Multimedia Technology and Enhanced Learning
This two-volume book constitutes the refereed proceedings of the 3rd International Conference on Multimedia Technology and Enhanced Learning, ICMTEL 2021, held in April 2021. Due to the COVID-19 pandemic the conference was held virtually. The 97 revised full papers have been selected from 208 submissions. They describe new learning technologies which range from smart school, smart class and smart learning at home and which have been developed from new technologies such as machine learning, multimedia and Internet of Things.
Maple in Mathematics Education and Research
This book constitutes refereed proceedings of the 4th Maple Conference, MC 2020, held in Waterloo, Ontario, Canada, in November 2020. The 25 revised full papers and 3 short papers were carefully reviewed and selected out of 75 submissions, one invited paper is also presented in the volume. The papers included in this book cover topics in education, algorithms, and applciations of the mathematical software Maple.
Applied Cryptography in Computer and Communications
This book constitutes the refereed post-conference proceedings of the First International Conference on Applied Cryptography in Computer and Communications, AC3 2021, and the First International Workshop on Security for Internet of Things (IoT). The conference was held in May 2021 and due to COVID-19 pandemic virtually.The 15 revised full papers were carefully reviewed and selected from 42 submissions. The papers present are grouped in 4 tracks on blockchain; authentication; secure computation; practical crypto application. They detail technical aspects of applied cryptography, including symmetric cryptography, public-key cryptography, cryptographic protocols, cryptographic implementations, cryptographic standards and practices.
Advances of Science and Technology
This two-volume set constitutes the refereed post-conference proceedings of the 8th International Conference on Advancement of Science and Technology, ICAST 2020, which took place in Bahir Dar, Ethiopia, in October 2020.The 74 revised full papers were carefully reviewed and selected from more than 200 submissions of which 157 were sent out for peer review. The papers present economic and technologic developments in modern societies in 6 tracks: Chemical, food and bio-process engineering; Electrical and computer engineering; IT, computer science and software engineering; Civil, water resources, and environmental engineering; Mechanical and industrial engineering; Material science and engineering.
Advances of Science and Technology
This two-volume set constitutes the refereed post-conference proceedings of the 8th International Conference on Advancement of Science and Technology, ICAST 2020, which took place in Bahir Dar, Ethiopia, in October 2020.The 74 revised full papers were carefully reviewed and selected from more than 200 submissions of which 157 were sent out for peer review. The papers present economic and technologic developments in modern societies in 6 tracks: Chemical, food and bio-process engineering; Electrical and computer engineering; IT, computer science and software engineering; Civil, water resources, and environmental engineering; Mechanical and industrial engineering; Material science and engineering.
Getting Started with Streamlit for Data Science
Create, deploy, and test your Python applications, analyses, and models with ease using StreamlitKey Features: Learn how to showcase machine learning models in a Streamlit application effectively and efficientlyBecome an expert Streamlit creator by getting hands-on with complex application creationDiscover how Streamlit enables you to create and deploy apps effortlesslyBook Description: Streamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time.You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you'll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps.By the end of this book, you'll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python.What You Will Learn: Set up your first development environment and create a basic Streamlit app from scratchExplore methods for uploading, downloading, and manipulating data in Streamlit appsCreate dynamic visualizations in Streamlit using built-in and imported Python librariesDiscover strategies for creating and deploying machine learning models in StreamlitUse Streamlit sharing for one-click deploymentBeautify Streamlit apps using themes, Streamlit Components, and Streamlit sidebarImplement best practices for prototyping your data science work with StreamlitWho this book is for: This book is for data scientists and machine learning enthusiasts who want to create web apps using Streamlit. Whether you're a junior data scientist looking to deploy your first machine learning project in Python to improve your resume or a senior data scientist who wants to use Streamlit to make convincing and dynamic data analyses, this book will help you get there! Prior knowledge of Python programming will assist with understanding the concepts covered.
Advances in the Theory of Probabilistic and Fuzzy Data Scientific Methods with Applications
This book focuses on the advanced soft computational and probabilistic methods that the authors have published over the past few years. It describes theoretical results and applications, and discusses how various uncertainty measures - probability, plausibility and belief measures - can be treated in a unified way. It also examines approximations of four notable probability distributions (Weibull, exponential, logistic and normal) using a unified probability distribution function, and presents a fuzzy arithmetic-based time series model that provides an easy-to-use forecasting technique. Lastly, it proposes flexible fuzzy numbers for Likert scale-based evaluations. Featuring methods that can be successfully applied in a variety of areas, including engineering, economics, biology and the medical sciences, the book offers useful guidelines for practitioners and researchers.
Experimental IR Meets Multilinguality, Multimodality, and Interaction
This book constitutes the refereed proceedings of the 12th International Conference of the CLEF Association, CLEF 2021, held virtually in September 2021.The conference has a clear focus on experimental information retrieval with special attention to the challenges of multimodality, multilinguality, and interactive search ranging from unstructured to semi structures and structured data. The 11 full papers presented in this volume were carefully reviewed and selected from 21 submissions. This year, the contributions addressed the following challenges: application of neural methods for entity recognition as well as misinformation detection in the health area, skills extraction in job-match databases, stock market prediction using financial news, and extraction of audio features for podcast retrieval. In addition to this, the volume presents 5 "best of the labs" papers which were reviewed as full paper submissions with the same review criteria. 12 lab overview papers were accepted and represent scientific challenges based on new data sets and real world problems in multimodal and multilingual information access.
Smart and Intelligent Systems
In today's digital world, the words "smart" and intelligent" are now used to label devices, machinery, systems, and even environments. What is a "smart" system? Is "smart" synonymous to "intelligent"? If not, what does an "intelligent system" mean? Are all the smart systems intelligent?
National Cyber Summit (Ncs) Research Track 2021
Part I - Cyber Security EducationAn Integrated System for Connecting Cybersecurity Competency, Student Activities and Career Building Li-Chiou Chen, Andreea Cotoranu, Praviin Mandhare and Darren Hayes Simulating Industrial Control Systems using Node-RED and Unreal Engine 4 Steven Day, William Smallwood and Joshua Kuhn Student Educational Learning Experience Through Cooperative Research Melissa Hannis, Idongesit Mkpong-Ruffin and Drew Hamilton Digital Forensics Education: Challenges and Future Opportunities Megan Stigall and Kim-Kwang Raymond Choo Designing a Cybersecurity Curriculum Library: Best Practices from Digital Library Research Blair Taylor, Sidd Kaza and Melissa Dark Design of a Virtual Cybersecurity Escape Room Tania Williams and Omar El-Gayar Part II - Cyber Security Technology A Novel Method for the Automated Generation for JOP Chain Exploits Bramwell Brizendine, Austin Babcock and Josh Stroschien Increasing Log Availability in Unmanned Vehicle Systems Nicholas Carter, Peter Pommer, Duane Davis and Cynthia Irvine Testing Detection of K-Ary Code Obfuscated by Metamorphic and Polymorphic Techniques George Harter and Neil Rowe Enhancing Secure Coding Assistant System with Design by Contract and Programming Logic Wenhui Liang, Cui Zhang and Jun Dai Social Engineering Attacks in Healthcare Systems: A Survey Christopher Nguyen, Walt Williams, Brandon Didlake, Donte Mitchell, James McGinnis and Dipankar Dasgupta Identifying Anomalous Industrial-Control-System Network Flow Activity Using Cloud Honeypots Neil Rowe, Thuy Nguyen, Jeffrey Dougherty, Matthew Bieker and Darry Pilkington Risks of Electric Vehicle Supply Equipment Integration within Building Energy Management System Environments: A Look at Remote Attack Surface and Implications Roland Varriale, Michael Jaynes and Ryan Crawford
Chinese Computational Linguistics
This book constitutes the proceedings of the 20th China National Conference on Computational Linguistics, CCL 2021, held in Hohhot, China, in August 2021.The 31 full presented in this volume were carefully reviewed and selected from 90 submissions. The conference papers covers the following topics such as Machine Translation and Multilingual Information Processing, Minority Language Information Processing, Social Computing and Sentiment Analysis, Text Generation and Summarization, Information Retrieval, Dialogue and Question Answering, Linguistics and Cognitive Science, Language Resource and Evaluation, Knowledge Graph and Information Extraction, and NLP Applications.
Data Science for Economics and Finance
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
The Analytics Process
This book is about the process of using analytics and the capabilities of analytics in today's organizations. Cutting through the buzz surrounding the term analytics and the overloaded expectations about using analytics, the book demystifies analytics with an in-depth examination of concepts grounded in operations research and management science. Analytics as a set of tools and processes is only as effective as: The data with which it is working The human judgment applying the processes and understanding the output of these processes. For this reason, the book focuses on the analytics process. What is intrinsic to analytics' real organizational impact are the careful application of tools and the thoughtful application of their outcomes. This work emphasizes analytics as part of a process that supports decision-making within organizations. It wants to debunk overblown expectations that somehow analytics outputs or analytics as applied to other concepts, such as Big Data, are the be-all and end-all of the analytics process. They are, instead, only a step within a holistic and critical approach to management thinking that can create real value for an organization.To develop this holistic approach, the book is divided into two sections that examine concepts and applications. The first section makes the case for executive management taking a holistic approach to analytics. It draws on rich research in operations and management science that form the context in which analytics tools are to be applied. There is a strong emphasis on knowledge management concepts and techniques, as well as risk management concepts and techniques. The second section focuses on both the use of the analytics process and organizational issues that are required to make the analytics process relevant and impactful.
Advanced Information Systems Engineering
This book constitutes the proceedings of the 33rd International Conference on Advanced Information Systems Engineering, CAiSE 2021, which was held online during June 28-July 2, 2021. The conference was planned to take place in Melbourne, Australia, and changed to an online format due to the COVID-19 pandemic. The papers included in these proceedings focus on intelligent information systems and deal with novel approaches to IS engineering; models, methods and techniques in IS engineering; architectures and platforms for IS engineering; and domain specific and multi-aspect in IS engineering.
Text Mining with Machine Learning
This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets, conclusions which are not normally evident, emerge and can be used for various purposes and applications. The book provides explanations of principles of time-proven machine learning algorithms applied in text mining together with step-by-step demonstrations of how to reveal the semantic contents in real-world datasets using the popular R-language with its implemented machine learning algorithms. The book is not only aimed at IT specialists, but is meant for a wider audience that needs to process big sets of text documents and has basic knowledge of the subject, e.g. e-mail service providers, online shoppers, librarians, etc.The book starts with an introduction to text-based natural language data processing and its goals and problems. It focuses on machine learning, presenting various algorithms with their use and possibilities, and reviews the positives and negatives. Beginning with the initial data pre-processing, a reader can follow the steps provided in the R-language including the subsuming of various available plug-ins into the resulting software tool. A big advantage is that R also contains many libraries implementing machine learning algorithms, so a reader can concentrate on the principal target without the need to implement the details of the algorithms her- or himself. To make sense of the results, the book also provides explanations of the algorithms, which supports the final evaluation and interpretation of the results. The examples are demonstrated using realworld data from commonly accessible Internet sources.
Valuing Data
The past decade has seen a dramatic increase in the amount and variety of information that is generated and stored electronically by business enterprises. Storing this increased volume of information has not been a problem to date, but as these information stores grow larger and larger, multiple challenges arise for senior management: namely, questions such as "How much is our data worth?" "Are we storing our data in the most cost-effective way?" "Are we managing our data effectively and efficiently?" "Do we know which data is most important?" "Are we extracting business insight from the right data?" "Are our data adding to the value of our business?" "Are our data a liability?" "What is the potential for monetizing our data?" and "Do we have an appropriate risk management plan in place to protect our data?"To answer these value-based questions, data must be treated with the same rigor and discipline as other tangible and intangible assets. In other words, corporate data should be treated as a potential asset and should have its own asset valuation methodology that is accepted by the business community, the accounting and valuation community, and other important stakeholder groups. Valuing Data: An Open Framework is a first step in that direction. Its purpose is to: Provide the reader with some background on the nature of dataPresent the common categories of business dataExplain the importance of data managementReport the current thinking on data valuationOffer some business reasons to value dataPresent an "open framework"-along with some proposed methods-for valuing dataThe book does not aim to prescribe exactly how data should be valued monetarily, but rather it is a "starting point" for a discussion of data valuation with the objective of developing a stakeholder consensus, which, in turn, will become accepted standards and practices.
Computational Sciences - Modelling, Computing and Soft Computing
This book constitutes revised and selected papers of the First International Conference on Computational Sciences - Modelling, Computing and Soft Computing, held in Kozhikode, Kerala, India, in September 2020. The 15 full papers and 6 short papers presented were thoroughly reviewed and selected from the 150 submissions. They are organized in the topical secions on computing; soft computing; general computing; modelling.
Exam Ref AZ-304 Microsoft Azure Architect Design Certification and Beyond
Master the Microsoft Azure platform and prepare for the AZ-304 certification exam by learning the key concepts needed to identify key stakeholder requirements and translate these into robust solutionsKey Features: Build secure and scalable solutions on the Microsoft Azure platformLearn how to design solutions that are compliant with customer requirementsWork with real-world scenarios to become a successful Azure architect, and prepare for the AZ-304 examBook Description: The AZ-304 exam tests an architect's ability to design scalable, reliable, and secure solutions in Azure based on customer requirements. Exam Ref AZ-304 Microsoft Azure Architect Design Certification and Beyond offers complete, up-to-date coverage of the AZ-304 exam content to help you prepare for it confidently, pass the exam first time, and get ready for real-world challenges.This book will help you to investigate the need for good architectural practices and discover how they address common concerns for cloud-based solutions. You will work through the CloudStack, from identity and access through to infrastructure (IaaS), data, applications, and serverless (PaaS). As you make progress, you will delve into operations including monitoring, resilience, scalability, and disaster recovery. Finally, you'll gain a clear understanding of how these operations fit into the real world with the help of full scenario-based examples throughout the book.By the end of this Azure book, you'll have covered everything you need to pass the AZ-304 certification exam and have a handy desktop reference guide.What You Will Learn: Understand the role of architecture in the cloudEnsure security through identity, authorization, and governanceFind out how to use infrastructure components such as compute, containerization, networking, and storage accountsDesign scalable applications and databases using web apps, functions, messaging, SQL, and Cosmos DBMaintain operational health through monitoring, alerting, and backupsDiscover how to create repeatable and reliable automated deploymentsUnderstand customer requirements and respond to their changing needsWho this book is for: This book is for Azure Solution Architects who advise stakeholders and help translate business requirements into secure, scalable, and reliable solutions. Junior architects looking to advance their skills in the Cloud will also benefit from this book. Experience with the Azure platform is expected, and a general understanding of development patterns will be advantageous.
Advances in Databases and Information Systems
This book constitutes the proceedings of the 25th European Conference on Advances in Databases and Information Systems, ADBIS 2021, held in Tartu, Estonia, in August 2021. The 18 full papers presented together with 3 keynotes were carefully reviewed and selected from 70 submissions. The selected papers span a wide spectrum of topics in databases and related technologies, tackling challenging problems and presenting inventive and efficient solutions. They are organized in 5 sessions: patterns and events, social media and text mining, indexes, queries and constraints, high-dimensional data and data streams, and data integration.
Data and Information in Online Environments
This book constitutes the refereed post-conference proceedings of the Second International Conference on Data Information in Online Environments, DIONE 2021, which took place in March 2021. Due to COVID-19 pandemic the conference was held virtually. DIONE 2021 presents theoretical proposals and practical solutions in the treatment, processing and study of data and information produced in online environments, the latest trends in the analysis of network information, media metrics social, data processing technologies and open science. The 40 revised full papers were carefully reviewed and selected from 86 submissions. The papers are grouped in thematical sessions on evaluation of science in social networking environment; scholarly publishing and online communication; and education in online environments.
Intelligent Information Systems
This book constitutes the thoroughly refereed proceedings of the CAiSE Forum 2021 which was held as part of the 33rd International Conference on Advanced Information Systems Engineering, CAiSE 2021, in June 2021. The conference was held virtually due to the COVID-19 pandemic.The CAiSE Forum is a place within the CAiSE conference for presenting and discussing new ideas and tools related to information systems engineering. Intended to serve as an interactive platform, the Forum aims at the presentation of emerging new topics and controversial positions, as well as demonstration of innovative systems, tools and applications. This year's theme was "Intelligent Information Systems".The 18 full papers presented in this volume were carefully reviewed and selected for inclusion in this book.
97 Things Every Data Engineer Should Know
Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail
Econometrics in Practice
No detailed description available for "Econometrics in Practice".
Data-Centric Business and Applications
Van der Pol Oscillators Based on Transistor Structures with Negative Differential Resistance for Infocommunication System Facilities.- Study of the Influence of Changing Signal Propagation Conditions in the Communication Channel on Bit Error Rate.- Quality Assessment of Measuring the Coordinates of Airborne Objects with a Secondary Surveillance Radar.- Pulse and Multifrequency Van der Pol Generators Based on Transistor Structures with Negative Differential Resistance for Infocommunication System Facilities.- The Method of Redistributing Traffic in Mobile Network.- Complex Tools for Surge Process Analysis and Hardware Disturbance Protection.- Development of Evaluation Templates for the Protection System of Wireless Sensor Network.- Studying of Useful Signal Impact on Convergence Parameters of the Gradient Signal Processing Algorithm for Adaptive Antenna Arrays that Obviates Reference Signal Presence.- Interference Immunity Assessment Identification Friend or Foe Systems.- Estimation of Signal Parameters Using SSA and Linear Transformation of Covariance Matrix or Data Matrix.
Machine Learning and Iot for Intelligent Systems and Smart Applications
The fusion of AI and IoT enables the systems to be predictive, prescriptive, and autonomous, and this convergence has evolved the nature of emerging applications from being assisted to augmented, and ultimately to autonomous intelligence. This book discusses algorithmic applications in the field of machine learning and IoT with pertinent applications.　 It further discusses challenges and future directions in the machine learning area and develops understanding of its role in technology, in terms of IoT security issues. Pertinent applications described include speech recognition, medical diagnosis, optimizations, predictions, and security aspects. Features: Focuses on algorithmic and practical parts of the artificial intelligence approaches in IoT applications. Discusses supervised and unsupervised machine learning for IoT data and devices. Presents an overview of the different algorithms related to Machine learning and IoT. Covers practical case studies on industrial and smart home automation. Includes implementation of AI from case studies in personal and industrial IoT. This book aims at Researchers and Graduate students in Computer Engineering, Networking Communications, Information Science Engineering, and Electrical Engineering.
Machine Learning with the Elastic Stack - Second Edition
Discover expert techniques for combining machine learning with the analytic capabilities of Elastic Stack and uncover actionable insights from your dataKey Features: Integrate machine learning with distributed search and analyticsPreprocess and analyze large volumes of search data effortlesslyOperationalize machine learning in a scalable, production-worthy wayBook Description: Elastic Stack, previously known as the ELK stack, is a log analysis solution that helps users ingest, process, and analyze search data effectively. With the addition of machine learning, a key commercial feature, the Elastic Stack makes this process even more efficient. This updated second edition of Machine Learning with the Elastic Stack provides a comprehensive overview of Elastic Stack's machine learning features for both time series data analysis as well as for classification, regression, and outlier detection.The book starts by explaining machine learning concepts in an intuitive way. You'll then perform time series analysis on different types of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you'll deploy machine learning within Elastic Stack for logging, security, and metrics. Finally, you'll discover how data frame analysis opens up a whole new set of use cases that machine learning can help you with.By the end of this Elastic Stack book, you'll have hands-on machine learning and Elastic Stack experience, along with the knowledge you need to incorporate machine learning in your distributed search and data analysis platform.What You Will Learn: Find out how to enable the ML commercial feature in the Elastic StackUnderstand how Elastic machine learning is used to detect different types of anomalies and make predictionsApply effective anomaly detection to IT operations, security analytics, and other use casesUtilize the results of Elastic ML in custom views, dashboards, and proactive alertingTrain and deploy supervised machine learning models for real-time inferenceDiscover various tips and tricks to get the most out of Elastic machine learningWho this book is for: If you're a data professional looking to gain insights into Elasticsearch data without having to rely on a machine learning specialist or custom development, then this Elastic Stack machine learning book is for you. You'll also find this book useful if you want to integrate machine learning with your observability, security, and analytics applications. Working knowledge of the Elastic Stack is needed to get the most out of this book.
Natural Language Processing and Information Systems
This book constitutes the refereed proceedings of the 26th International Conference on Applications of Natural Language to Information Systems, NLDB 2021, held online in July 2021. The 19 full papers and 14 short papers were carefully reviewed and selected from 82 submissions. The papers are organized in the following topical sections: role of learning; methodological approaches; semantic relations; classification; sentiment analysis; social media; linking documents; multimodality; applications.
Social, Cultural, and Behavioral Modeling
This book constitutes the proceedings of the 14th International Conference on Social, Cultural, and Behavioral Modeling, SBP-BRiMS 2021, which was held online during July 6-9, 2021.The 32 full papers presented in this volume were carefully reviewed and selected from 56 submissions. The papers were organized in topical sections as follows: COVID-related focus; methodologies; social cybersecurity and social networks; and human and agent modeling. They represent a wide number of disciplines including computer science, psychology, sociology, communication science, public health, bioinformatics, political science, and organizational science. Numerous types of computational methods are used including, but not limited to, machine learning, language technology, social network analysis and visualization, agent-based simulation, and statistics.
Communicating with Data Visualisation
How can you transform a spreadsheet of numbers into a clear, compelling story that your audience will want to pass on? This book is a step-by-step guide (honed through the authors′ Guardian masterclasses, workshops and seminars) to bringing data to life through visualisations, from static charts and maps to interactive infographics and motion graphics. Introducing a four-step framework to creating engaging and innovative visualisations, it helps you to: - Find the human stories in your datasets - Design a visual story that will resonate with your audience - Make a clear, persuasive visual that represents your data truthfully - Refine your work to ensure your visual expresses your story in the best possible way. This book also includes a portfolio of best-practice examples and annotated templates to help you choose the right visual for the right audience, and repurpose your work for different contexts.
Theoremus
A compact and easily accessible book, it guides the reader in unravelling the apparent mysteries found in doing mathematical proofs. Simply written, it introduces the art and science of proving mathematical theorems and propositions and equips students with the skill required to tackle the task of proving mathematical assertions. Theoremus - A Student's Guide to Mathematical Proofs is divided into two parts. Part 1 provides a grounding in the notion of mathematical assertions, arguments and fallacies and Part 2, presents lessons learned in action by applying them into the study of logic itself. The book supplies plenty of examples and figures, gives some historical background on personalities that gave rise to the topic and provides reflective problems to try and solve. The author aims to provide the reader with the confidence to take a deep dive into some more advanced work in mathematics or logic.
Mastering Tableau 2021- Third Edition
Build, design, and improve advanced business intelligence solutions using Tableau's latest features, including Tableau Prep Builder, Tableau Hyper, and Tableau ServerKey Features: Master new features in Tableau 2021 to solve real-world analytics challengesPerform geo-spatial, time series, and self-service analytics using real-life examplesBuild and publish dashboards and explore storytelling using Python and R integration supportBook Description: Tableau is one of the leading business intelligence (BI) tools used to solve data analysis challenges. With this book, you will master Tableau's features and offerings in various paradigms of the BI domain.Updated with fresh topics including Quick Level of Detail expressions, the newest Tableau Server features, Einstein Discovery, and more, this book covers essential Tableau concepts and advanced functionalities. Leveraging Tableau Hyper files and using Prep Builder, you'll be able to perform data preparation and handling easily. You'll gear up to perform complex joins, spatial joins, unions, and data blending tasks using practical examples. Following this, you'll learn how to execute data densification and further explore expert-level examples to help you with calculations, mapping, and visual design using Tableau extensions. You'll also learn about improving dashboard performance, connecting to Tableau Server and understanding data visualization with examples. Finally, you'll cover advanced use cases such as self-service analysis, time series analysis, and geo-spatial analysis, and connect Tableau to Python and R to implement programming functionalities within Tableau.By the end of this Tableau book, you'll have mastered the advanced offerings of Tableau 2021 and be able to tackle common and advanced challenges in the BI domain.What You Will Learn: Get up to speed with various Tableau componentsMaster data preparation techniques using Tableau Prep BuilderDiscover how to use Tableau to create a PowerPoint-like presentationUnderstand different Tableau visualization techniques and dashboard designsInteract with the Tableau server to understand its architecture and functionalitiesStudy advanced visualizations and dashboard creation techniquesBrush up on powerful self-service analytics, time series analytics, and geo-spatial analyticsWho this book is for: This book is designed for business analysts, business intelligence professionals and data analysts who want to master Tableau to solve a range of data science and business intelligence problems. The book is ideal if you have a good understanding of Tableau and want to take your skills to the next level.
Distributed, Ambient and Pervasive Interactions
This conference proceedings LNCS 12782 constitutes the refereed proceedings of the 9 th International Conference on Distributed, Ambient and Pervasive Interactions, DAPI 2021, held as part of the 23rd International Conference, HCI International 2021, which took place in July 2021. The conference was held virtually due to the COVID-19 pandemic.The total of 1276 papers and 241 posters included in the 39 HCII 2021 proceedings volumes was carefully reviewed and selected from 5222 submissions. The papers of DAPI 2021, Distributed, Ambient and Pervasive Interactions, are organized in topical sections named: Smart Cities; IoT, Sensors and Smart Environments; Learning and Culture in Intelligent Environments; Designing Intelligent Environments.
Business Modeling and Software Design
This book constitutes the refereed proceedings of the 11th International Symposium on Business Modeling and Software Design, BMSD 2021, which took place in Sofia, Bulgaria, in July 2021.The 14 full and 13 short papers included in this book were carefully reviewed and selected from a total of 61 submissions. BMSD is a leading international forum that brings together researchers and practitioners interested in business modeling and its relation to software design. Particular areas of interest are: Business Processes and Enterprise Engineering; Business Models and Requirements; Business Models and Services; Business Models and Software; Information Systems Architectures and Paradigms; Data Aspects in Business Modeling and Software Development; Blockchain-Based Business Models and Information Systems; IoT and Implications for Enterprise Information Systems. The BMSD 2021 theme was: Towards Enterprises and Software that are Resilient against Disruptive Events.
From Opinion Mining to Financial Argument Mining
Opinion mining is a prevalent research issue in many domains. In the financial domain, however, it is still in the early stages. Most of the researches on this topic only focus on the coarse-grained market sentiment analysis, i.e., 2-way classification for bullish/bearish. Thanks to the recent financial technology (FinTech) development, some interdisciplinary researchers start to involve in the in-depth analysis of investors' opinions. These works indicate the trend toward fine-grained opinion mining in the financial domain. When expressing opinions in finance, terms like bullish/bearish often spring to mind. However, the market sentiment of the financial instrument is just one type of opinion in the financial industry. Like other industries such as manufacturing and textiles, the financial industry also has a large number of products. Financial services are also a major business for many financial companies, especially in the context of the recent FinTech trend. For instance, many commercial banks focus on loans and credit cards. Although there are a variety of issues that could be explored in the financial domain, most researchers in the AI and NLP communities only focus on the market sentiment of the stock or foreign exchange. This open access book addresses several research issues that can broaden the research topics in the AI community. It also provides an overview of the status quo in fine-grained financial opinion mining to offer insights into the futures goals. For a better understanding of the past and the current research, it also discusses the components of financial opinions one-by-one with the related works and highlights some possible research avenues, providing a research agenda with both micro- and macro-views toward financial opinions.
The Once-Only Principle
This open access State-of-the-Art Survey describes and documents the developments and results of the Once-Only Principle Project (TOOP). The Once-Only Principle (OOP) is part of the seven underlying principles of the eGovernment Action Plan 2016-2020. It aims to make the government more effective and to reduce administrative burdens by asking citizens and companies to provide certain standard information to the public authorities only once.The project was horizontal and policy-driven with the aim of showing that the implementation of OOP in a cross-border and cross-sector setting is feasible. The book summarizes the results of the project from policy, organizational, architectural, and technical points of view.
Transactions on Large-Scale Data- And Knowledge-Centered Systems XLVIII
The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 48th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains 8 invited papers dedicated to the memory of Prof. Dr. Roland Wagner. The topics covered include distributed database systems, NewSQL, scalable transaction management, strong consistency, caches, data warehouse, ETL, reinforcement learning, stochastic approximation, multi-agent systems, ontology, model-driven development, organisational modelling, digital government, new institutional economics and data governance.
Business Intelligence
Decision support, information systems and NLP (full papers).- Part-of-Speech Tagging using Long Short Term Memory (LSTM): Amazigh text writ-ten in Tifinaghe characters.- Contribution to Arabic Text Classification using Machine Learning Techniques.- Analyzing Moroccan Tweets to Extract Sentiments Related to the Coronavirus Pandemic: A New Classification Approach.- Towards a support system for brainstorming based content-based information ex-traction and machine learning.- Classification of Documents using Machine Learning and Genetic Algorithms.- Toward Student Classification In Educational Video Courses Using Knowledge Tracing .- Assessment of lifestyle and mental health: case study of the FST Beni Mellal.- The search for digital information by evaluating four models.- Overview of the main recommendation approaches for the scientific articles.- Online students' classification based on the Formal Concepts analysis and multiple choice questions.- How BERT's dropout fine-tuning affects text classification.- Big data, Datamining, Web services & Web semantics (full papers).- Selection of Composite Web Services based on QoS.- A MapReduce Improved ID3 Decision Tree for Classifying Twitter Data.- Clustering techniques for Big Data Mining.- Data mining approach for intrusion detection.- Optimization and Decision support (full papers).- Markov Decision Processes with DiscountedRewards: New action elimination procedure.- Learning Management System comparison: new approach using Multi-Criteria Decision Making.- Finding agreements: Study and evaluation of heuristic approaches to multilateral negotiation.- Signal, Image and Vision Computing (full papers).- A new approach based on Steganography to face facial recognition vulnerabilities against Fake identities.- Mean Square Convergence of Reproducing Kernel For Channel Identification: Application to Bran D Channel Impulse Response.- Deep learning for medical image segmentation.- Networking, Cloud computing & Networking Architectures in Cloud (full papers).- Optimal Virtual Machine Provisioning in Cloud Computing Using Game Theory.- Comparative study between RFID readers anti-collision protocols in dense environments.- Game Theoretic Approaches to Mitigate Cloud Security Risks: An Initial Insight.- Comparative study on the McEliece Public-key Cryptosystem based on Goppa and QC-MDPC codes.- Optimization Of Leach Protocol For Saving Energie In Wireless Sensor Networks.- Big data, Datamining, Web services & Web semantics (poster papers).- Brain Cancer Ontology Construction.- Semantic Web for sharing medical resources.- Construction of glaucoma disease ontology.- Signal, Image and Vision Computing (poster papers).- Creation of a Callbot module for automatic processing of a customer service calls.- Applied CNN for automatic diabetic retinopathy assessment using Fundus Images.- RFID Based Security and Automatic Parking Access Control System.
Hands-On Kubernetes on Azure - Third Edition
Understand the fundamentals of Kubernetes deployment on Azure with a learn-by-doing approachKey Features: Get to grips with the fundamentals of containers and KubernetesDeploy containerized applications using the Kubernetes platformLearn how you can scale your workloads and secure your application running in Azure Kubernetes ServiceBook Description: Containers and Kubernetes containers facilitate cloud deployments and application development by enabling efficient versioning with improved security and portability.With updated chapters on role-based access control, pod identity, storing secrets, and network security in AKS, this third edition begins by introducing you to containers, Kubernetes, and Azure Kubernetes Service (AKS), and guides you through deploying an AKS cluster in different ways. You will then delve into the specifics of Kubernetes by deploying a sample guestbook application on AKS and installing complex Kubernetes apps using Helm. With the help of real-world examples, you'll also get to grips with scaling your applications and clusters.As you advance, you'll learn how to overcome common challenges in AKS and secure your applications with HTTPS. You will also learn how to secure your clusters and applications in a dedicated section on security. In the final section, you'll learn about advanced integrations, which give you the ability to create Azure databases and run serverless functions on AKS as well as the ability to integrate AKS with a continuous integration and continuous delivery (CI/CD) pipeline using GitHub Actions.By the end of this Kubernetes book, you will be proficient in deploying containerized workloads on Microsoft Azure with minimal management overhead.What You Will Learn: Plan, configure, and run containerized applications in production.Use Docker to build applications in containers and deploy them on Kubernetes.Monitor the AKS cluster and the application.Monitor your infrastructure and applications in Kubernetes using Azure Monitor.Secure your cluster and applications using azure-native security tools.Connect an app to the Azure database.Store your container images securely with Azure Container Registry.Install complex Kubernetes applications using Helm.Integrate Kubernetes with multiple Azure PaaS services, such as databases, Azure Security Center, and Functions.Use GitHub Actions to perform continuous integration and continuous delivery to your cluster.Who this book is for: If you are an aspiring DevOps professional, system administrator, developer, or site reliability engineer interested in learning how to get the most out of containers and Kubernetes, then this book is for you.
Closing the Analytics Talent Gap
How can we recruit out of your program? We have a project - how do we reach out to your students? If we do research together who owns it? We have employees who need to "upskill" in analytics - can you help me with that? How much does all of this cost? Managers and executives are increasingly asking university professors such questions as they deal with a critical shortage of skilled data analysts. At the same time, academics are asking such questions as: How can I bring a "real" analytical project in the classroom? How can I get "real" data to help my students develop the skills necessary to be a "data scientist? Is what I am teaching in the classroom aligned with the demands of the market for analytical talent? After spending several years answering almost daily e-mails and telephone calls from business managers asking for staffing help and aiding fellow academics with their analytics teaching needs, Dr. Jennifer Priestley of Kennesaw State University and Dr. Robert McGrath of the University of New Hampshire wrote Closing the Analytics Talent Gap: An Executive's Guide to Working with Universities. The book builds a bridge between university analytics programs and business organizations. It promotes a dialog that enables executives to learn how universities can help them find strategically important personnel and universities to learn how they can develop and educate this personnel. Organizations are facing previously unforeseen challenges related to the translation of massive amounts of data - structured and unstructured, static and in-motion, voice, text, and image - into information to solve current challenges and anticipate new ones. The advent of analytics and data science also presents universities with unforeseen challenges of providing learning through application. This book helps both organizations with finding "data natives" and universities with educating students to develop the facility to work in a multi-faceted and complex data environment. .　　
Logics in Artificial Intelligence
This book constitutes the proceedings of the 17th European Conference on Logics in Artificial Intelligence, JELIA 2021, held as a virtual event, in May 2021. The 27 full papers and 3 short papers included in this volume were carefully reviewed and selected from 68 submissions. The accepted papers span a number of areas within Logics in AI, including: argumentation; belief revision; reasoning about actions, causality, and change; constraint satisfaction; description logics and ontological reasoning; non-classical logics; and logic programming (answer set programming).
Information Refinement Technologies for Crisis Informatics
Marc-Andr矇 Kaufhold explores user expectations and design implications for the utilization of new media in crisis management and response. He develops a novel framework for information refinement, which integrates the event, organisational, societal, and technological perspectives of crises. Therefore, he reviews the state of the art on crisis informatics and empirically examines the use, potentials and barriers of both social media and mobile apps. Based on these insights, he designs and evaluates ICT concepts and artifacts with the aim to overcome the issues of information overload and quality in large-scale crises, concluding with practical and theoretical implications for technology adaptation and design.
Hands-On Data Analysis with Pandas - Second Edition
Get to grips with pandas by working with real datasets and master data discovery, data manipulation, data preparation, and handling data for analytical tasksKey FeaturesPerform efficient data analysis and manipulation tasks using pandas 1.xApply pandas to different real-world domains with the help of step-by-step examplesMake the most of pandas as an effective data exploration toolBook DescriptionExtracting valuable business insights is no longer a 'nice-to-have', but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time.This book will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn.Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data.This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making - valuable knowledge that can be applied across multiple domains.What you will learnUnderstand how data analysts and scientists gather and analyze dataPerform data analysis and data wrangling using PythonCombine, group, and aggregate data from multiple sourcesCreate data visualizations with pandas, matplotlib, and seabornApply machine learning algorithms to identify patterns and make predictionsUse Python data science libraries to analyze real-world datasetsSolve common data representation and analysis problems using pandasBuild Python scripts, modules, and packages for reusable analysis codeWho this book is forThis book is for data science beginners, data analysts, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. Data scientists looking to implement pandas in their machine learning workflow will also find plenty of valuable know-how as they progress.You'll find it easier to follow along with this book if you have a working knowledge of the Python programming language, but a Python crash-course tutorial is provided in the code bundle for anyone who needs a refresher.Table of ContentsIntroduction to Data AnalysisWorking with Pandas DataFramesData Wrangling with PandasAggregating Pandas DataFramesVisualizing Data with Pandas and MatplotlibPlotting with Seaborn and Customization TechniquesFinancial Analysis - Bitcoin and the Stock MarketRule-Based Anomaly DetectionGetting Started with Machine Learning in PythonMaking Better Predictions - Optimizing ModelsMachine Learning Anomaly DetectionThe Road Ahead
Responsible Data Science
Explore the most serious prevalent ethical issues in data science with this insightful new resource The increasing popularity of data science has resulted in numerous well-publicized cases of bias, injustice, and discrimination. The widespread deployment of "Black box" algorithms that are difficult or impossible to understand and explain, even for their developers, is a primary source of these unanticipated harms, making modern techniques and methods for manipulating large data sets seem sinister, even dangerous. When put in the hands of authoritarian governments, these algorithms have enabled suppression of political dissent and persecution of minorities. To prevent these harms, data scientists everywhere must come to understand how the algorithms that they build and deploy may harm certain groups or be unfair. Responsible Data Science delivers a comprehensive, practical treatment of how to implement data science solutions in an even-handed and ethical manner that minimizes the risk of undue harm to vulnerable members of society. Both data science practitioners and managers of analytics teams will learn how to: Improve model transparency, even for black box models Diagnose bias and unfairness within models using multiple metrics Audit projects to ensure fairness and minimize the possibility of unintended harm Perfect for data science practitioners, Responsible Data Science will also earn a spot on the bookshelves of technically inclined managers, software developers, and statisticians.
Hci in Games: Experience Design and Game Mechanics
This two-volume set LNCS 12789 and 12790 constitutes the refereed proceedings of the Third International Conference on HCI in Games, HCI-Games 2021, held as part of the 23rd International Conference, HCI International 2021, which took place in July 2021. Due to COVID-19 pandemic the conference was held virtually. The total of 1276 papers and 241 posters included in the 39 HCII 2021 proceedings volumes was carefully reviewed and selected from 5222 submissions. The papers of HCI-Games 2021, Part I, are organized in topical sections named: Experience Design in Games; User Engagement and Game Impact; Game Mechanics.
Contemporary Issues in Group Decision and Negotiation
This book constitutes the refereed proceedings of the 21st International Conference on Group Decision and Negotiation, GDN 2021, which was planned to be held in Toronto, ON, Canada, during June 6-10, 2021. The conference was held virtually due to the COVID-19 pandemic.The field of Group Decision and Negotiation focuses on decision processes with at least two participants and a common goal but conflicting individual goals. Research areas of Group Decision and Negotiation include electronic negotiations, experiments, the role of emotions in group decision and negotiations, preference elicitation and decision support for group decisions and negotiations, and conflict resolution principles. The 12 full papers presented in this volume were carefully reviewed and selected from 74 submissions. They were organized in topical sections as follows: pandemic responses; preference modeling for group decision and negotiation; conflict resolution; and collaborative decision making processes.
Advances in Enterprise Engineering XIV
This book constitutes the proceedings of the 10th Enterprise Engineering Working Conference, EEWC 2020, which was planned to take place in Bozen-Bolzano, Italy, and had to change to an online event due to the COVID 19 pandemic. The online event took place on September 28, 2020, October 19, 2020, and November 9-10, 2020.EEWC aims at addressing the challenges that modern and complex enterprises are facing in a rapidly changing world. The participants of the working conference share a belief that dealing with these challenges requires rigorous and scientific solutions, focusing on the design and engineering of enterprises. The goal of EEWC is to stimulate interaction between the different stakeholders, scientists as well as practitioners, interested in making Enterprise Engineering a reality. The 8 full papers and 2 short papers presented in this volume were carefully reviewed and selected from 23 submissions. The volume also contains 3 keynote papers in full paper length. Thepapers were organized in topical sections as follows: formal approaches and modeling; the DEMO modeling language; and enterprise engineering practice.
Digital Fluency
If you are curious about the basics of artificial intelligence, blockchain technology, and quantum computing as key enablers for digital transformation and innovation, Digital Fluency is your handy guide. The real-world applications of these cutting-edge technologies are expanding rapidly, and your daily life will continue to be affected by each of them. There is no better time than now to get started and become digitally fluent.You need not have previous knowledge of these versatile technologies, as author Volker Lang will expertly guide you through this digital age. He illustrates key concepts and applications in numerous practical examples and more than 48 catchy figures throughout Digital Fluency. The end of each chapter presents you with a helpful implementation checklist of central lessons before proceeding to the next. This book gets to the heart of digital buzzwords and concepts, and tells you what they truly mean. Breaking down topics such as automated driving and intelligent robotics powered by artificial intelligence, blockchain-based cryptocurrencies and smart contracts, drug development and optimization of financial investment portfolios by quantum computing, and more is imperative to being ready for what the future of industry holds. Whether your own digital transformation journey takes place within your private or public organization, your studies, or your individual household, Digital Fluency maps out a concrete digital action plan for all of your technology and innovation strategy needs.What You Will Learn Gain guidance in the digital age without requiring any previous knowledge about digital technologies and digital transformationGet acquainted with the most popular current and prospective applications of artificial intelligence, blockchain technology, and quantum computing across a wide range of industries including healthcare, financial services, and the automobile industryBecome familiar with the digital innovation models of Amazon, Google, Microsoft, IBM, and other world-leading organizations Implement your own digital transformation successfully along the eight core dimensions of a concrete digital action planWho This Book Is ForThought-leaders, business executives and industry strategists, management and strategy consultants, politicians and policy makers, entrepreneurs, financial analysts, investors and venture capitalists, students and research scientists, as well as general readers, who want to become digitally fluent.
Analysis of Images, Social Networks and Texts
This book constitutes revised selected papers from the 9th International Conference on Analysis of Images, Social Networks and Texts, AIST 2020, held during October 15-16, 2020. The conference was planned to take place in Moscow, Russia, but changed to an online format due to the COVID-19 pandemic.The 27 full papers and 4 short papers presented in this volume were carefully reviewed and selected from a total of 108 qualified submissions. The papers are organized in topical sections as follows: invited papers; natural language processing; computer vision; social network analysis; data analysis and machine learning; theoretical machine learning and optimization; and process mining.
