Databaser

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 volume presents the proceedings of the 4th International Scientific and Practical Conference on Digital Economy and Finances (DEFIN22) at the Saint-Petersburg University of Management Technologies and Economics (UMTE), which took place in March 2022. It includes the newest research on the impact of new digital technologies on the growth and capitalization of companies and the labor market. The volume discusses the problems of situational modeling of economic processes and the creation of "e;digital twins"e; of enterprises. The contributions analyse how big data and artificial intelligence technologies are shaping the financial markets.

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 emphasizes the latest developments and achievements in AI and related technologies with a special focus on food quality. The book describes the applications, and conceptualization of ideas, and critical surveys covering most aspects of AI for food quality.

This handbook aims to serve as a one-stop, reliable source of reference, with curations of survey and expository contributions on the state-of-the-art in Blockchain technology. It covers a comprehensive range of topics, providing the technical and non-technical reader with fundamentals, applications, and deep details on a variety of topics. The readership is expected to span broadly from technologically-minded business professionals and entrepreneurs, to students, instructors, novices and seasoned researchers, in computer science, engineering, software engineering, finance, and data science. Though Blockchain technology is relatively young, its evolution as a field and a practice is booming in growth and its importance to society had never been more important than it is today. Blockchain solutions enable a decentralization of a digital society where people can contribute, collaborate, and transact without having to second-guess the trust and transparency factors with many geographical, financial, and political barriers removed. It is the distributed ledger technology behind the success of Bitcoin, Ethereum, and many emerging applications.The resource is divided into 5 parts. Part 1 (Foundation) walks the reader through a comprehensive set of essential concepts, protocols, and algorithms that lay the foundation for Blockchain. Part 2 (Scalability) focuses on the most pressing challenges of today's blockchain networks in how to keep pace with real-world expectations. Part 3 (Trust and Security) provides detailed coverage on the issues of trust, reputation, and security in Blockchain.  Part 4 (Decentralized Finance) is devoted to a high-impact application of Blockchain to finance, the sector that has most benefitted from this technology. Part 5 (Application and Policy) includes several cases where Blockchain applies to the real world.

The main objective of this book is to explore the synergy between cutting-edge AI technologies and environmentally conscious practices through collecting best selected research papers presented at the International Conference on Artificial Intelligence and Green Computing (ICAIGC 2023), which took place from March 15 to 17, 2023, in Beni Mellal, Morocco.Within the pages of this book, readers find a wealth of research findings, survey works, and practical experiences aimed at fostering a comprehensive understanding of the pivotal role AI plays in various fields, including agriculture, health care, IT, and more. It highlights both the opportunities presented by the widespread usage of AI and the challenges associated with its continued advancement. As a result, the book has been divided into three parts: 1)- AI for multimedia processing, 2)- AI for distributed computing, and 3)- AI applications.The book serves as a comprehensive resource that brings together on-goingresearch and practical experiences from the ICAIGC 2023 conference. It strives to deepen the understanding of the essential role AI plays in multiple fields. Whether you are an AI enthusiast, researcher, or practitioner, the insights contained within these pages expand your horizons and inspire further exploration of AI's potential in shaping a greener and more technologically advanced future.

Im Zentrum dieses essentials steht die Kurzdarstellung des praxisrelevanten kollektiven Arbeitsrechts. Maria Dimartino erklärt prägnant die wichtigsten Begriffe und bietet eine Übersicht des Betriebsverfassungsrechts, der Beteiligungsrechte von Arbeitnehmergremien, des betrieblichen Eingliederungsmanagements, der Grundlagen des Tarifrechts, des Streikrechts, der Arbeitsgerichtsverfahren sowie weiterer Arbeitnehmergremien im Unternehmen. Das essential ist speziell auf die Bedürfnisse von Praktikern zugeschnitten. Neu in der 2. Auflage sind die Themen Einsatz von KI und betriebliches Eingliederungsmanagement.

With approximately 2500 problems, this book provides a collection of practical problems on the basic and advanced data structures, design, and analysis of algorithms. To make this book suitable for self-instruction, about one-third of the algorithms are supported by solutions, and some others are supported by hints and comments. This book is intended for students wishing to deepen their knowledge of algorithm design in an undergraduate or beginning graduate class on algorithms, for those teaching courses in this area, for use by practicing programmers who wish to hone and expand their skills, and as a self-study text for graduate students who are preparing for the qualifying examination on algorithms for a Ph.D. program in Computer Science or Computer Engineering. About all, it is a good source for exam problems for those who teach algorithms and data structure. The format of each chapter is just a little bit of instruction followed by lots of problems. This book is intended to augment the problem sets found in any standard algorithms textbook. This book * begins with four chapters on background material that most algorithms instructors would like their students to have mastered before setting foot in an algorithms class. The introductory chapters include mathematical induction, complexity notations, recurrence relations, and basic algorithm analysis methods. * provides many problems on basic and advanced data structures including basic data structures (arrays, stack, queue, and linked list), hash, tree, search, and sorting algorithms. * provides many problems on algorithm design techniques: divide and conquer, dynamic programming, greedy algorithms, graph algorithms, and backtracking algorithms. * is rounded out with a chapter on NP-completeness.

This book has the merit of being the first book analysing different aspects of data taxation from a wide perspective encompassing not only tax law but also other significant issues related to data, such as data protection and economic inefficiencies. The main aim is to provide data-specific solutions to data-driven problems. In the midst of a number of critical issues and a great deal of uncertainty currently reigning in the field, the authors attempt to put forward easy-to-implement and efficient proposals on the basis of an interdisciplinary analysis. The core idea of this book consists of segregating the utilisation of data into four different yet interdependent steps and constructing the tax law analysis on top of these four corner stones. Step one, occurring in the generation and collection phases of the datäs life cycle, comprises ¿the digital barter¿ and other collection of data. Step two, taking place during the processing and analytics phasesof the datäs life cycle, consists of microwork. Step three, situated in the storage, processing, analytics and use phases of the datäs life cycle, encompasses aggregation and internal use of data. Step four, materialising during the distribution and use phases of the datäs life cycle, covers sale of data, transfer of data and granting the right to use a database. The main issues occurring in each of the four steps are analysed separately, and yet interdependently, with an emphasis on international tax law. The book also comprises a VAT analysis; suggestion of a new type of tax, namely «data collection tax»; and a brief opinion on a potential future «robo-data tax». The subjects explored in this book are of interest for researchers, lawyers as well as tax administrations. Albeit being an academic publication, the developments made in this contribution are also relevant for the general public. After all, data, the youngest intangible, constitute the raw material of the fourth industrial revolution; and their use and taxation affect each and every citizen!

Get to grips with the fundamental concepts of data engineering, and solve mock interview questions while building a strong resume and a personal brand to attract the right employersKey FeaturesDevelop your own brand, projects, and portfolio with expert help to stand out in the interview roundGet a quick refresher on core data engineering topics, such as Python, SQL, ETL, and data modelingPractice with 50 mock questions on SQL, Python, and more to ace the behavioral and technical roundsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionPreparing for a data engineering interview can often get overwhelming due to the abundance of tools and technologies, leaving you struggling to prioritize which ones to focus on. This hands-on guide provides you with the essential foundational and advanced knowledge needed to simplify your learning journey.The book begins by helping you gain a clear understanding of the nature of data engineering and how it differs from organization to organization. As you progress through the chapters, you'll receive expert advice, practical tips, and real-world insights on everything from creating a resume and cover letter to networking and negotiating your salary. The chapters also offer refresher training on data engineering essentials, including data modeling, database architecture, ETL processes, data warehousing, cloud computing, big data, and machine learning. As you advance, you'll gain a holistic view by exploring continuous integration/continuous development (CI/CD), data security, and privacy. Finally, the book will help you practice case studies, mock interviews, as well as behavioral questions.By the end of this book, you will have a clear understanding of what is required to succeed in an interview for a data engineering role.What you will learnCreate maintainable and scalable code for unit testingUnderstand the fundamental concepts of core data engineering tasksPrepare with over 100 behavioral and technical interview questionsDiscover data engineer archetypes and how they can help you prepare for the interviewApply the essential concepts of Python and SQL in data engineeringBuild your personal brand to noticeably stand out as a candidateWho this book is forIf you're an aspiring data engineer looking for guidance on how to land, prepare for, and excel in data engineering interviews, this book is for you. Familiarity with the fundamentals of data engineering, such as data modeling, cloud warehouses, programming (python and SQL), building data pipelines, scheduling your workflows (Airflow), and APIs, is a prerequisite.Table of ContentsThe Roles and Responsibilities of a Data EngineerMust-Have Data Engineering Portfolio ProjectsBuilding Your Data Engineering Brand on LinkedInPreparing for Behavioral InterviewsEssential Python for Data EngineersUnit TestingDatabase FundamentalsEssential SQL for Data EngineersDatabase Design and OptimizationData Processing and ETLData Pipeline Design for Data EngineersData Warehouses and Data LakesEssential Tools You Should Know AboutContinuous Integration/Continuous Development for Data EngineersData Security and PrivacyAdditional Interview Questions

COVID-19 hat die Sportkommunikation nachhaltig verändert. Der Band bietet empirische Analysen, Einschätzungen und theoretische Ansätze sowie Erfahrungsberichte aus der Praxis zum Einfluss der Corona-Pandemie auf Medien, Journalismus und Sport. Aus unterschiedlichen Perspektiven schauen Wissenschaftlerinnen und Wissenschaftler sowie Personen aus der Praxis auf die Veränderungen in verschiedenen Medien- und Sportorganisationen sowie den Wandel von Produktions- und Arbeitsbedingungen innerhalb der Sportkommunikation. Gleichzeitig lassen sich erste Prognosen für einen Ausblick auf die Zukunft des Mediensports ableiten.

Psychotherapeutinnen und Psychotherapeuten behandeln Menschen, die durch familiäre Erziehung, durch schulische und berufliche Bildung geprägt wurden. Nicht selten gehen psychische Erkrankungen mit sozialen Problemen einher, die eine sozial- oder rehabilitations­pädagogische Begleitung erfordern. In diesem Buch finden angehende Psychotherapeutinnen und Psychotherapeuten eine anwendungsorientierte Einführung in Erziehungs- und Sozialisationsprozesse sowie pädagogische Interventionen. Die Bedeutung der einzelnen Themen für die psychotherapeutische Praxis wird in jedem Kapitel gesondert erläutert.

This new edition will help you learn PostgreSQL from scratch with the latest version, providing a complete focused view on aspects like configuration, high performance, partitioning, backup, server-side programming and replication.Purchase of the print or Kindle book includes a free eBook in PDF format.Key Features:Learn the fundamentals of PostgreSQL 16, including SQL statements, replication, and securityEnhance your learning journey with the provided Docker images for practical hands-on exercises and tests at the end of each chapterGet new and improved examples, use-cases, and scenarios specifically for concepts like partitioning, replication, back-up and restore, cluster configuration, monitoring and othersBook Description:The latest edition of this PostgreSQL book will help you to start using PostgreSQL from absolute scratch, helping you to quickly understand the internal workings of the database. With a structured approach and practical examples, go on a journey that covers the basics, from SQL statements and how to run server-side programs, to configuring, managing, securing, and optimizing database performance.This new edition will not only help you get to grips with all the recent changes within the PostgreSQL ecosystem but will also dig deeper into concepts like partitioning and replication with a fresh set of examples. The book is also equipped with Docker images for each chapter which makes the learning experience faster and easier. Starting with the absolute basics of databases, the book sails through to advanced concepts like window functions, logging, auditing, extending the database, configuration, partitioning, and replication. It will also help you seamlessly migrate your existing database system to PostgreSQL and contains a dedicated chapter on disaster recovery. Each chapter ends with practice questions to test your learning at regular intervals.By the end of this book, you will be able to install, configure, manage, and develop applications against a PostgreSQL database. What You Will Learn:Gain a deeper understanding of PostgreSQL internals like transactions, MVCC, security and replicationEnhance data management with PostgreSQL's latest partitioning featuresChoose the right replication strategy for your databaseSee concrete examples of how to migrate data from another database, perform backups and restores, monitor your PostgreSQL installation and moreEnsure security and compliance with schemas and user privilegesCreate customized database functions and extensionsGet to grips with server-side programming, window functions, and triggersWho this book is for:Learning PostgresSQL 16 book is for anyone interested in learning about the PostgreSQL database from scratch. Anyone looking to build robust data warehousing applications and scale the database for high-availability and performance using the latest features of PostgreSQL will also find this book useful. Although prior knowledge of PostgreSQL is not required, familiarity with databases is expected.

Looking to revolutionize your data transformation game with AWS? Look no further! From strong foundations to hands-on building of data engineering pipelines, our expert-led manual has got you covered.Key FeaturesDelve into robust AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelinesStay up to date with a comprehensive revised chapter on Data GovernanceBuild modern data platforms with a new section covering transactional data lakes and data meshBook DescriptionThis book, authored by a seasoned Senior Data Architect with 25 years of experience, aims to help you achieve proficiency in using the AWS ecosystem for data engineering. This revised edition provides updates in every chapter to cover the latest AWS services and features, takes a refreshed look at data governance, and includes a brand-new section on building modern data platforms which covers; implementing a data mesh approach, open-table formats (such as Apache Iceberg), and using DataOps for automation and observability.You'll begin by reviewing the key concepts and essential AWS tools in a data engineer's toolkit and getting acquainted with modern data management approaches. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how that transformed data is used by various data consumers. You'll learn how to ensure strong data governance, and about populating data marts and data warehouses along with how a data lakehouse fits into the picture. After that, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. Then, you'll explore how the power of machine learning and artificial intelligence can be used to draw new insights from data. In the final chapters, you'll discover transactional data lakes, data meshes, and how to build a cutting-edge data platform on AWS.By the end of this AWS book, you'll be able to execute data engineering tasks and implement a data pipeline on AWS like a pro!What you will learnSeamlessly ingest streaming data with Amazon Kinesis Data FirehoseOptimize, denormalize, and join datasets with AWS Glue StudioUse Amazon S3 events to trigger a Lambda process to transform a fileLoad data into a Redshift data warehouse and run queries with easeVisualize and explore data using Amazon QuickSightExtract sentiment data from a dataset using Amazon ComprehendBuild transactional data lakes using Apache Iceberg with Amazon AthenaLearn how a data mesh approach can be implemented on AWSWho this book is forThis book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts, while gaining practical experience with common data engineering services on AWS, will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book, but it's not a prerequisite. Familiarity with the AWS console and core services will also help you follow along.Table of ContentsAn Introduction to Data EngineeringData Management Architectures for AnalyticsThe AWS Data Engineer's ToolkitData Governance, Security, and CatalogingArchitecting Data Engineering PipelinesIngesting Batch and Streaming DataTransforming Data to Optimize for AnalyticsIdentifying and Enabling Data ConsumersA Deeper Dive into Data Marts and Amazon RedshiftOrchestrating the Data Pipeline(N.B. Please use the Look Inside option to see further chapters)

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.

This book provides a new model to explore discoverability and enhance the meaning of information. The authors have coined the term epidata, which includes items and circumstances that impact the expression of the data in a document, but are not part of the ordinary process of retrieval systems.  Epidata affords pathways and points to details that cast light on proximities that might otherwise go unknown.  In addition, epidata are clues to mis-and dis-information discernment.  There are many ways to find needed information; however, finding the most useable information is not an easy task.  The book explores the uses of proximity and the concept of epidata that increases the probability of  finding functional information.  The authors sketch a constellation of proximities, present examples of attempts to accomplish proximity, and provoke a discussion of the role of proximity in the field. In addition, the authors suggest that proximity is a thread between retrieval constructs based on known topics, predictable relations, and types of information seeking that lie outside constructs such as browsing, stumbling, encountering, detective work, art making, and translation.

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.  

This book highlights selected papers from the 4th ICSA-Canada Chapter Symposium, as well as invited articles from established researchers in the areas of statistics and data science. It covers a variety of topics, including methodology development in data science, such as methodology in the analysis of high dimensional data, feature screening in ultra-high dimensional data and natural language ranking; statistical analysis challenges in sampling, multivariate survival models and contaminated data, as well as applications of statistical methods. With this book, readers can make use of frontier research methods to tackle their problems in research, education, training and consultation.

Master the art of machine learning by using the one-of-a-kind Qlik platform, and take your data analytics skills to the next levelKey FeaturesGain a solid understanding of machine learning concepts and learn to effectively define a problemExplore the application of machine learning principles within the Qlik platformApply your knowledge of ML to real-world scenarios with the help of practical examplesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe ability to forecast future trends through data prediction, coupled with the integration of ML and AI, has become indispensable to global enterprises. Qlik, with its extensive machine learning capabilities, stands out as a leading analytics platform enabling businesses to achieve exhaustive comprehension of their data. This book helps you maximize these capabilities by using hands-on illustrations to improve your ability to make data-driven decisions.You'll begin by cultivating an understanding of machine learning concepts and algorithms, and build a foundation that paves the way for subsequent chapters. The book then helps you navigate through the process of framing machine learning challenges and validating model performance. Through the lens of Qlik Sense, you'll explore data preprocessing and analysis techniques, as well as find out how to translate these techniques into pragmatic machine learning solutions. The concluding chapters will help you get to grips with advanced data visualization methods to facilitate a clearer presentation of findings, complemented by an array of real-world instances to bolster your skillset.By the end of this book, you'll have mastered the art of machine learning using Qlik tools and be able to take your data analytics journey to new heights.What you will learnFind out how to build practical machine learning solutions with the Qlik platformDevelop the skills needed to generate and verify machine learning models using the Qlik platformDiscover techniques used for preparing and investigating data to craft machine learning solutionsUnderstand how to transform real-world business problems into machine learning modelsExpand your potential to new use cases with data analyticsExplore advanced visualization techniques to make your models come aliveWho this book is forIf you're interested in data and analytics and are looking to extend your skillset to machine learning, this book is for you. Basic working knowledge of data, preferably with Qlik tools, will help you get started with this book. This is an excellent guide for anyone who wants to start using machine learning as part of their data analytics journey.Table of ContentsIntroduction to Machine Learning with QlikMachine Learning Algorithms and Models with QlikData Literacy in Machine Learning ContextCreating a Good Machine Learning Solution with the Qlik PlatformSetting Up the EnvironmentsPreprocessing and Exploring Data with Qlik SenseDeploying and Monitoring Machine Learning ModelsUtilizing Qlik AutoMLAdvanced Data Visualisation Techniques for Machine Learning SolutionsExamples and Case StudiesFuture Direction

These 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.

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.

These 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.

DESCRIPTIONThe Ultimate Blockchain Security Handbook will help you identify and remediate your Blockchain solution bugs before others do. Covering the latest threats and vulnerabilities, as well as effective mitigation strategies, it takes you on a journey from the security foundations of blockchain technology to implementing advanced security solutions for blockchain applications. It helps you identify, assess, and mitigate risks using a variety of tools and techniques, including threat modeling, penetration testing, vulnerability scanning, attack analysis, and security audits. It covers formal verification methods for testing smart contract code, with an application of the K semantic framework. It then explores a range of blockchain security solutions, including zero-knowledge proof architecture, access control design, the establishment of robust public key infrastructures, and the implementation of security logging and monitoring tools to track activities effectively. WHAT WILL YOU LEARN ¿ Acquire proficiency in efficiently assessing and categorizing risks through comprehensive threat modeling exercises.¿ Learn to conduct thorough audits of smart contracts and blockchain applications using diverse tools, formal verification methods, and established frameworks.¿ Learn to design secure blockchain architectures by implementing cutting-edge techniques, including zero-knowledge proofs, access controls, PKI (Public Key Infrastructure), and robust security logging.¿ Learn to take appropriate actions when encountering cryptocurrency-related internet scams, ensuring a safer digital environment.WHO IS THIS BOOK FOR?This book is tailored for blockchain developers, architects, security experts, software professionals, and researchers/cryptographers seeking in-depth knowledge of blockchain security. Whether you're building blockchain systems, strengthening security expertise, integrating blockchain into software, or delving into advanced research, this book equips you with essential skills and insights.

Learn the fundamentals of statistics and machine learning using R libraries for data processing, visualization, model training, and statistical inferenceKey Features:Advance your ML career with the help of detailed explanations, intuitive illustrations, and code examplesGain practical insights into the real-world applications of statistics and machine learningExplore the technicalities of statistics and machine learning for effective data presentationPurchase of the print or Kindle book includes a free PDF eBookBook Description:The Statistics and Machine Learning with R Workshop is a comprehensive resource packed with insights into statistics and machine learning, along with a deep dive into R libraries. The learning experience is further enhanced by practical examples and hands-on exercises that provide explanations of key concepts.Starting with the fundamentals, you'll explore the complete model development process, covering everything from data pre-processing to model development. In addition to machine learning, you'll also delve into R's statistical capabilities, learning to manipulate various data types and tackle complex mathematical challenges from algebra and calculus to probability and Bayesian statistics. You'll discover linear regression techniques and more advanced statistical methodologies to hone your skills and advance your career.By the end of this book, you'll have a robust foundational understanding of statistics and machine learning. You'll also be proficient in using R's extensive libraries for tasks such as data processing and model training and be well-equipped to leverage the full potential of R in your future projects.What You Will Learn:Hone your skills in different probability distributions and hypothesis testingExplore the fundamentals of linear algebra and calculusMaster crucial statistics and machine learning concepts in theory and practiceDiscover essential data processing and visualization techniquesEngage in interactive data analysis using RUse R to perform statistical modeling, including Bayesian and linear regressionWho this book is for:This book is for beginner to intermediate-level data scientists, undergraduate to masters-level students, and early to mid-senior data scientists or analysts looking to expand their knowledge of machine learning by exploring various R libraries. Basic knowledge of linear algebra and data modeling is a must.