th International Workshop on Conceptual Modeling for Life Sciences;CMOMM4FAIR ¿ Third Workshop on Conceptual Modeling, Ontologies and (Meta)data Management for Findable, Accessible, Interoperable, and Reusable (FAIR) Data;EmpER ¿ 6th International Workshop on Empirical Methods in Conceptual Modeling;JUSMOD ¿ Second International Workshop on Digital Justice, Digital Law and Conceptual Modeling;OntoCom ¿ 9th International Workshop on Ontologies and Conceptual Modeling;QUAMES ¿ 4th International Workshop on Quality and Measurement of Model-Driven Software Development;SmartFood ¿ First Workshop on Controlled Vocabularies and Data Platforms for Smart Food Systems.
This book constitutes the refereed proceedings of the 16th International Conference on Similarity Search and Applications, SISAP 2023, held in A Coruña, Spain, during October 9¿11, 2023.The 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 33 submissions. They were organized in topical sections as follows: similarity queries, similarity measures, indexing and retrieval, data management, feature extraction, intrinsic dimensionality, efficient algorithms, similarity in machine learning and data mining.