Rechnerarchitektur Grundlagen
Studierende der Fachrichtung Informatik erhalten mit "Rechnerarchitektur Grundlagen" einen ?berblick 羹ber den Aufbau und die Architektur von Rechnern. Sie lernen das breite Spektrum vom digitalen Teil eines Rechners, 羹ber Datenrepr瓣sentation, Schnittstellen, Assembler hin zu Speichermedien und Speicherorganisation kennen.
Granular Video Computing
This volume links the concept of granular computing using deep learning and the Internet of Things to object tracking for video analysis. It describes how uncertainties, involved in the task of video processing, could be handled in rough set theoretic granular computing frameworks. Issues such as object tracking from videos in constrained situations, occlusion/overlapping handling, measuring of the reliability of tracking methods, object recognition and linguistic interpretation in video scenes, and event prediction from videos, are the addressed in this volume. The book also looks at ways to reduce data dependency in the context of unsupervised (without manual interaction/ labeled data/ prior information) training.This book may be used both as a textbook and reference book for graduate students and researchers in computer science, electrical engineering, system science, data science, and information technology, and is recommended for both students and practitioners working in computer vision, machine learning, video analytics, image analytics, artificial intelligence, system design, rough set theory, granular computing, and soft computing.
Modeling and Control of Drug Delivery Systems
Modeling and Control of Drug Delivery Systems provides comprehensive coverage of various drug delivery and targeting systems and their state-of-the-art related works, ranging from theory to real-world deployment and future perspectives. Various drug delivery and targeting systems have been developed to minimize drug degradation and adverse effect and increase drug bioavailability. Site-specific drug delivery may be either an active and/or passive process. Improving delivery techniques that minimize toxicity and increase efficacy offer significant potential benefits to patients and open up new markets for pharmaceutical companies. This book will attract many researchers working in DDS field as it provides an essential source of information for pharmaceutical scientists and pharmacologists working in academia as well as in the industry. In addition, it has useful information for pharmaceutical physicians and scientists in many disciplines involved in developing DDS, such as chemical engineering, biomedical engineering, protein engineering, gene therapy.
Machine Learning, Big Data, and Iot for Medical Informatics
Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics. In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data. This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT.
Data Modeling for Quality
This book is for all data modelers, data architects, and database designers―be they novices who want to learn what's involved in data modeling, or experienced modelers who want to brush up their skills. A novice will not only gain an overview of data modeling, they will also learn how to follow the data modeling process, including the activities required for each step. The experienced practitioner will discover (or rediscover) techniques to ensure that data models accurately reflect business requirements. This book describes rigorous yet easily implemented approaches to: modeling of business information requirements for review by business stakeholders before development of the logical data modelnormalizing data, based on simple questions rather than the formal definitions which many modelers find intimidatingnaming and defining concepts and attributesmodeling of time-variant datadocumenting business rules governing both the real world and datadata modeling in an Agile projectmanaging data model change in any type of project transforming a business information model to a logical data model against which developers can codeimplementing the logical data model in a traditional relational DBMS, an SQL:2003-compliant DBMS, an object-relational DBMS, or in XML.Part 1 describes business information models in-depth, including: the importance of modeling business information requirements before embarking on a logical data modelbusiness concepts (entity classes)attributes of business conceptsattribute classes as an alternative to DBMS data typesrelationships between business conceptstime-variant datageneralization and specialization of business conceptsnaming and defining the components of the business information modelbusiness rules governing data, including a distinction between real-world rules and data rules.Part 2 journeys from requirements to a working data resource, covering: sourcing data requirementsdeveloping the business information modelcommunicating it to business stakeholders for review, both as diagrams and verballymanaging data model changetransforming the business information model into a logical data model of stored data for implementation in a relational or object-relational DBMSattribute value representation and data constraints (important but often overlooked)modeling data vault, dimensional and XML data.
Python Data Analysis - Third Edition
Understand data analysis pipelines using machine learning algorithms and techniques with this practical guideKey Features: Prepare and clean your data to use it for exploratory analysis, data manipulation, and data wranglingDiscover supervised, unsupervised, probabilistic, and Bayesian machine learning methodsGet to grips with graph processing and sentiment analysisBook Description: Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you'll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines.Starting with the essential statistical and data analysis fundamentals using Python, you'll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You'll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you'll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you'll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask.By the end of this data analysis book, you'll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.What You Will Learn: Explore data science and its various process modelsPerform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing valuesCreate interactive visualizations using Matplotlib, Seaborn, and BokehRetrieve, process, and store data in a wide range of formatsUnderstand data preprocessing and feature engineering using pandas and scikit-learnPerform time series analysis and signal processing using sunspot cycle dataAnalyze textual data and image data to perform advanced analysisGet up to speed with parallel computing using DaskWho this book is for: This book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands-on approach. A basic understanding of math and working knowledge of the Python programming language will help you get started with this book.
Smoke - An Android Echo Chat Software Application
Smoke is a Personal Chat Messenger - an Android Echo Software Application which is known as worldwide the first mobile McEliece Messenger (McEliece, Fujisaka and Pointcheval). This Volume I is about the Chat Client Smoke. Volume II of the same author is about the referring SmokeStack Chat Server. This Open Source Technical Website Reference Documentation on paper addresses to students, teachers, and developers to create a Personal Chat Messenger based on Java for learning and teaching purposes. The book introduces into TCP over Echo (TCPE), Cryptographic Discovery, Fiasco Forwarding Keys, an Argon2id key-derivation function, the Steam file transfer protocol and the Juggling Juggernaut Protocol for Juggernaut Keys and further topics.
SmokeStack - An Android Echo Chat Server Application
SmokeStack is a Personal Chat Server - an Android Echo Server Application for the Chat Messenger Smoke which is known as worldwide the first mobile McEliece Messenger (McEliece, Fujisaka and Pointcheval). This Volume II is about the Chat Server SmokeStack. Volume I of the same author is about the referring Smoke Chat Messenger Client. This Open Source Technical Website Reference Documentation on paper addresses to students, teachers, and developers to create a Personal Chat Server on Android based on Java for learning and teaching purposes. The book introduces into TCP over Echo (TCPE), Cryptographic Discovery, Fiasco Forwarding Keys, an Argon2id key-derivation function, the Steam file transfer protocol and the Juggling Juggernaut Protocol for Juggernaut Keys, mobile Server Technologies, Ozone Postboxes and further topics.
Supercomputing
This book constitutes the refereed post-conference proceedings of the 6th Russian Supercomputing Days, RuSCDays 2020, held in Moscow, Russia, in September 2020.* The 51 revised full and 4 revised short papers presented were carefully reviewed and selected from 106 submissions. The papers are organized in the following topical sections: parallel algorithms; supercomputer simulation; HPC, BigData, AI: architectures, technologies, tools; and distributed and cloud computing. * The conference was held virtually due to the COVID-19 pandemic.
Knowledge-Based Explorable Extended Reality Environments
This book presents explorable XR environments--their rationale, concept, architectures as well as methods and tools for spatial-temporal composition based on domain knowledge, including geometrical, presentational, structural and behavioral elements. Explorable XR environments enable monitoring, analyzing, comprehending, examining and controlling users' and objects' behavior and features as well as users' skills, experience, interests and preferences.The E-XR approach proposed in this book relies on two main pillars. The first is knowledge representation technologies, such as logic programming, description logics and the semantic web, which permit automated reasoning and queries. The second is imperative programming languages, which are a prevalent solution for building XR environments. Potential applications of E-XR are in a variety of domains, e.g., education, training, medicine, design, tourism, marketing, merchandising, engineering and entertainment.The book's readers will understand the emerging domain of explorable XR environments with their possible applications. Special attention is given to an in-depth discussion of the field with taxonomy and classification of the available related solutions. Examples and design patterns of knowledge-based composition and exploration of XR behavior are provided, and an extensive evaluation and analysis of the proposed approach is included. This book helps researchers in XR systems, 3D modeling tools and game engines as well as lecturers and students who search for clearly presented information supported by use cases. For XR and game programmers as well as graphic designers, the book is a valuable source of information and examples in XR development. Professional software and web developers may find the book interesting as the proposed ideas are illustrated by rich examples demonstrating design patterns and guidelines in object-oriented, procedural and declarative programming.
Essential Statistics for Non-STEM Data Analysts
Reinforce your understanding of data science and data analysis from a statistical perspective to extract meaningful insights from your data using Python programmingKey featuresWork your way through the entire data analysis pipeline with statistics concerns in mind to make reasonable decisionsUnderstand how various data science algorithms functionBuild a solid foundation in statistics for data science and machine learning using Python-based examplesBook DescriptionStatistics remain the backbone of modern analysis tasks, helping you to interpret the results produced by data science pipelines. This book is a detailed guide covering the math and various statistical methods required for undertaking data science tasks.The book starts by showing you how to preprocess data and inspect distributions and correlations from a statistical perspective. You'll then get to grips with the fundamentals of statistical analysis and apply its concepts to real-world datasets. As you advance, you'll find out how statistical concepts emerge from different stages of data science pipelines, understand the summary of datasets in the language of statistics, and use it to build a solid foundation for robust data products such as explanatory models and predictive models. Once you've uncovered the working mechanism of data science algorithms, you'll cover essential concepts for efficient data collection, cleaning, mining, visualization, and analysis. Finally, you'll implement statistical methods in key machine learning tasks such as classification, regression, tree-based methods, and ensemble learning.By the end of this Essential Statistics for Non-STEM Data Analysts book, you'll have learned how to build and present a self-contained, statistics-backed data product to meet your business goals.What you will learnFind out how to grab and load data into an analysis environmentPerform descriptive analysis to extract meaningful summaries from dataDiscover probability, parameter estimation, hypothesis tests, and experiment design best practicesGet to grips with resampling and bootstrapping in PythonDelve into statistical tests with variance analysis, time series analysis, and A/B test examplesUnderstand the statistics behind popular machine learning algorithmsAnswer questions on statistics for data scientist interviewsWho this book is forThis book is an entry-level guide for data science enthusiasts, data analysts, and anyone starting out in the field of data science and looking to learn the essential statistical concepts with the help of simple explanations and examples. If you're a developer or student with a non-mathematical background, you'll find this book useful. Working knowledge of the Python programming language is required.
GIS Guidebook
The new Task Items in ArcGIS Pro allow you to develop a step-by-step script that guides users through a workflow or process. This book is designed to teach you how to document, streamline, and develop the processes that step through your workflows using Tasks. The book contains discussions of the topics associated with tasks, and examples of how they are used. Each section also has descriptions of the building process and parameter settings for these items so that you will not only understand the examples shown here but be able to transfer that knowledge to your own workflows and processes. After each of the discussion sections there are exercises to work through that will provide some hands-on experience with the features and settings, including tips and tricks for their best use. At the end of the book are two project suggestions that you can complete on your own, either with the provided data or using your own data sets.
Semantic Systems. in the Era of Knowledge Graphs
This open access book constitutes the refereed proceedings of the 16th International Conference on Semantic Systems, SEMANTiCS 2020, held in Amsterdam, The Netherlands, in September 2020. The conference was held virtually due to the COVID-19 pandemic.
Artificial Intelligence and Soft Computing
The two-volume set LNCS 12415 and 12416 constitutes the refereed proceedings of of the 19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020, held in Zakopane, Poland*, in October 2020.The 112 revised full papers presented were carefully reviewed and selected from 265 submissions. The papers included in the first volume are organized in the following six parts: ​neural networks and their applications; fuzzy systems and their applications; evolutionary algorithms and their applications; pattern classification; bioinformatics, biometrics and medical applications; artificial intelligence in modeling and simulation.The papers included in the second volume are organized in the following four parts: computer vision, image and speech analysis; data mining; various problems of artificial intelligence; agent systems, robotics and control.*The conference was held virtually due to the COVID-19 pandemic.
Data Engineering with Python
Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projectsKey features: Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examplesDesign data models and learn how to extract, transform, and load (ETL) data using PythonSchedule, automate, and monitor complex data pipelines in productionBook DescriptionData engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python.The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You'll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You'll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you'll build architectures on which you'll learn how to deploy data pipelines.By the end of this Python book, you'll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production.What you will learnUnderstand how data engineering supports data science workflowsDiscover how to extract data from files and databases and then clean, transform, and enrich itConfigure processors for handling different file formats as well as both relational and NoSQL databasesFind out how to implement a data pipeline and dashboard to visualize resultsUse staging and validation to check data before landing in the warehouseBuild real-time pipelines with staging areas that perform validation and handle failuresGet to grips with deploying pipelines in the production environmentWho this book is forThis book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering or refresh their knowledge of data engineering using Python. This book will also be useful for students planning to build a career in data engineering or IT professionals preparing for a transition. No previous knowledge of data engineering is required.
Tools and Algorithms for the Construction and Analysis of Systems
This book is Open Access under a CC BY licence. This book, LNCS 11429, is part III of the proceedings of the 25th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2019, which took place in Prague, Czech Republic, in April 2019, held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019. It's a special volume on the occasion of the 25 year anniversary of TACAS. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Tools and Algorithms for the Construction and Analysis of Systems
This book is Open Access under a CC BY licence. This book, LNCS 11429, is part III of the proceedings of the 25th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2019, which took place in Prague, Czech Republic, in April 2019, held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019. It's a special volume on the occasion of the 25 year anniversary of TACAS. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
6gn for Future Wireless Networks
This book constitutes the proceedings of the Third International Conference on 6G for Future Wireless Networks, 6GN 2020, held in Tianjin, China, in August 2020. The conference was held virtually due to the COVID-19 pandemic. The 45 full papers were selected from 109 submissions and present the state of the art and practical applications of 6G technologies. The papers are arranged thematically on network scheduling and optimization; wireless system and platform; intelligent applications; network performance evaluation; cyber security and privacy; technologies for private 5G/6G.
DEEP LEARNING with MATLAB. NEURAL NETWORKS by EXAMPLES
MATLAB has the tool Deep Learning Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets (Big data), you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox.
Blockchain Data Analytics for Dummies
Get ahead of the curve--learn about big data on the blockchain Blockchain came to prominence as the disruptive technology that made cryptocurrencies work. Now, data pros are using blockchain technology for faster real-time analysis, better data security, and more accurate predictions. Blockchain Data Analytics For Dummies is your quick-start guide to harnessing the potential of blockchain. Inside this book, technologists, executives, and data managers will find information and inspiration to adopt blockchain as a big data tool. Blockchain expert Michael G. Solomon shares his insight on what the blockchain is and how this new tech is poised to disrupt data. Set your organization on the cutting edge of analytics, before your competitors get there! Learn how blockchain technologies work and how they can integrate with big data Discover the power and potential of blockchain analytics Establish data models and quickly mine for insights and results Create data visualizations from blockchain analysis Discover how blockchains are disrupting the data world with this exciting title in the trusted For Dummies line!
Implementing Azure DevOps Solutions
A comprehensive guide to becoming a skilled Azure DevOps engineerKey Features Explore a step-by-step approach to designing and creating a successful DevOps environment Understand how to implement continuous integration and continuous deployment pipelines on Azure Integrate and implement security, compliance, containers, and databases in your DevOps strategies Book Description Implementing Azure DevOps Solutions helps DevOps engineers and administrators to leverage Azure DevOps Services to master practices such as continuous integration and continuous delivery (CI/CD), containerization, and zero downtime deployments. This book starts with the basics of continuous integration, continuous delivery, and automated deployments. You will then learn how to apply configuration management and Infrastructure as Code (IaC) along with managing databases in DevOps scenarios. Next, you will delve into fitting security and compliance with DevOps. As you advance, you will explore how to instrument applications, and gather metrics to understand application usage and user behavior. The latter part of this book will help you implement a container build strategy and manage Azure Kubernetes Services. Lastly, you will understand how to create your own Azure DevOps organization, along with covering quick tips and tricks to confidently apply effective DevOps practices. By the end of this book, you'll have gained the knowledge you need to ensure seamless application deployments and business continuity. What you will learn Get acquainted with Azure DevOps Services and DevOps practices Implement CI/CD processes Build and deploy a CI/CD pipeline with automated testing on Azure Integrate security and compliance in pipelines Understand and implement Azure Container Services Become well versed in closing the loop from production back to development Who this book is for This DevOps book is for software developers and operations specialists interested in implementing DevOps practices for the Azure cloud. Application developers and IT professionals with some experience in software development and development practices will also find this book useful. Some familiarity with Azure DevOps basics is an added advantage. Professionals preparing for the Exam AZ-400: Designing and Implementing Microsoft DevOps Solutions certification will also find this book useful.
E-Business and Telecommunications
This book contains a compilation of the revised and extended versions of the best papers presented at the 16th International Joint Conference on E-Business and Telecommunications, ICETE 2019, held in Prague, Czech Republic, in July 2019.ICETE is a joint international conference integrating four major areas of knowledge that are divided into six corresponding conferences: International Conference on Data Communication Networking, DCNET; International Conference on E-Business, ICE-B; International Conference on Optical Communication Systems, OPTICS; International Conference on Security and Cryptography, SECRYPT; International Conference on Signal Processing and Multimedia, SIGMAP; International Conference on Wireless Information Systems, WINSYS. The 11 full papers presented in the volume were carefully reviewed and selected from the 166 submissions. The papers cover the following key areas of data communication networking, e-business, security and cryptography, signal processing and multimedia applications.
Fifty Years of Relational, and Other Database Writings
Fifty years of relational. It's hard to believe the relational model has been around now for over half a century! But it has-it was born on August 19th, 1969, when Codd's first database paper was published. And Chris Date has been involved with it for almost the whole of that time, working closely with Codd for many years and publishing the very first, and definitive, book on the subject in 1975. In this book's title essay, Chris offers his own unique perspective (two chapters) on those fifty years. No database professional can afford to miss this one of a kind history. But there's more to this book than just a little personal history. Another unique feature is an extensive and in depth discussion (nine chapters) of a variety of frequently asked questions on relational matters, covering such topics as mathematics and the relational model; relational algebra; predicates; relation valued attributes; keys and normalization; missing information; and the SQL language. Another part of the book offers detailed responses to critics (four chapters). Finally, the book also contains the text of several recent interviews with Chris Date, covering such matters as RM/V2, XML, NoSQL, The Third Manifesto, and how SQL came to dominate the database landscape. About Chris: Chris Date has a stature that is unique in the database industry. He is best known for his textbook An Introduction to Database Systems (Addison-Wesley), which has sold some 900,000 copies at the time of writing. He enjoys a reputation that is second to none for his ability to explain complex technical issues in a clear and understandable fashion. He was inducted into the Computing Industry Hall of Fame in 2004.
Building Machine Learning Pipelines
Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques
Mastering Kafka Streams and Ksqldb
Working with unbounded and fast-moving data streams has historically been difficult. But with Kafka Streams and ksqlDB, building stream processing applications is easy and fun. This practical guide shows data engineers how to use these tools to build highly scalable stream processing applications for moving, enriching, and transforming large amounts of data in real time. Mitch Seymour, data services engineer at Mailchimp, explains important stream processing concepts against a backdrop of several interesting business problems. You'll learn the strengths of both Kafka Streams and ksqlDB to help you choose the best tool for each unique stream processing project. Non-Java developers will find the ksqlDB path to be an especially gentle introduction to stream processing. Learn the basics of Kafka and the pub/sub communication pattern Build stateless and stateful stream processing applications using Kafka Streams and ksqlDB Perform advanced stateful operations, including windowed joins and aggregations Understand how stateful processing works under the hood Learn about ksqlDB's data integration features, powered by Kafka Connect Work with different types of collections in ksqlDB and perform push and pull queries Deploy your Kafka Streams and ksqlDB applications to production
Deep Learning for Coders with Fastai and Pytorch
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Engineering Multi-Agent Systems7th International Workshop, Emas 2019, Montreal, Qc, Canada
Combinatorial Optimization6th International Symposium, Isco 2020, Montreal, Qc, Canada, Ma
Reversible Computation
This book constitutes the refereed proceedings of the 12th International Conference on Reversible Computation, RC 2020, held in Oslo, Norway, in July 2020. The 17 full papers included in this volume were carefully reviewed and selected from 22 submissions. The papers are organized in the following topical sections: theory and foundation; programming languages; circuit synthesis; evaluation of circuit synthesis; and applications and implementations.
The Shape of Data in Digital Humanities
Data and its technologies now play a large and growing role in humanities research and teaching. This book addresses the needs of humanities scholars who seek deeper expertise in the area of data modeling and representation. The authors, all experts in digital humanities, offer a clear explanation of key technical principles, a grounded discussion of case studies, and an exploration of important theoretical concerns. The book opens with an orientation, giving the reader a history of data modeling in the humanities and a grounding in the technical concepts necessary to understand and engage with the second part of the book. The second part of the book is a wide-ranging exploration of topics central for a deeper understanding of data modeling in digital humanities. Chapters cover data modeling standards and the role they play in shaping digital humanities practice, traditional forms of modeling in the humanities and how they have been transformed by digital approaches, ontologies which seek to anchor meaning in digital humanities resources, and how data models inhabit the other analytical tools used in digital humanities research. It concludes with a glossary chapter that explains specific terms and concepts for data modeling in the digital humanities context. This book is a unique and invaluable resource for teaching and practising data modeling in a digital humanities context.
Artificial Intelligence in Education
This two-volume set LNAI 12163 and 12164 constitutes the refereed proceedings of the 21th International Conference on Artificial Intelligence in Education, AIED 2020, held in Ifrane, Morocco, in July 2020.*The 49 full papers presented together with 66 short, 4 industry & innovation, 4 doctoral consortium, and 4 workshop papers were carefully reviewed and selected from 214 submissions. The conference provides opportunities for the cross-fertilization of approaches, techniques and ideas from the many fields that comprise AIED, including computer science, cognitive and learning sciences, education, game design, psychology, sociology, linguistics as well as many domain-specific areas. ​*The conference was held virtually due to the COVID-19 pandemic.
Reversible Computation: Extending Horizons of Computing
This open access State-of-the-Art Survey presents the main recent scientific outcomes in the area of reversible computation, focusing on those that have emerged during COST Action IC1405 "Reversible Computation - Extending Horizons of Computing", a European research network that operated from May 2015 to April 2019.Reversible computation is a new paradigm that extends the traditional forwards-only mode of computation with the ability to execute in reverse, so that computation can run backwards as easily and naturally as forwards. It aims to deliver novel computing devices and software, and to enhance existing systems by equipping them with reversibility. There are many potential applications of reversible computation, including languages and software tools for reliable and recovery-oriented distributed systems and revolutionary reversible logic gates and circuits, but they can only be realized and have lasting effect if conceptual and firm theoretical foundations are established first.
Microsoft Exchange Server 2016 Administration GuideDeploy, Manage and Administer Microsoft
Discover and work with the new features in Microsoft Exchange Server 2016 Key FeaturesDeploy Exchange 2016 in a new environment or coexisting environment with a legacy version of Exchange. Learn how to migrate your environment from Exchange 2010 or 2013 to Exchange 2016.Get familiar with Failover Cluster Manager as well as creating and managing Database Availability Groups (DAG). Learn how to migrate unified messaging using Microsoft's guidelines. Description This book is a handy guide on how you can use the features of Microsoft Exchange Server 2016. It begins with sharing the new features of Exchange 2016 and compares it with the previous versions. This book will help you install Exchange 2016 and give you an in-depth understanding of how to configure its server end-to-end to ensure its fully operational. You will then go through the client connectivity protocols by configuring each one of them. Later you will learn how to view, create, and configure Databases and Database Availability Groups. Next, you will perform migrations of Unified Messaging and also mailbox migrations in different ways in Exchange 2016. Lastly, you will work with the new commands of Exchange Management Shell and Exchange Admin Center. Towards the end, you will go through the common issues in Exchange 2016 and learn how to fix them. What will you learnLearn how to configure all the Client connectivity protocols.View, Create and Configure Database and Database Availability Group.Create Public folders and Migrating Public folders from earlier versions of Microsoft Exchange.Understand the working of Exchange Management Shell and Exchange Admin Center.Troubleshoot some common issues in Exchange 2016.Who this book is for This book is for anyone interested in or using Microsoft Exchange 2016. It is also for professionals who have been using Microsoft Exchange 2013 and would like to get familiar with the new features of Exchange 2016. Table of Contents 1. Introduction to Exchange 20162. Installation of Exchange 20163. Post Configuration4. Post Configuration Continued5. Client Connectivity6. Databases and Database Availability Groups7. Public Folders8. Unified Messaging9. Migrations10. Exchange Management Shell vs. EAC11. Troubleshooting common issuesAbout the Author Edward van Biljon is an Experienced Messaging Specialist with a demonstrated history of working in the information technology and services industry. He is a four-time Office Apps & Services MVP with 18 years of experience in Exchange.Edward is also a Microsoft Certified Trainer and spends a lot of time teaching Exchange and other technologies like Azure and Office 365.Edward is a passionate blogger and creates videos and articles on how to do things in Exchange or how to fix a problem in Exchange. You can also find him on the TechNet Forums, assisting people that require help with their Exchange environment.Your Blog links: https: //collaborationpro.com https: //everything-powershell.comYour LinkedIn Profile: https: //www.linkedin.com/in/edward-van-biljon-75946840
Social Robotics
This book constitutes the refereed proceedings of the 11th International Conference on Social Robotics, ICSR 2019, held in Madrid, Spain, in November 2019.The 69 full papers presented were carefully reviewed and selected from 92 submissions. The theme of the 2018 conference is: Friendly Robotics.The papers focus on the following topics: perceptions and expectations of social robots; cognition and social values for social robots; verbal interaction with social robots; social cues and design of social robots; emotional and expressive interaction with social robots; collaborative SR and SR at the workplace; game approaches and applications to HRI; applications in health domain; robots at home and at public spaces; robots in education; technical innovations in social robotics; and privacy and safety of the social robots.
Graph Transformation
This book constitutes the refereed proceedings of the 13th International Conference on Graph Transformation, ICGT 2020, in Bergen, Norway, in June 2020.*The 16 research papers and 4 tool paper presented in this book were carefully reviewed and selected from 40 submissions. One invited paper is also included. The papers deal with the following topics: theoretical advances; application domains; and tool presentations.*The conference was held virtually due to the COVID-19 pandemic.
Data Modeling Made Simple with erwin DM
Master erwin DM to deliver robust and precise designs for both operational and analytical projects. Steve and Jeff start from the basics, explaining data modeling concepts and how to get up and running with erwin DM (erwin DM). Through a hands-on approach, business analysts, data professionals, and project managers will learn step-by-step how to build effective conceptual, logical, and physical data models. Complete the stages in identifying essential business requirements, designing the logical data model, transposing those logical modeling objects into physical tables and columns, and even generating the implementation database scripts.This book contains seven parts. Part I provides a foundation in data modeling and Part II a foundation in erwin DM. Part III covers the design layer technique and its application using erwin DM, distinguishing conceptual, logical, physical, and operational data models. Part IV covers entities, domains, attributes, key groups, validation rules, default rules, and subject areas, along with how to implement them using erwin DM. Part V explains the physical data model and how to convert a logical data model to a physical data model in erwin DM. Become confident creating tables, columns, indexes, and views. Part VI reveals advanced features available within erwin DM, including user defined properties, naming standards, forward engineering, reverse engineering, complete compare, report designer, and the bulk editor. Part VII explains several important tools to use in combination with erwin DM, including erwin DM NoSQL, erwin Data Catalog, and erwin Data Literacy.
Combinatorial Algorithms
This book constitutes the proceedings of the 31st International Workshop on Combinatorial Algorithms which was planned to take place in Bordeaux, France, during June 8-10, 2020. Due to the COVID-19 pandemic the conference changed to a virtual format. The 30 full papers included in this book were carefully reviewed and selected from 62 submissions. They focus on algorithms design for the myriad of combinatorial problems that underlie computer applications in science, engineering and business.
The Future of Technology in Education
The Future of Technology in Education We are facing like never before extreme challenges in economic, social, and environmental. It is driven by accelerating globalization and the speed rate of technological developments in this world.Simultaneously, those forces are providing us with countless new opportunities for human advancement. While the future is uncertain and we cannot envision or speculate about the future, but we need to be prepared and be ready for it.New technologies like artificial intelligence, plus machine learning and educational software are changing the field for students plus shaking up the role of educators, creating changes in the way they teach, and structure or remodeling the classroom.A teacher must adapt to the new way of teaching as the research suggests that technology is automating an extremely large proportion of jobs worldwide, and this is disrupting the global economy and will eventually leave millions of people unemployed.This is a wake-up call!We must prepare our students with proper technology to help further facilitate and streamline these changes within the educational institution
Logic and Relational Theory
This book is a revised, upgraded, and hugely improved version of an earlier one called Logic and Databases. Although it's effectively a brand new book, therefore, the following remarks from that earlier book are still relevant here. First, logic and databases are inextricably intertwined. The relational model itself is essentially just elementary logic, tailored to database needs. Now, if you're a database professional, this won't be news to you-but you still might not realize just how much everything we do in the database world is (or should be!) affected by logic. Logic is fundamental, and everywhere. As a database professional, therefore, you owe it to yourself to understand the basics of formal logic, and you ought to be able to explain (and perhaps defend) the connections between formal logic and database technology. And that's what this book is about. What it does is show, through a series of partly independent, partly interrelated essays, just how various crucial aspects of database technology-some of them very familiar, others maybe less so-are solidly grounded in formal logic. Overall, the goal is to help you realize the importance of logic in everything you do, and also, I hope, to help you see that logic can be fun.
Smarter Data Science
Organizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how. Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive. Smarter Data Science helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments. When an organization manages its data effectively, its data science program becomes a fully scalable function that's both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise. By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements: Improving time-to-value with infused AI models for common use cases Optimizing knowledge work and business processes Utilizing AI-based business intelligence and data visualization Establishing a data topology to support general or highly specialized needs Successfully completing AI projects in a predictable manner Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computing When they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations.
Artificial Intelligence Basics
Designed as a self-teaching introduction to the fundamental concepts of artificial intelligence, the book begins with its history, the Turing test, and early applications. Later chapters cover the basics of searching, game playing, and knowledge representation. Expert systems and machine learning are covered in detail, followed by separate programming chapters on Prolog and Python. The concluding chapter on artificial intelligence machines and robotics is comprehensive with numerous modernapplications. Features: Covers an introduction to concepts related to AI, including searching processes, knowledge representation, machine learning, expert systems, programming, and roboticsIncludes separate chapters on Prolog and Python to introduce basic programming techniques in AI
The Rosedata Stone
Creating a precise diagram of business terms within your projects is a simple yet powerful communication tool for project managers, data governance professionals, and business analysts.With more and more data being created and used, combined with intense competition, strict regulations, and rapid-spread social media, the financial, liability, and credibility stakes have never been higher and therefore the need for a Common Business Language has never been greater. Appreciate the power of the BTM and apply the steps to build a BTM over the book's five chapters: Challenges. Explore how a Common Business Language is more important than ever with technologies like the Cloud and NoSQL, and Regulations such as the GDPR.Needs. Identify scope and plan precise, minimal visuals that will capture the Common Business Language. Solution. Meet the BTM and its components, along with the variations of relational and dimensional BTMs. Experience how several data modeling tools display the BTM, including CaseTalk, ER/Studio, erwin DM, and Hackolade.Construction. Build operational (relational) and analytics (dimensional) BTMs for a bakery chain.Practice. Reinforce BTM concepts and build BTMs for two of your own initiatives alongside a real example.
Embedded Vision
Embedded vision is the integration of "computer vision" intomachines that use algorithms to decode meaning from observed images or video.It has a wide range of applications to machine learning, artificialintelligence, industrial, medical, driverless cars, drones, smart phones, aerospace, defense, agriculture, consumer, surveillance, robotics and security.This book is an introductory guide for anyone who is interested in designing machinesthat have vision-enabled, embedded products. It covers a large number of topicsencountered in hardware architecture, software algorithms, applications, advancements in camera, processors, and sensors in the field of embeddedvision.Features: Includes a wide range of applications to artificialintelligence, machine learning, industry, science, medicine, transportation, civil infrastructure, and security Covers a large number of topics encountered in hardwarearchitecture, software algorithms, applications, advancements in processors andsensors.
Interactive Collaborative Robotics
This book constitutes the refereed proceedings of the 4th International Conference on Interactive Collaborative Robotics, ICR 2019, held in Istanbul, Turkey, in August 2019. The 32 papers presented in this volume were carefully reviewed and selected from 46 submissions. They deal with challenges of human-robot interaction; robot control and behavior in social robotics and collaborative robotics; and applied robotic and cyber-physical systems.
Robocup 2018: Robot World Cup XXII
This book includes the post-conference proceedings of the 22nd RoboCup International Symposium, held in Montreal, QC, Canada, in June 2018. The 32 full revised papers and 11 papers from the winning teams presented were carefully reviewed and selected from 51 submissions. This book highlights the approaches of champion teams from the competitions and documents the proceedings of the 22nd annual RoboCup International Symposium. Due to the complex research challenges set by the RoboCup initiative, the RoboCup International Symposium offers a unique perspective for exploring scientific and engineering principles underlying advanced robotic and AI systems.
