Relational and Algebraic Methods in Computer Science
This 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.
SQL Query Design Patterns and Best Practices
Enhance your SQL query writing skills to provide greater business value using advanced techniques such as common table expressions, window functions, and JSONPurchase of the print or Kindle book includes a free PDF eBookKey Features: Examine query design and performance using query plans and indexesSolve business problems using advanced techniques such as common table expressions and window functionsUse SQL in modern data platform solutions with JSON and Jupyter notebooksBook Description: SQL has been the de facto standard when interacting with databases for decades and shows no signs of going away. Through the years, report developers or data wranglers have had to learn SQL on the fly to meet the business needs, so if you are someone who needs to write queries, SQL Query Design and Pattern Best Practices is for you.This book will guide you through making efficient SQL queries by reducing set sizes for effective results. You'll learn how to format your results to make them easier to consume at their destination. From there, the book will take you through solving complex business problems using more advanced techniques, such as common table expressions and window functions, and advance to uncovering issues resulting from security in the underlying dataset. Armed with this knowledge, you'll have a foundation for building queries and be ready to shift focus to using tools, such as query plans and indexes, to optimize those queries. The book will go over the modern data estate, which includes data lakes and JSON data, and wrap up with a brief on how to use Jupyter notebooks in your SQL journey.By the end of this SQL book, you'll be able to make efficient SQL queries that will improve your report writing and the overall SQL experience.What You Will Learn: Build efficient queries by reducing the data being returnedManipulate your data and format it for easier consumptionForm common table expressions and window functions to solve complex business issuesUnderstand the impact of SQL security on your resultsUnderstand and use query plans to optimize your queriesUnderstand the impact of indexes on your query performance and designWork with data lake data and JSON in SQL queriesOrganize your queries using Jupyter notebooksWho this book is for: This book is for SQL developers, data analysts, report writers, data scientists, and other data gatherers looking to expand their skills for complex querying as well as for building more efficient and performant queries.For those new to SQL, this book can help you accelerate your learning and keep you from making common mistakes.
Explainable Deep Learning AI
Explainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability challenges for deep learning AI systems. The book overviews XAI and then covers a number of specific technical works and approaches for deep learning, ranging from general XAI methods to specific XAI applications, and finally, with user-oriented evaluation approaches. It also explores the main categories of explainable AI - deep learning, which become the necessary condition in various applications of artificial intelligence. The groups of methods such as back-propagation and perturbation-based methods are explained, and the application to various kinds of data classification are presented.
Bio-Inspired Computing: Theories and Applications
This book constitutes the refereed proceedings of the 17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022, held in Wuhan, China, during December 16-18, 2022.The 56 full papers included in this book were carefully reviewed and selected from 148 submissions. They were organized in topical sections as follows: evolutionary computation and swarm intelligence; machine learning and deep learning; intelligent control and simulation and molecular computing and nanotechnology.
Analysis, Estimations, and Applications of Embedded Systems
This book constitutes the refereed proceedings of the 6th IFIP TC 10 International Embedded Systems Symposium, IESS 2019, which took place in Friedrichshafen, Germany, in September 2019. The 16 full papers and 4 short papers presented in this book were carefully reviewed and selected from 32 submissions. The papers were organized in topical sections on embedded real-time systems; estimations; architecture and applications; algorithms and System C; and analysis.
The Enterprise Data Catalog
Combing the web is simple, but how do you search for data at work? It's difficult and time-consuming, and can sometimes seem impossible. This book introduces a practical solution: the data catalog. Data analysts, data scientists, and data engineers will learn how to create true data discovery in their organizations, making the catalog a key enabler for data-driven innovation and data governance. Author Ole Olesen-Bagneux explains the benefits of implementing a data catalog. You'll learn how to organize data for your catalog, search for what you need, and manage data within the catalog. Written from a data management perspective and from a library and information science perspective, this book helps you: Learn what a data catalog is and how it can help your organization Organize data and its sources into domains and describe them with metadata Search data using very simple-to-complex search techniques and learn to browse in domains, data lineage, and graphs Manage the data in your company via a data catalog Implement a data catalog in a way that exactly matches the strategic priorities of your organization Understand what the future has in store for data catalogs
Cloud Computing Playbook
IF YOU WANT TO PASS THE MICROSOFT AZURE AZ-900 EXAM, OR WANT TO BECOME AN AWS CERTIFIED CLOUD PRACTITIONER, AND/OR WANT TO DISCOVER HOW TO AUTOMATE YOUR INFRASTRUCTURE ON ANY CLOUD WITH TERRAFORM, THIS BOOK IS FOR YOU!10 BOOKS IN 1 DEAL!- BOOK 1 - CLOUD COMPUTING FUNDAMENTALS: INTRODUCTION TO MICROSOFT AZURE AZ-900 EXAM- BOOK 2 - MICROSOFT AZURE SECURITY AND PRIVACY CONCEPTS: CLOUD DEPLOYMENT TOOLS AND TECHNIQUES, SECURITY & COMPLIANCE- BOOK 3 - MICROSOFT AZURE PRICING & SUPPORT OPTIONS: AZURE SUBSCRIPTIONS, MANAGEMENT GROUPS & COST MANAGEMENT- BOOK 4 - MICROSOFT AZURE AZ-900 EXAM PREPARATION GUIDE: HOW TO PREPARE, REGISTER AND PASS YOUR EXAM- BOOK 5 - AWS CLOUD PRACTITIONER: CLOUD COMPUTING ESSENTIALS- BOOK 6 - AWS CLOUD COMPUTING: INTRODUCTION TO CORE SERVICES- BOOK 7 - AWS CLOUD SECURITY: BEST PRACTICES FOR SMALL AND MEDIUM BUSINESSES- BOOK 8 - TERRAFORM FUNDAMENTALS: INFRASTRUCTURE DEPLOYMENT ACROSS MULTIPLE SERVICES- BOOK 9 - AUTOMATION WITH TERRAFORM: ADVANCED CONCEPTS AND FUNCTIONALITY- BOOK 10 - TERRAFORM CLOUD DEPLOYMENT: AUTOMATION, ORCHESTRATION, AND COLLABORATION GET THIS BOOK NOW AND BECOME A CLOUD PRO TODAY!
Azure Containers Explained
Apply scenario-based learning to effectively handle the technical and business impact of various services like AKS, ACI, Azure Functions, and Azure Container Apps on AzurePurchase of the print or Kindle book includes a free PDF eBookKey Features: Understand the what, why, and how of different container technologies available on Microsoft AzureExplore the practical implementation of various Azure container technologies with the help of use casesLearn common business strategies for selecting the right Azure container technology at optimized costBook Description: Whether you're working with a start-up or an enterprise, making decisions related to using different container technologies on Azure has a notable impact your app migration and modernization strategies. This is where companies face challenges, while choosing the right solutions and deciding when to move on to the next technology. Azure Containers Explained helps you make the right architectural choices for your solutions and get well-versed with the migration path to other platforms using practical examples.You'll begin with a recap of containers as technology and where you can store them within Azure. Next, you'll explore the different Microsoft Azure container technologies and understand how each platform, namely Azure Container Apps, Azure Kubernetes Service (AKS), Azure Container Instances (ACI), Azure Functions, and Azure App Services, work - you'll learn to implement them by grasping their respective characteristics and use cases. Finally, you'll build upon your own container solution on Azure using best practices from real-world examples and successfully transform your business from a start-up to a full-fledged enterprise.By the end of this book, you'll be able to effectively cater to your business and application needs by selecting and modernizing your apps using various Microsoft Azure container services.What You Will Learn: Make the best-suited architectural choices to meet your business and application needsUnderstand the migration paths between different Azure Container servicesDeploy containerized applications on Azure to multiple technologiesKnow when to use Azure Container Apps versus Azure Kubernetes ServiceFind out how to add features to an AKS clusterInvestigate the containers on Azure Web apps and Functions appsDiscover ways to improve your current architecture without starting againExplore the financial implications of using Azure container servicesWho this book is for: This book is for cloud and DevOps architects, application developers, technical leaders, decision makers, and IT professionals working with Microsoft Azure and cloud native technologies, especially containers. Reasonable knowledge of containers and a solid understanding of Microsoft Azure will help you grasp the concepts in this book.
Algorithms and Discrete Applied Mathematics
This 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.
Social Robotics
The two-volume set LNAI 13817 and 13818 constitutes the refereed proceedings of the 14th International Conference on Social Robotics, ICSR 2022, which took place in Florence, Italy, in December 2022. The 111 papers presented in the proceedings set were carefully reviewed and selected from 143 submissions. The contributions were organized in topical sections as follows: Social robot navigation and interaction capabilities (voice, tactile); Social robot perception and control capabilities; Investigating non verbal interaction with Social robots; Foster attention and engagement strategies in social robots; Special Session 1: Social Robotics Driven by Intelligent Perception and Endogenous Emotion-Motivation Core; Special Session 2: Adaptive behavioral models of robotic systems based on brain-inspired AI cognitive architectures; Advanced HRI capabilities for interacting with children; Social robots as advanced educational tool; Social robot applications in clinical and assistive scenarios; Collaborative social robots through dynamic game; Design and evaluate user's robot perception and acceptance; Ethics, gender & trust in social robotics.
Distributed Systems
Distributed Systems Comprehensive textbook resource on distributed systems--integrates foundational topics with advanced topics of contemporary importance within the field Distributed Systems: Theory and Applications is organized around three layers of abstractions: networks, middleware tools, and application framework. It presents data consistency models suited for requirements of innovative distributed shared memory applications. The book also focuses on distributed processing of big data, representation of distributed knowledge and management of distributed intelligence via distributed agents. To aid in understanding how these concepts apply to real-world situations, the work presents a case study on building a P2P Integrated E-Learning system. Downloadable lecture slides are included to help professors and instructors convey key concepts to their students. Additional topics discussed in Distributed Systems: Theory and Applications include: Network issues and high-level communication tools Software tools for implementations of distributed middleware. Data sharing across distributed components through publish and subscribe-based message diffusion, gossip protocol, P2P architecture and distributed shared memory. Consensus, distributed coordination, and advanced middleware for building large distributed applications Distributed data and knowledge management Autonomy in distributed systems, multi-agent architecture Trust in distributed systems, distributed ledger, Blockchain and related technologies. Researchers, industry professionals, and students in the fields of science, technology, and medicine will be able to use Distributed Systems: Theory and Applications as a comprehensive textbook resource for understanding distributed systems, the specifics behind the modern elements which relate to them, and their practical applications.
Internet of Things. Technology and Applications
This book constitutes the refereed post-conference proceedings of the Fourth IFIP International Cross-Domain Conference on Internet of Things, IFIPIoT 2021, held virtually in November 2021. The 15 full papers presented were carefully reviewed and selected from 33 submissions. Also included is a summary of two panel sessions held at the conference. The papers are organized in the following topical sections: challenges in IoT Applications and Research, Modernizing Agricultural Practice Using IoT, Cyber-physical IoT systems in Wildfire Context, IoT for Smart Health, Security, Methods.
Data Wrangling with R
Take your data wrangling skills to the next level by gaining a deep understanding of tidyverse libraries and effectively prepare your data for impressive analysisPurchase of the print or Kindle book includes a free PDF eBookKey Features: Explore state-of-the-art libraries for data wrangling in R and learn to prepare your data for analysisFind out how to work with different data types such as strings, numbers, date, and timeBuild your first model and visualize data with ease through advanced plot types and with ggplot2Book Description: In this information era, where large volumes of data are being generated every day, companies want to get a better grip on it to perform more efficiently than before. This is where skillful data analysts and data scientists come into play, wrangling and exploring data to generate valuable business insights. In order to do that, you'll need plenty of tools that enable you to extract the most useful knowledge from data.Data Wrangling with R will help you to gain a deep understanding of ways to wrangle and prepare datasets for exploration, analysis, and modeling. This data book enables you to get your data ready for more optimized analyses, develop your first data model, and perform effective data visualization.The book begins by teaching you how to load and explore datasets. Then, you'll get to grips with the modern concepts and tools of data wrangling. As data wrangling and visualization are intrinsically connected, you'll go over best practices to plot data and extract insights from it. The chapters are designed in a way to help you learn all about modeling, as you will go through the construction of a data science project from end to end, and become familiar with the built-in RStudio, including an application built with Shiny dashboards.By the end of this book, you'll have learned how to create your first data model and build an application with Shiny in R.What You Will Learn: Discover how to load datasets and explore data in RWork with different types of variables in datasetsCreate basic and advanced visualizationsFind out how to build your first data modelCreate graphics using ggplot2 in a step-by-step way in Microsoft Power BIGet familiarized with building an application in R with ShinyWho this book is for: If you are a professional data analyst, data scientist, or beginner who wants to learn more about data wrangling, this book is for you. Familiarity with the basic concepts of R programming or any other object-oriented programming language will help you to grasp the concepts taught in this book. Data analysts looking to improve their data manipulation and visualization skills will also benefit immensely from this book.
Deep Learning and XAI Techniques for Anomaly Detection
Create interpretable AI models for transparent and explainable anomaly detection with this hands-on guidePurchase of the print or Kindle book includes a free PDF eBookKey Features: Build auditable XAI models for replicability and regulatory complianceDerive critical insights from transparent anomaly detection modelsStrike the right balance between model accuracy and interpretabilityBook Description: Despite promising advances, the opaque nature of deep learning models makes it difficult to interpret them, which is a drawback in terms of their practical deployment and regulatory compliance.Deep Learning and XAI Techniques for Anomaly Detection shows you state-of-the-art methods that'll help you to understand and address these challenges. By leveraging the Explainable AI (XAI) and deep learning techniques described in this book, you'll discover how to successfully extract business-critical insights while ensuring fair and ethical analysis.This practical guide will provide you with tools and best practices to achieve transparency and interpretability with deep learning models, ultimately establishing trust in your anomaly detection applications. Throughout the chapters, you'll get equipped with XAI and anomaly detection knowledge that'll enable you to embark on a series of real-world projects. Whether you are building computer vision, natural language processing, or time series models, you'll learn how to quantify and assess their explainability.By the end of this deep learning book, you'll be able to build a variety of deep learning XAI models and perform validation to assess their explainability.What You Will Learn: Explore deep learning frameworks for anomaly detectionMitigate bias to ensure unbiased and ethical analysisIncrease your privacy and regulatory compliance awarenessBuild deep learning anomaly detectors in several domainsCompare intrinsic and post hoc explainability methodsExamine backpropagation and perturbation methodsConduct model-agnostic and model-specific explainability techniquesEvaluate the explainability of your deep learning modelsWho this book is for: This book is for anyone who aspires to explore explainable deep learning anomaly detection, tenured data scientists or ML practitioners looking for Explainable AI (XAI) best practices, or business leaders looking to make decisions on trade-off between performance and interpretability of anomaly detection applications. A basic understanding of deep learning and anomaly detection-related topics using Python is recommended to get the most out of this book.
The Kaggle Workbook
Move up the Kaggle leaderboards and supercharge your data science and machine learning career by analyzing famous competitions and working through exercises.Purchase of the print or Kindle book includes a free eBook in PDF format.Key Features: Challenge yourself to start thinking like a Kaggle GrandmasterFill your portfolio with impressive case studies that will come in handy during interviewsPacked with exercises and notes pages for you to enhance your skills and record key findingsBook Description: More than 80,000 Kaggle novices currently participate in Kaggle competitions. To help them navigate the often-overwhelming world of Kaggle, two Grandmasters put their heads together to write The Kaggle Book, which made plenty of waves in the community. Now, they've come back with an even more practical approach based on hands-on exercises that can help you start thinking like an experienced data scientist.In this book, you'll get up close and personal with four extensive case studies based on past Kaggle competitions. You'll learn how bright minds predicted which drivers would likely avoid filing insurance claims in Brazil and see how expert Kagglers used gradient-boosting methods to model Walmart unit sales time-series data. Get into computer vision by discovering different solutions for identifying the type of disease present on cassava leaves. And see how the Kaggle community created predictive algorithms to solve the natural language processing problem of subjective question-answering.You can use this workbook as a supplement alongside The Kaggle Book or on its own alongside resources available on the Kaggle website and other online communities. Whatever path you choose, this workbook will help make you a formidable Kaggle competitor.What You Will Learn: Take your modeling to the next level by analyzing different case studiesBoost your data science skillset with a curated selection of exercisesCombine different methods to create better solutionsGet a deeper insight into NLP and how it can help you solve unlikely challengesSharpen your knowledge of time-series forecastingChallenge yourself to become a better data scientistWho this book is for: If you're new to Kaggle and want to sink your teeth into practical exercises, start with The Kaggle Book, first. A basic understanding of the Kaggle platform, along with knowledge of machine learning and data science is a prerequisite.This book is suitable for anyone starting their Kaggle journey or veterans trying to get better at it. Data analysts/scientists who want to do better in Kaggle competitions and secure jobs with tech giants will find this book helpful.
Data Science
Do you want to know all about data science? Do you really want to understand why it is the future in all the most demanding job?The demand for data science is increasing rapidly. The basic reason behind this is the massive boom in the data generated and retained by the companies. Also known as big data, data scientists make the best use of this available information and figure out their best use.The team of data scientists also helps in generating a good amount of analytics from the information available. This information brings clarity to people on how to interact with the web and are the foundations on which most of the critical business strategies rest. This book will discuss the following topics: What is Data Science?What Exactly Does a Data Scientist Do?A Look at What Data Analytics Is All AboutRegression AnalysisHow to work with Database QueryingA Look at Artificial IntelligenceAnd much more!The book has been structured with easy-to-understand sections to help you learn everything you need to know about data science. In this book you will learn about the prerequisites of data science and the skills you need to become a data scientist. So, what are you waiting for? Grab your copy of this comprehensive guide now!
Designing Data Governance from the Ground Up
Businesses own more data than ever before, but it's of no value if you don't know how to use it. Data governance manages the people, processes, and strategy needed for deploying data projects to production. But doing it well is far from easy: Less than one fourth of business leaders say their organizations are data driven. In Designing Data Governance from the Ground Up, you'll build a cross-functional strategy to create roadmaps and stewardship for data-focused projects, embed data governance into your engineering practice, and put processes in place to monitor data after deployment. In the last decade, the amount of data people produced grew 3,000 percent. Most organizations lack the strategy to clean, collect, organize, and automate data for production-ready projects. Without effective data governance, most businesses will keep failing to gain value from the mountain of data that's available to them. There's a plethora of content intended to help DataOps and DevOps teams reach production, but 90 percent of projects trained with big data fail to reach production because they lack governance. This book shares six steps you can take to build a data governance strategy from scratch. You'll find a data framework, pull together a team of data stewards, build a data governance team, define your roadmap, weave data governance into your development process, and monitor your data in production Whether you're a chief data officer or individual contributor, this book will show you how to manage up, get the buy-in you need to build data governance, find the right colleagues to co-create data governance, and keep them engaged for the long haul.
Azure Machine Learning Engineering
Fully build and productionize end-to-end machine learning solutions using Azure Machine Learning ServiceKey Features: Automate complete machine learning solutions using Microsoft AzureUnderstand how to productionize machine learning modelsGet to grips with monitoring, MLOps, deep learning, distributed training, and reinforcement learningBook Description: Data scientists working on productionizing machine learning (ML) workloads face a breadth of challenges at every step owing to the countless factors involved in getting ML models deployed and running. This book offers solutions to common issues, detailed explanations of essential concepts, and step-by-step instructions to productionize ML workloads using the Azure Machine Learning service. You'll see how data scientists and ML engineers working with Microsoft Azure can train and deploy ML models at scale by putting their knowledge to work with this practical guide.Throughout the book, you'll learn how to train, register, and productionize ML models by making use of the power of the Azure Machine Learning service. You'll get to grips with scoring models in real time and batch, explaining models to earn business trust, mitigating model bias, and developing solutions using an MLOps framework.By the end of this Azure Machine Learning book, you'll be ready to build and deploy end-to-end ML solutions into a production system using the Azure Machine Learning service for real-time scenarios.What You Will Learn: Train ML models in the Azure Machine Learning serviceBuild end-to-end ML pipelinesHost ML models on real-time scoring endpointsMitigate bias in ML modelsGet the hang of using an MLOps framework to productionize modelsSimplify ML model explainability using the Azure Machine Learning service and Azure InterpretWho this book is for: Machine learning engineers and data scientists who want to move to ML engineering roles will find this AMLS book useful. Familiarity with the Azure ecosystem will assist with understanding the concepts covered.
DevSecOps in Practice with VMware Tanzu
Modernize your apps, run them in containers on Kubernetes, and understand the business value and the nitty-gritty of the VMware Tanzu portfolio with hands-on instructionsPurchase of the print or kindle book includes a free eBook in the PDF formatKey Features: Gain insights into the key features and capabilities of distinct VMWare Tanzu productsLearn how and when to use the different Tanzu products for common day-1 and day-2 operationsModernize applications deployed on multi-cloud platforms using DevSecOps best practicesBook Description: As Kubernetes (or K8s) becomes more prolific, managing large clusters at scale in a multi-cloud environment becomes more challenging - especially from a developer productivity and operational efficiency point of view. DevSecOps in Practice with VMware Tanzu addresses these challenges by automating the delivery of containerized workloads and controlling multi-cloud Kubernetes operations using Tanzu tools.This comprehensive guide begins with an overview of the VMWare Tanzu platform and discusses its tools for building useful and secure applications using the App Accelerator, Build Service, Catalog service, and API portal. Next, you'll delve into running those applications efficiently at scale with Tanzu Kubernetes Grid and Tanzu Application Platform. As you advance, you'll find out how to manage these applications, and control, observe, and connect them using Tanzu Mission Control, Tanzu Observability, and Tanzu Service Mesh. Finally, you'll explore the architecture, capabilities, features, installation, configuration, implementation, and benefits of these services with the help of examples.By the end of this VMware book, you'll have gained a thorough understanding of the VMWare Tanzu platform and be able to efficiently articulate and solve real-world business problems.What You Will Learn: Build apps to run as containers using predefined templatesGenerate secure container images from application source codeBuild secure open source backend services container imagesDeploy and manage a Kubernetes-based private container registryManage a multi-cloud deployable Kubernetes platformDefine a secure path to production for Kubernetes-based applicationsStreamline multi-cloud Kubernetes operations and observabilityConnect containerized apps securely using service meshWho this book is for: This book is for cloud platform engineers and DevOps engineers who want to learn about the operations of tools under the VMware Tanzu umbrella. The book also serves as a useful reference for application developers and solutions architects as well as IT leaders who want to understand how business and security outcomes can be achieved using the tools covered in this book. Prior knowledge of containers and Kubernetes will help you get the most out of this book.
Python for Finance Cookbook - Second Edition
Use modern Python libraries such as pandas, NumPy, and scikit-learn and popular machine learning and deep learning methods to solve financial modeling problemsPurchase of the print or Kindle book includes a free eBook in the PDF formatKey FeaturesExplore unique recipes for financial data processing and analysis with PythonApply classical and machine learning approaches to financial time series analysisCalculate various technical analysis indicators and backtest trading strategiesBook DescriptionPython is one of the most popular programming languages in the financial industry, with a huge collection of accompanying libraries. In this new edition of the Python for Finance Cookbook, you will explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, as well as modern machine learning and deep learning solutions.You will use popular Python libraries that, in a few lines of code, provide the means to quickly process, analyze, and draw conclusions from financial data. In this new edition, more emphasis was put on exploratory data analysis to help you visualize and better understand financial data. While doing so, you will also learn how to use Streamlit to create elegant, interactive web applications to present the results of technical analyses.Using the recipes in this book, you will become proficient in financial data analysis, be it for personal or professional projects. You will also understand which potential issues to expect with such analyses and, more importantly, how to overcome them.What you will learnPreprocess, analyze, and visualize financial dataExplore time series modeling with statistical (exponential smoothing, ARIMA) and machine learning modelsUncover advanced time series forecasting algorithms such as Meta's ProphetUse Monte Carlo simulations for derivatives valuation and risk assessmentExplore volatility modeling using univariate and multivariate GARCH modelsInvestigate various approaches to asset allocationLearn how to approach ML-projects using an example of default predictionExplore modern deep learning models such as Google's TabNet, Amazon's DeepAR and NeuralProphetWho this book is forThis book is intended for financial analysts, data analysts and scientists, and Python developers with a familiarity with financial concepts. You'll learn how to correctly use advanced approaches for analysis, avoid potential pitfalls and common mistakes, and reach correct conclusions for a broad range of finance problems.Working knowledge of the Python programming language (particularly libraries such as pandas and NumPy) is necessary.Table of ContentsAcquiring Financial DataData PreprocessingVisualizing Financial Time SeriesExploring Financial Time Series DataTechnical Analysis and Building Interactive DashboardsTime Series Analysis and ForecastingMachine Learning-Based Approaches to Time Series ForecastingMulti-Factor ModelsModelling Volatility with GARCH Class ModelsMonte Carlo Simulations in FinanceAsset AllocationBacktesting Trading StrategiesApplied Machine Learning: Identifying Credit DefaultAdvanced Concepts for Machine Learning ProjectsDeep Learning in Finance
Machine Learning Model Serving Patterns and Best Practices
Become a successful machine learning professional by effortlessly deploying machine learning models to production and implementing cloud-based machine learning models for widespread organizational useKey Features: Learn best practices about bringing your models to productionExplore the tools available for serving ML models and the differences between themUnderstand state-of-the-art monitoring approaches for model serving implementationsBook Description: Serving patterns enable data science and ML teams to bring their models to production. Most ML models are not deployed for consumers, so ML engineers need to know the critical steps for how to serve an ML model.This book will cover the whole process, from the basic concepts like stateful and stateless serving to the advantages and challenges of each. Batch, real-time, and continuous model serving techniques will also be covered in detail. Later chapters will give detailed examples of keyed prediction techniques and ensemble patterns. Valuable associated technologies like TensorFlow severing, BentoML, and RayServe will also be discussed, making sure that you have a good understanding of the most important methods and techniques in model serving. Later, you'll cover topics such as monitoring and performance optimization, as well as strategies for managing model drift and handling updates and versioning. The book will provide practical guidance and best practices for ensuring that your model serving pipeline is robust, scalable, and reliable. Additionally, this book will explore the use of cloud-based platforms and services for model serving using AWS SageMaker with the help of detailed examples.By the end of this book, you'll be able to save and serve your model using state-of-the-art techniques.What You Will Learn: Explore specific patterns in model serving that are crucial for every data science professionalUnderstand how to serve machine learning models using different techniquesDiscover the various approaches to stateless servingImplement advanced techniques for batch and streaming model servingGet to grips with the fundamental concepts in continued model evaluationServe machine learning models using a fully managed AWS Sagemaker cloud solutionWho this book is for: This book is for machine learning engineers and data scientists who want to bring their models into production. Those who are familiar with machine learning and have experience of using machine learning techniques but are looking for options and strategies to bring their models to production will find great value in this book. Working knowledge of Python programming is a must to get started.
Telecommunications and Remote Sensing
This book contains the proceedings of ICTRS 2022 (the 11th International Conference on Telecommunications and Remote Sensing), held in Sofia, Bulgaria, on 21-22 November 2022. ICTRS is an annual event that brings together researchers and practitioners interested in telecommunications, remote sensing, and their societal implications. As mentioned already, ICTRS is essentially leaning toward telecommunications and remote sensing plus relevant societal implications. In this, ICTRS 2022 addresses a large number of research areas and topics, such as: Wireless Telecommunications and Networking; Electromagnetic Waves and Fields; Electronics and Photonics; Remote Sensing and Data Interpretation; Remote Sensing and Internet-Of-Things; and Societal Impact.
Supercomputing
This book constitutes the refereed proceedings of the 8th Russian Supercomputing Days on Supercomputing, RuSCDays 2022, which took place in Moscow, Russia, in September 2022. The 49 full papers and 1 short paper presented in this volume were carefully reviewed and selected from 94 submissions. The papers are organized in the following topical sections: Supercomputer Simulation; HPC, BigData, AI: Architectures, Technologies, Tools; Distributed and Cloud Computing.
Data Management at Scale
As data management continues to evolve rapidly, managing all of your data in a central place, such as a data warehouse, is no longer scalable. Today's world is about quickly turning data into value. This requires a paradigm shift in the way we federate responsibilities, manage data, and make it available to others. With this practical book, you'll learn how to design a next-gen data architecture that takes into account the scale you need for your organization. Executives, architects and engineers, analytics teams, and compliance and governance staff will learn how to build a next-gen data landscape. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including regulatory requirements, privacy concerns, and new developments such as data mesh and data fabric Go deep into building a modern data architecture, including cloud data landing zones, domain-driven design, data product design, and more Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata
Practical Data Privacy
Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for integrating breakthrough privacy-enhancing technologies into production systems. Practical Data Privacy answers important questions such as: What do privacy regulations like GDPR and CCPA mean for my data workflows and data science use cases? What does "anonymized data" really mean? How do I actually anonymize data? How does federated learning and analysis work? Homomorphic encryption sounds great, but is it ready for use? How do I compare and choose the best privacy-preserving technologies and methods? Are there open-source libraries that can help? How do I ensure that my data science projects are secure by default and private by design? How do I work with governance and infosec teams to implement internal policies appropriately?
CompTIA Data＋
Learn data analysis essentials and prepare for the Data+ exam with this CompTIA exam guide, complete with practice exams towards the end.Key Features: Apply simple methods of data analysis and find out when and how to apply more complicated onesTake business requirements and produce a remote to the correct audience using appropriate visualizationsLearn about data governance rules, including quality and controlBook Description: The CompTIA Data+ certification exam not only helps validate a skill set required to enter one of the fastest growing fields in the world, but is also starting to standardize language and concepts within the field. However, there's a lot of conflicting information and lack of existing resources about the topics covered in this exam, and even professionals working in data analytics may need a study guide to help them pass on their first attempt.The CompTIA Data + (DAO-001) Certification Guide will give you a solid foundation on how to prepare, analyze and report the data for better insights.You'll get an introduction to Data+ certification exam format to begin with, and then quickly dive into preparing data. You'll learn about collecting, cleaning, and processing data along with data wrangling and manipulation. As you progress, you'll cover data analysis topics like types of analysis, common techniques, hypothesis techniques, and statistical analysis before tackling data reporting, common visualizations, and data governance. All knowledge you've gained throughout the book will be tested of mock tests that appear in the final chapters.By the end of this book, you'll be ready to pass the Data+ exam with confidence and take the next step in your career.What You Will Learn: Get well versed with the five domains covered in the DAO-001 examGain an understanding of all the major concepts covered in the exam and when to apply themUnderstand the fundamental concepts behind ETL and ELTExplore various imputation and deletion methods to deal with missing dataIdentify and deal with outliersLearn and perform hypothesis testingCreate insightful reports to showcase your findingsWho this book is for: If you are a data analyst looking to get certified with DAO-001 exam this is the book for you. This CompTIA book is also ideal for who needs help in entering the quickly growing field of Data Analytics and are seeking professional certifications.
The Art of Data-Driven Business
Learn how to make the right decisions for your business with the help of Python recipes and the expertise of data leadersKey Features: Learn and practice various clustering techniques to gather market insightsExplore real-life use cases from the business world to contextualize your learningWork your way through practical recipes that will reinforce what you have learnedBook Description: One of the most valuable contributions of data science is toward helping businesses make the right decisions. Understanding this complicated confluence of two disparate worlds, as well as a fiercely competitive market, calls for all the guidance you can get.The Art of Data-Driven Business is your invaluable guide to gaining a business-driven perspective, as well as leveraging the power of machine learning (ML) to guide decision-making in your business. This book provides a common ground of discussion for several profiles within a company.You'll begin by looking at how to use Python and its many libraries for machine learning. Experienced data scientists may want to skip this short introduction, but you'll soon get to the meat of the book and explore the many and varied ways ML with Python can be applied to the domain of business decisions through real-world business problems that you can tackle by yourself. As you advance, you'll gain practical insights into the value that ML can provide to your business, as well as the technical ability to apply a wide variety of tried-and-tested ML methods.By the end of this Python book, you'll have learned the value of basing your business decisions on data-driven methodologies and have developed the Python skills needed to apply what you've learned in the real world.What You Will Learn: Create effective dashboards with the seaborn libraryPredict whether a customer will cancel their subscription to a serviceAnalyze key pricing metrics with pandasRecommend the right products to your customersDetermine the costs and benefits of promotionsSegment your customers using clustering algorithmsWho this book is for: This book is for data scientists, machine learning engineers and developers, data engineers, and business decision makers who want to apply data science for business process optimization and develop the skills needed to implement data science projects in marketing, sales, pricing, customer success, ad tech, and more from a business perspective. Other professionals looking to explore how data science can be used to improve business operations, as well as individuals with technical skills who want to back their technical proposal with a strong business case will also find this book useful.
Data Modeling with Tableau
Save time analyzing volumes of data using best practices to extract, model, and create insights from your dataKey Features: Master best practices in data modeling with Tableau Prep Builder and Tableau DesktopApply Tableau Server and Cloud to create and extend data modelsBuild organizational data models based on data and content governance best practicesBook Description: Tableau is unlike most other BI platforms that have a single data modeling tool and enterprise data model (for example, LookML from Google's Looker). That doesn't mean Tableau doesn't have enterprise data governance; it is both robust and very flexible. This book will help you build a data-driven organization with the proper use of Tableau governance models.Data Modeling with Tableau is an extensive guide, complete with step-by-step explanations of essential concepts, practical examples, and hands-on exercises. As you progress through the chapters, you will learn the role that Tableau Prep Builder and Tableau Desktop each play in data modeling. You'll also explore the components of Tableau Server and Cloud that make data modeling more robust, secure, and performant. Moreover, by extending data models for Ask and Explain Data, you'll gain the knowledge required to extend analytics to more people in their organizations, leading to better data-driven decisions. Finally, this book will get into the entire Tableau stack and get the techniques required to build the right level of governance into Tableau data models for the right use cases.By the end of this Tableau book, you'll have a firm understanding of how to leverage data modeling in Tableau to benefit your organization.What You Will Learn: Showcase Tableau published data sources and embedded connectionsApply Ask Data in data cataloging and natural language queryExhibit features of Tableau Prep Builder with hands-on exercisesModel data with Tableau Desktop through examplesFormulate a governed data strategy using Tableau Server and CloudOptimize data models for Ask and Explain DataWho this book is for: This book is for data analysts and business analysts who are looking to expand their data skills, offering a broad foundation to build better data models in Tableau for easier analysis and better query performance.It will also benefit individuals responsible for making trusted and secure data available to their organization through Tableau, such as data stewards and others who work to take enterprise data and make it more accessible to business analysts.
Fundamentals of Logic and Computation
This textbook aims to help the reader develop an in-depth understanding of logical reasoning and gain knowledge of the theory of computation. The book combines theoretical teaching and practical exercises; the latter is realised in Isabelle/HOL, a modern theorem prover, and PAT, an industry-scale model checker. I also give entry-level tutorials on the two software to help the reader get started. By the end of the book, the reader should be proficient in both software. Content-wise, this book focuses on the syntax, semantics and proof theory of various logics; automata theory, formal languages, computability and complexity. The final chapter closes the gap with a discussion on the insight that links logic with computation. This book is written for a high-level undergraduate course or a Master's course. The hybrid skill set of practical theorem proving and model checking should be helpful for the future of readers should they pursue a research career or engineering informal methods.
Biomimetic and Biohybrid Systems
This book constitutes the proceedings of the 11th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2022, held as virtual event, in July 19-22, 2022. The conference was held virtually due to the COVID-19 crisis.The 30 full papers and 8 short papers presented were carefully reviewed and selected from 48 submissions. They deal with research on novel life-like technologies inspired by the scientific investigation of biological systems; biomimetics; and research that seeks to interface biological and artificial systems to create biohybrid systems.
Artificial Life and Evolutionary Computation
This book constitutes the proceedings of the 15th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2021, held in Winterthur, Switzerland, in September 2022. The 14 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 25 submissions. The papers are organized in the following topical sections: Networks; Droplets, Fluids, and Synthetic Biology; Robot Systems; Computer Vision and Computational Creativity; Semantic Search; Artificial Medicine and Pharmacy; Trade and Finance; Ethics in Computational Modelling.Chapters 4, 5, 6, 7, 22, and 24 are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Machine Learning Techniques for Text
Take your Python text processing skills to another level by learning about the latest natural language processing and machine learning techniques with this full color guideKey Features: Learn how to acquire and process textual data and visualize the key findingsObtain deeper insight into the most commonly used algorithms and techniques and understand their tradeoffsImplement models for solving real-world problems and evaluate their performanceBook Description: With the ever-increasing demand for machine learning and programming professionals, it's prime time to invest in the field. This book will help you in this endeavor, focusing specifically on text data and human language by steering a middle path among the various textbooks that present complicated theoretical concepts or focus disproportionately on Python code.A good metaphor this work builds upon is the relationship between an experienced craftsperson and their trainee. Based on the current problem, the former picks a tool from the toolbox, explains its utility, and puts it into action. This approach will help you to identify at least one practical use for each method or technique presented. The content unfolds in ten chapters, each discussing one specific case study. For this reason, the book is solution-oriented. It's accompanied by Python code in the form of Jupyter notebooks to help you obtain hands-on experience. A recurring pattern in the chapters of this book is helping you get some intuition on the data and then implement and contrast various solutions.By the end of this book, you'll be able to understand and apply various techniques with Python for text preprocessing, text representation, dimensionality reduction, machine learning, language modeling, visualization, and evaluation.What You Will Learn: Understand fundamental concepts of machine learning for textDiscover how text data can be represented and build language modelsPerform exploratory data analysis on text corporaUse text preprocessing techniques and understand their trade-offsApply dimensionality reduction for visualization and classificationIncorporate and fine-tune algorithms and models for machine learningEvaluate the performance of the implemented systemsKnow the tools for retrieving text data and visualizing the machine learning workflowWho this book is for: This book is for professionals in the area of computer science, programming, data science, informatics, business analytics, statistics, language technology, and more who aim for a gentle career shift in machine learning for text. Students in relevant disciplines that seek a textbook in the field will benefit from the practical aspects of the content and how the theory is presented. Finally, professors teaching a similar course will be able to pick pertinent topics in terms of content and difficulty. Beginner-level knowledge of Python programming is needed to get started with this book.
Deep Learning for Genomics
Learn concepts, methodologies, and applications of deep learning for building predictive models from complex genomics data sets to overcome challenges in the life sciences and biotechnology industriesKey Features: Apply deep learning algorithms to solve real-world problems in the field of genomicsExtract biological insights from deep learning models built from genomic datasetsTrain, tune, evaluate, deploy, and monitor deep learning models for enabling predictions in genomicsBook Description: Deep learning has shown remarkable promise in the field of genomics; however, there is a lack of a skilled deep learning workforce in this discipline. This book will help researchers and data scientists to stand out from the rest of the crowd and solve real-world problems in genomics by developing the necessary skill set. Starting with an introduction to the essential concepts, this book highlights the power of deep learning in handling big data in genomics. First, you'll learn about conventional genomics analysis, then transition to state-of-the-art machine learning-based genomics applications, and finally dive into deep learning approaches for genomics. The book covers all of the important deep learning algorithms commonly used by the research community and goes into the details of what they are, how they work, and their practical applications in genomics. The book dedicates an entire section to operationalizing deep learning models, which will provide the necessary hands-on tutorials for researchers and any deep learning practitioners to build, tune, interpret, deploy, evaluate, and monitor deep learning models from genomics big data sets.By the end of this book, you'll have learned about the challenges, best practices, and pitfalls of deep learning for genomics.What You Will Learn: Discover the machine learning applications for genomicsExplore deep learning concepts and methodologies for genomics applicationsUnderstand supervised deep learning algorithms for genomics applicationsGet to grips with unsupervised deep learning with autoencodersImprove deep learning models using generative modelsOperationalize deep learning models from genomics datasetsVisualize and interpret deep learning modelsUnderstand deep learning challenges, pitfalls, and best practicesWho this book is for: This deep learning book is for machine learning engineers, data scientists, and academicians practicing in the field of genomics. It assumes that readers have intermediate Python programming knowledge, basic knowledge of Python libraries such as NumPy and Pandas to manipulate and parse data, Matplotlib, and Seaborn for visualizing data, along with a base in genomics and genomic analysis concepts.
Augmented User Manual for CSP-Rules-V2.1
This book is the User Manual for CSP-Rules-V2.1, a generic pattern-based (or rule-based) solver of finite binary Constraint Satisfaction Problems (CSPs). The associated software, CSP-Rules-V2.1, is available on GitHub. It includes fully developed applications to logic puzzles such as Latin Squares, Sudoku, Futoshiki, Kakuro, Map Colouring, Numbrix, Hidato and Slithering. This third edition includes additions related to reducing the number of steps in a resolution path and to a newly discovered pattern (tridagon) for extremely hard puzzles.
Making Wise Decisions in a Smart World: Responsible Leadership in an Era of Artificial Intelligence
Good and smart decisions should be distinguished from wise decision-making -- especially in the age of artificial intelligence (AI) where algorithms are increasingly used to automate business processes or to augment the accuracy and speed of decisions. This book argues why specific forms of intelligence as well as consciousness and enhanced conscience are crucial to make wise decisions -- with consciousness to be clearly distinguished from intelligence. It also addresses why machine learning and smart computers (AI) are plausibly able to make 'smart' (and thus to a certain extent 'intelligent') decisions but definitely unable to help us to become wiser. In essence, optimizing a desired output in a business context will require a balanced approach with cognitive awareness and ethical reflection -- synthesizing intuitive and algorithmic thinking -- encompassing short-term profit and longer-term envisioning, and aiming to optimize created and captured value for shareholders while taking the concerns of those who have a real stake in the organization seriously. If business is about creating and sharing value in a future that is both 'digital' and 'relational', then innovative technologies like AI will play an increasingly important role. Consequently, mindful executives and their responsible boards therefore need to acknowledge the limitations of AI in business -- especially when the uncertain future is estimated to be rather volatile or ambiguous than stable.
Making Wise Decisions in a Smart World: Responsible Leadership in an Era of Artificial Intelligence (Student Edition)
Good and smart decisions should be distinguished from wise decision-making -- especially in the age of artificial intelligence (AI) where algorithms are increasingly used to automate business processes or to augment the accuracy and speed of decisions. This book argues why specific forms of intelligence as well as consciousness and enhanced conscience are crucial to make wise decisions -- with consciousness to be clearly distinguished from intelligence. It also addresses why machine learning and smart computers (AI) are plausibly able to make 'smart' (and thus to a certain extent 'intelligent') decisions but definitely unable to help us to become wiser. In essence, optimizing a desired output in a business context will require a balanced approach with cognitive awareness and ethical reflection -- synthesizing intuitive and algorithmic thinking -- encompassing short-term profit and longer-term envisioning, and aiming to optimize created and captured value for shareholders while taking the concerns of those who have a real stake in the organization seriously. If business is about creating and sharing value in a future that is both 'digital' and 'relational', then innovative technologies like AI will play an increasingly important role. Consequently, mindful executives and their responsible boards therefore need to acknowledge the limitations of AI in business -- especially when the uncertain future is estimated to be rather volatile or ambiguous than stable.
Machine Learning Engineering on AWS
Work seamlessly with production-ready machine learning systems and pipelines on AWS by addressing key pain points encountered in the ML life cycleKey Features: Gain practical knowledge of managing ML workloads on AWS using Amazon SageMaker, Amazon EKS, and moreUse container and serverless services to solve a variety of ML engineering requirementsDesign, build, and secure automated MLOps pipelines and workflows on AWSBook Description: There is a growing need for professionals with experience in working on machine learning (ML) engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production.This machine learning book covers the essential concepts as well as step-by-step instructions that are designed to help you get a solid understanding of how to manage and secure ML workloads in the cloud. As you progress through the chapters, you'll discover how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. You'll also delve into proven cost optimization techniques as well as data privacy and model privacy preservation strategies in detail as you explore best practices when using each AWS.By the end of this AWS book, you'll be able to build, scale, and secure your own ML systems and pipelines, which will give you the experience and confidence needed to architect custom solutions using a variety of AWS services for ML engineering requirements.What You Will Learn: Find out how to train and deploy TensorFlow and PyTorch models on AWSUse containers and serverless services for ML engineering requirementsDiscover how to set up a serverless data warehouse and data lake on AWSBuild automated end-to-end MLOps pipelines using a variety of servicesUse AWS Glue DataBrew and SageMaker Data Wrangler for data engineeringExplore different solutions for deploying deep learning models on AWSApply cost optimization techniques to ML environments and systemsPreserve data privacy and model privacy using a variety of techniquesWho this book is for: This book is for machine learning engineers, data scientists, and AWS cloud engineers interested in working on production data engineering, machine learning engineering, and MLOps requirements using a variety of AWS services such as Amazon EC2, Amazon Elastic Kubernetes Service (EKS), Amazon SageMaker, AWS Glue, Amazon Redshift, AWS Lake Formation, and AWS Lambda -- all you need is an AWS account to get started. Prior knowledge of AWS, machine learning, and the Python programming language will help you to grasp the concepts covered in this book more effectively.
Python Feature Engineering Cookbook - Second Edition
Create end-to-end, reproducible feature engineering pipelines that can be deployed into production using open-source Python librariesKey Features: Learn and implement feature engineering best practicesReinforce your learning with the help of multiple hands-on recipesBuild end-to-end feature engineering pipelines that are performant and reproducibleBook Description: Feature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook will take the struggle out of feature engineering by showing you how to use open source Python libraries to accelerate the process via a plethora of practical, hands-on recipes.This updated edition begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you'll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner.By the end of this Python book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature engineering pipelines that can be deployed into production.What You Will Learn: Impute missing data using various univariate and multivariate methodsEncode categorical variables with one-hot, ordinal, and count encodingHandle highly cardinal categorical variablesTransform, discretize, and scale your variablesCreate variables from date and time with pandas and Feature-engineCombine variables into new featuresExtract features from text as well as from transactional data with FeaturetoolsCreate features from time series data with tsfreshWho this book is for: This book is for machine learning and data science students and professionals, as well as software engineers working on machine learning model deployment, who want to learn more about how to transform their data and create new features to train machine learning models in a better way.
Explainable Human-AI Interaction
From its inception, artificial intelligence (AI) has had a rather ambivalent relationship with humans--swinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives at an ever-increasing pace, there is a greater need for AI systems to work synergistically with humans. One critical requirement for such synergistic human‒AI interaction is that the AI systems' behavior be explainable to the humans in the loop. To do this effectively, AI agents need to go beyond planning with their own models of the world, and take into account the mental model of the human in the loop. At a minimum, AI agents need approximations of the human's task and goal models, as well as the human's model of the AI agent's task and goal models. The former will guide the agent to anticipate and manage the needs, desires and attention of the humans in the loop, and the latter allow it to act in ways that are interpretable to humans (by conforming to their mental models of it), andbe ready to provide customized explanations when needed. The authors draw from several years of research in their lab to discuss how an AI agent can use these mental models to either conform to human expectations or change those expectations through explanatory communication. While the focus of the book is on cooperative scenarios, it also covers how the same mental models can be used for obfuscation and deception. The book also describes several real-world application systems for collaborative decision-making that are based on the framework and techniques developed here. Although primarily driven by the authors' own research in these areas, every chapter will provide ample connections to relevant research from the wider literature. The technical topics covered in the book are self-contained and are accessible to readers with a basic background in AI.
Data Storytelling with Google Data Studio
Apply data storytelling concepts and analytical thinking to create dashboards and reports in Google Data Studio to aid data-driven decision makingKey Features: Gain a solid understanding of data visualization principles and learn to apply them effectivelyGet to grips with the concepts and features of Data Studio to create powerful data storiesExplore the end-to-end process of building dashboards with the help of practical examplesBook Description: Presenting data visually makes it easier for organizations and individuals to interpret and analyze information. Google Data Studio is an easy-to-use, collaborative tool that enables you to transform your data into engaging visualizations. This allows you to build and share dashboards that help monitor key performance indicators, identify patterns, and generate insights to ultimately drive decisions and actions.Data Storytelling with Google Data Studio begins by laying out the foundational design principles and guidelines that are essential to creating accurate, effective, and compelling data visualizations. Next, you'll delve into features and capabilities of Data Studio - from basic to advanced - and explore their application with examples. The subsequent chapters walk you through building dashboards with a structured three-stage process called the 3D approach using real-world examples that'll help you understand the various design and implementation considerations. This approach involves determining the objectives and needs of the dashboard, designing its key components and layout, and developing each element of the dashboard.By the end of this book, you will have a solid understanding of the storytelling approach and be able to create data stories of your own using Data Studio.What You Will Learn: Understand what storytelling with data means, and explore its various formsDiscover the 3D approach to building dashboards - determine, design, and developTest common data visualization pitfalls and learn how to mitigate themGet up and running with Data Studio and leverage it to explore and visualize dataExplore the advanced features of Data Studio with examplesBecome well-versed in the step-by-step process of the 3D approach using practical examplesMeasure and monitor the usage patterns of your Data Studio reportsWho this book is for: If you are a beginner or an aspiring data analyst looking to understand the core concepts of data visualization and want to use Google Data Studio for creating effective dashboards, this book is for you. No specific prior knowledge is needed to understand the concepts present in this book. Experienced data analysts and business intelligence developers will also find this book useful as a detailed guide to using Data Studio as well as a refresher of core dashboarding concepts.
Scalable Data Architecture with Java
Orchestrate data architecting solutions using Java and related technologies to evaluate, recommend and present the most suitable solution to leadership and clientsKey Features: Learn how to adapt to the ever-evolving data architecture technology landscapeUnderstand how to choose the best suited technology, platform, and architecture to realize effective business valueImplement effective data security and governance principlesBook Description: Java architectural patterns and tools help architects to build reliable, scalable, and secure data engineering solutions that collect, manipulate, and publish data.This book will help you make the most of the architecting data solutions available with clear and actionable advice from an expert.You'll start with an overview of data architecture, exploring responsibilities of a Java data architect, and learning about various data formats, data storage, databases, and data application platforms as well as how to choose them. Next, you'll understand how to architect a batch and real-time data processing pipeline. You'll also get to grips with the various Java data processing patterns, before progressing to data security and governance. The later chapters will show you how to publish Data as a Service and how you can architect it. Finally, you'll focus on how to evaluate and recommend an architecture by developing performance benchmarks, estimations, and various decision metrics.By the end of this book, you'll be able to successfully orchestrate data architecture solutions using Java and related technologies as well as to evaluate and present the most suitable solution to your clients.What You Will Learn: Analyze and use the best data architecture patterns for problemsUnderstand when and how to choose Java tools for a data architectureBuild batch and real-time data engineering solutions using JavaDiscover how to apply security and governance to a solutionMeasure performance, publish benchmarks, and optimize solutionsEvaluate, choose, and present the best architectural alternativesUnderstand how to publish Data as a Service using GraphQL and a REST APIWho this book is for: Data architects, aspiring data architects, Java developers and anyone who wants to develop or optimize scalable data architecture solutions using Java will find this book useful. A basic understanding of data architecture and Java programming is required to get the best from this book.
SX001G, Glossary for the S-Series IPS specifications, Issue 3.0
The Glossary for the S-Series IPS specifications is a compilation of the description of all classes and attributes shared by the full set of the S-Series of Integrated Product Support (IPS) specifications. It also includes common terms used by the S-Series.
Unity 3D Game Development
Create ready-to-play 3D games with reactive environments, sound, dynamic effects, and more!Key Features: Build a solid foundation for game design and game developmentUnderstand the fundamentals of 3D such as coordinates, spaces, vectors, and camerasGet to grips with essential Unity concepts including characters, scenes, terrains, objects and moreBook Description: This book, written by a team of experts at Unity Technologies, follows an informal, demystifying approach to the world of game development.Within Unity 3D Game Development, you will learn to: Design and build 3D characters, and the game environmentThink about the users' interactions with your gameDevelop the interface and apply visual effects to add an emotional connection to your worldGrasp a solid foundation of sound design, animations, and lightning to your creationsBuild, test, and add final touchesThe book is split between expert insights that you'll read before you look into the project on GitHub to understand all the underpinnings. This way, you get to see the end result, and you're allowed to be creative and give your own thoughts to design, as well as work through the process with the new tools we introduce.Join the book community on Discord: Read this book with Unity game developers, and the team of authors. Ask questions, build teams, chat with the authors, participate in events and much more. The link to join is included in the book.What You Will Learn: Learn fundamentals of designing a 3D game and C# scriptingDesign your game character and work through their mechanics and movementsCreate an environment with Unity Terrain and ProBuilderExplore instantiation and rigid bodies through physics theory and codeImplement sound, lighting effects, trail rendering, and other dynamic effectsCreate a short, fully functional segment of your game in a vertical slicePolish your game with performance tweaksJOIN the 'book-club' to read alongside other users, Unity experts, and ask the authors when stuckWho this book is for: Our goal with this book is to enable every reader to build the right mindset to think about 3D games, and then show them all the steps we took to create ours.The main target audience for this book is those with some prior knowledge in game development, though regardless of your experience, we hope to create an enjoyable learning journey for you
Self Aware Security for Real Time Task Schedules in Reconfigurable Hardware Platforms
Chapter 1: Introduction(a) Reconfigurable hardware based embedded systems (b) Importance of Real Time Scheduling for such embedded architectures (c) Importance of Self Aware Security for such architectures Chapter 2: Background (a) Scheduling for embedded real time tasks and limitations of existing techniques (b) Security related to hardware attacks and limitations of existing techniques Chapter 3: A novel real-time scheduling for FPGAs having slotted area model This chapter presents deadline-partition oriented scheduling methodologiesfor periodic hard real-time dynamic task sets on fully and partiallyreconfigurable FPGAs in which the floor of the FPGA is assumed to be statically equi-partitioned into a set of homogeneous tiles such that anyarbitrary task of the given task set may be feasibly mapped into the areaof a given tile. Chapter 4: A novel real-time scheduling for FPGAs having flexible area model This chapter presents scheduling methodologies for periodic dependent hard real-time dynamic task sets on fully and partially reconfigurable FPGAs in which the floor of the FPGA follows flexible area model such that any task can be placed anywhere within the floor area. This will work will attempt to solve both the temporal and spatial aspects of the scheduling. Chapter 5: Denial of Service Attacks for Real Time Scheduling and Related Mitigation Techniques This chapter presents threat analysis associated with denial of service attacks due to delay inducing hardware trojans in embedded architectures for the scheduling strategies presenteed in Chapter 3 and 4. A self aware security module is also presented that detects and mitigates the threat. Chapter 6: Erroneous Result Generation Attack for Real Time Scheduling and Related Mitigation Technique This chapter presents threat analysis associated with generation of erroneous results that may jeopardize the real time task schedules presented in Chapter 3 and 4. Related detection and mitigation techniques are presented alongwith. In addition to this, it is also described how related modifications of the self aware security module can ensure security for the present scenario.Chapter 7: Conclusion In this book, we present the importance of real time scheduling for reconfigurable hardware based embedded platforms and related security needs. We present limitations of existing techniques and present some new real time scheduling techniques suitable for the embedded platform. We also focus on how denial of service and erroneous result generation may take place on the real time schedules due to vulnerability of hardware. Related detection and mitigation techniques are discussed, along with description of a self aware module that facilitates detection and mitigation from such threats.
Towards Autonomous Robotic Systems
The volume LNAI 13546 constitutes the refereed proceedings of the 23rd Annual Conference Towards Autonomous Robotic Systems, TAROS 2022, held in Culham, UK, in September 2022. The 14 full papers and 10 short papers were carefully reviewed and selected from 38 submissions. Organized in the topical sections "Algorithms" and "Systems", they discuss significant findings and advances in the following areas: Robotic Grippers and Manipulation; Soft Robotics, Sensing and Mobile Robots; Robotic Learning, Mapping and Planning; Robotic Systems and Applications.
The Nature of Data
When we look at some of the most pressing issues in environmental politics today, it is hard to avoid data technologies. Big data, artificial intelligence, and data dashboards all promise "revolutionary" advances in the speed and scale at which governments, corporations, conservationists, and even individuals can respond to environmental challenges. By bringing together scholars from geography, anthropology, science and technology studies, and ecology, The Nature of Data explores how the digital realm is a significant site in which environmental politics are waged. This collection as a whole makes the argument that we cannot fully understand the current conjuncture in critical, global environmental politics without understanding the role of data platforms, devices, standards, and institutions. In particular, The Nature of Data addresses the contested practices of making and maintaining data infrastructure, the imaginaries produced by data infrastructures, the relations between state and civil society that data infrastructure reworks, and the conditions under which technology can further socio-ecological justice instead of re-entrenching state and capitalist power. This innovative volume presents some of the first research in this new but rapidly growing subfield that addresses the role of data infrastructures in critical environmental politics.
The Nature of Data
When we look at some of the most pressing issues in environmental politics today, it is hard to avoid data technologies. Big data, artificial intelligence, and data dashboards all promise "revolutionary" advances in the speed and scale at which governments, corporations, conservationists, and even individuals can respond to environmental challenges. By bringing together scholars from geography, anthropology, science and technology studies, and ecology, The Nature of Data explores how the digital realm is a significant site in which environmental politics are waged. This collection as a whole makes the argument that we cannot fully understand the current conjuncture in critical, global environmental politics without understanding the role of data platforms, devices, standards, and institutions. In particular, The Nature of Data addresses the contested practices of making and maintaining data infrastructure, the imaginaries produced by data infrastructures, the relations between state and civil society that data infrastructure reworks, and the conditions under which technology can further socio-ecological justice instead of re-entrenching state and capitalist power. This innovative volume presents some of the first research in this new but rapidly growing subfield that addresses the role of data infrastructures in critical environmental politics. Jenny Goldstein is an assistant professor of global development at Cornell University. Eric Nost is an assistant professor of geography, environment, and geomatics at the University of Guelph.
Artificial Intelligence and Industry 4.0
Artificial Intelligence and Industry 4.0 explores recent advancements in blockchain technology and artificial intelligence (AI) as well as their crucial impacts on realizing Industry 4.0 goals. The book explores AI applications in industry including Internet of Things (IoT) and Industrial Internet of Things (IIoT) technology. Chapters explore how AI (machine learning, smart cities, healthcare, Society 5.0, etc.) have numerous potential applications in the Industry 4.0 era. This book is a useful resource for researchers and graduate students in computer science researching and developing AI and the IIoT.
