Expert systems / knowledge-based systems Books

Book SynopsisDesign for large-scale, high-performance queries using Snowflake's query processing engine to empower data consumers with timely, comprehensive, and secure access to data. This book also helps you protect your most valuable data assets using built-in security features such as end-to-end encryption for data at rest and in transit. It demonstrates key features in Snowflake and shows how to exploit those features to deliver a personalized experience to your customers. It also shows how to ingest the high volumes of both structured and unstructured data that are needed for game-changing business intelligence analysis.Mastering Snowflake Solutionsstarts with a refresher on Snowflake's unique architecture before getting into the advanced concepts that make Snowflake the market-leading product it is today. Progressing through each chapter, you will learn how to leverage storage, query processing, cloning, data sharing, and continuous data protection features. This approach allows for greater Table of Contents1. Snowflake Architecture2. Data Movement3. Cloning4. Managing Security and User Access Control 5. Protecting Data in Snowflake6. Business Continuity and Disaster Recovery7. Data Sharing and the Data Cloud8. Programming9. Advanced Performance Tuning10. Developing Applications in Snowflake

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Book SynopsisImplement the Snowflake Data Cloud using best practices and reap the benefits of scalability and low-cost from the industry-leading, cloud-based, data warehousing platform. This book provides a detailed how-to explanation, and assumes familiarity with Snowflake core concepts and principles. It is a project-oriented book with a hands-on approach to designing, developing, and implementing your Data Cloud with security at the center. As you work through the examples, you will develop the skill, knowledge, and expertise to expand your capability by incorporating additional Snowflake features, tools, and techniques. Your Snowflake Data Cloud will be fit for purpose, extensible, and at the forefront of both Direct Share, Data Exchange, and Snowflake Marketplace. Building the Snowflake Data Cloud helps you transform your organization into monetizing the value locked up within your data. As the digital economy takes hold, with data volume, velociTable of ContentsPart I. Context 1. The Snowflake Data Cloud 2. Breaking Data Siloes Part II. Concepts 3. Architecture 4. Account Security5. Role Based Access Control (RBAC)6. Account Usage StorePart III. Tools7. Ingesting Data8. Data Pipelines9. Data Presentation10. Semi Structured and Unstructured DataPart IV. Management11. Query Optimizer Basics12. Data Management13. Data Modelling14. Snowflake Data Cloud By Example

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Book SynopsisMaster the tricks and techniques of business analytics consulting, specifically applicable to small-to-medium businesses (SMEs). Written to help you hone your business analytics skills, this book applies data science techniques to help solve problems and improve upon many aspects of a business'' operations. SMEs are looking for ways to use data science and analytics, and this need is becoming increasingly pressing with the ongoing digital revolution. The topics covered in the books will help to provide the knowledge leverage needed for implementing data science in small business. The demand of small business for data analytics are in conjunction with the growing number of freelance data science consulting opportunities; hence this book will provide insight on how to navigate this new terrain. This book uses a do-it-yourself approach to analytics and introduces tools that are easily available online and are non-programming based. Data science Trade Review“By reading the book and working out the use case, subject matter experts will be able to get a coherent roadmap to the main techniques available for both descriptive and predictive data analytics, as well as be able to provide simple services related to their company data and future prospects.” (Rosario Uceda-Sosa, Computing Reviews, October 2, 2023)Table of Contents​ INTRODUCTIONWe introduce data science generally and narrow it down to data science for business which is also referred to as business analytics. We then give a detailed explanation of the process involved in business analytics in form of the business analytics journey. In this journey, we explain what it takes from start to finish to carry out an analytics project in the business world, focusing on small business consulting, even though the process is generic to all types of business, small or large. We also give a description of what small business refers to in this book and the peculiarities of navigating an analytics project in such a terrain. To conclude the chapter, we talk about the types of analytics problems that is common to small business and the tools available to solve these problems given the budget situation of small businesses when it comes to analytics project.· DATA SCIENCE· DATA SCIENCE FOR BUSINESS· BUSINESS ANALYTICS JOURNEY· SMALL AND MEDIUM BUSINESS (SME)· BUSINESS ANALYTICS IN SMALL BUSINESS· TYPES OF ANALYTICS PROBLEMS IN SME· ANALYTICS TOOLS FOR SMES· ROAD MAPS TO THIS BOOK· PROBLEMS· REFERENCES CHAPTER 1: DATA FOR ANALYSIS IN SMALL BUSINESSIn this chapter, we would look at the various sources of data generally and in small business. This chapter is important because the major challenge of consulting for small business is the lack of data or quality data for analysis. This chapter will therefore detail the sources of data for analysis explaining first the type or form that data exists and some general ideas of how to collect such data. It gives an overview on data quality and integrity issues and touches on data literacy. The chapter also includes the typical data preparation procedures for the common types of techniques used in small business analytics and by extension used in this book. To conclude the chapter, we look at data visualization, particularly towards preparing data for various analytics task as explained in section 1.3.· SOURCE OF DATA· DATA QUALITY & INTEGRITY· DATA GOVERNANCE· DATA PREPARATION· DATA VISUALIZATION· PROBLEMS· REFERENCESCHAPTER 2: BUSINESS ANALYTICS CONSULTINGIn this chapter, we will look at business analytics consulting, particularly what the concept implies and how to build such a career path. We will explain the types of business analytics consulting that exist and then narrow it down to how to navigate the world of business analytics consulting for small business. In this chapter, we will look at how to manage a typical analytics project and measure the success of analytics projects. In conclusion, we will discuss issues revolving around how to bill analytics project particularly as a consultant.· BUSINESS ANALYTICS CONSULTING· MANAGING ANALYTICS PROJECT· SUCCESS METRICS IN ANALYTICS PROJECT· BILLING ANALYTICS PROJECT· PROBLEMS· REFERENCESCHAPTER 3: BUSINESS ANALYTICS CONSULTING PHASESIn this chapter we will look at the stages involved business analytics consulting, particularly when the analytics service is offered as a product from either within or outside the business. We will look at the proposal and initial analysis stage which gives direction to the analytics project. Then we look at the details involved in the pre-engagement, engagement and post engagement phase. It is important to know that the stages are presented in a typical or generic way but when implemented, there might be reason to modify or customize them for the application scenario.· PROPOSAL & INITIAL ANALYSIS· PRE- ENGAGEMENT PHASE· ENGAGEMENT PHASE· POST ENGAGEMENT PHASE· PROBLEMS· REFERENCES CHAPTER 4: DESCRIPTIVE ANALYTICS TOOLSThis chapter is focused on the mostly common descriptive analytics tools used in business generally and specifically in small businesses. The chapter will help to use descriptive analytics tools to understand your business and make recommendations that can improve your business profits. For small business, descriptive analytics helps SMEs to make sense of available data in order to monitor business indicators at a glance, helps SME owners to observe sales trends and patterns on an overall basis, as well as deep-dive into product categories and customer groups. It also helps SME’s to plan product strategy, pricing policies that will maximize their projected revenues and derive a lot of valuable insights for getting more customers. · INTRODUCTION· BAR CHART· HISTOGRAM· LINE GRAPHS· SCATTER PLOTS· PACKED BUBBLES CHARTS· HEAT MAPS· GEOGRAPHICAL MAPS· A PRACTICAL BUSINESS PROBLEM I· PROBLEMS· REFERENCES CHAPTER 5: PREDICTION TECHNIQUESIn this chapter, we will explore the popular techniques used for prediction, particularly in retails business. The approach used in explaining these techniques us to use them in solving a business problem. The second business problem to be addressed is the sales prediction problem which is common in retail business. The chapter first explain the fundamental concept of prediction techniques, next we look at how such techniques are evaluated. After this, we describe the business problem we intend solving. We then pick each of the selected techniques one by one and explain the algorithms involved and how they can be used to solve the problem described. The prediction techniques used and compared are the Multiple linear regression, the Regression Trees and the Neural Network. To conclude the chapter, we compare the results of the three algorithms and conclude on the problem in question. In this chapter therefore, the analytics products being offered is to solve sales prediction problem for small retail business.· INTRODUCTION· PRACTICAL BUSINESS PROBLEM II (SALES PREDICTION)· MULTIPLE LINEAR REGRESSION· REGRESSIN TREES· NEURAL NETWORK (PREDICTION)· CONCLUSION ON SALES PREDICTION· PROBLEMS· REFERENCES CHAPTER 6: CLASSIFICATION TECHNIQUESIn this chapter, even though there are several classification techniques, we will explore the popular ones used for classification in the business domain. In doing this, we will use the third business problem centered on customer loyalty comparing neural network, classification tree and random forest algorithms. In solving this problem, we are particular about how to get and retain more customers for our small business. We will also introduce some other classification based techniques such as K-nearest neighbour logistic regression and persuasion modelling. We will use persuasion modelling for the fourth practical business problem. In using these techniques to solve the problem we explain the fundamental concepts in the chosen algorithms and use them to demonstrate how this problems solving process can be adopted in real business scenarios.· CLASSIFICATION MODELS & EVALUATION· PRACTICAL BUSINESS PROBLEM III (CUSTOMER LOYALTY)· NEURAL NETWORK· CLASSIFICATION TREE· RANDOM FOREST & BOOSTED TREES· K NEAREST NEIGHBOUR· LOGISTIC REGRESSION· PROBLEMS· REFERENCES CHAPTER 7: ADVANCED DESCRIPTIVE ANALYTICSThis chapter is focused mainly on advanced descriptive analytics techniques. In this chapter, we will first explain the concept of clustering which is a type of unsupervised learning approach. We will then pick one clustering technique which is the K means clustering. Using the fourth practical business problem, we will explain how we can use the K means clustering technique to solve a real business problem. Next will explain the association rule example and finally Network analysis. We conclude with the fifth business problem which is focused on using network analytics for employee efficiency.· CLUSTERING· K MEANS· PRACTICAL BUSINESS PROBLEM IV (Customer Segmentation)· ASSOCIATION ANALYSIS· NETWORK ANALYSIS· PRACTICAL BUSINESS PROBLEM V (Staff Efficiency)· PROBLEMS· REFERENCES CHAPTER 8: CASE STUDY PART IThis chapter is the beginning part of major consulting case study for this book. We will explain what transpired during a typical business analytics consulting and help to create a road map or an example of how to navigate a business analytics consulting project. We start with a description of the SME Ecommerce environment generally, since this is the business environment of our selected case study, we then talk about the sources of data for analytics peculiar this environment. Next we describe the business to be used as case study briefly, followed by the analytics road map peculiar to consulting for this business. This chapter ends with the results of the initial analysis and pre engagement phase which forms the bases for the detailed analytics and implementation phase in chapter 10.· SME ECORMERCE· INTRODUCTION TO SME CASE STUDY· INITIAL ANALYSIS· ANALYTICS APPROACH · PRE –ENGAGEMENT· PROBLEMS· REFERENCES CHAPTER 9: CASE STUDY PART IIIn this chapter, we will conclude the case study used for illustration of a typical business analytics consulting for an SME by presenting the details of the engagement phase for the case study in question. The post engagement phase is left out as the implementation of the recommendations is determined by the systems and procedures of the business. It is important to note that the consulting steps can be customized for any small business based on the intended problem. The whole steps described in chapter 9 and 10 have been made simple for understanding, though in real life business application there might be need to iterate the process until satisfactory results have been gotten. This is because you constantly need to incorporate feedback from the stakeholders and domain experts.· GOAL 1: INCREASE WEBSITE TRAFFIC· GOAL 2: INCREASE WEBSITE SALES REVENUE· PROBLEMS· REFERENCES

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Book SynopsisThis book is your practical and comprehensive guide to learning Google Cloud Platform (GCP) for data science, using only the free tier services offered by the platform. Data science and machine learning are increasingly becoming critical to businesses of all sizes, and the cloud provides a powerful platform for these applications. GCP offers a range of data science services that can be used to store, process, and analyze large datasets, and train and deploy machine learning models. The book is organized into seven chapters covering various topics such as GCP account setup, Google Colaboratory, Big Data and Machine Learning, Data Visualization and Business Intelligence, Data Processing and Transformation, Data Analytics and Storage, and Advanced Topics. Each chapter provides step-by-step instructions and examples illustrating how to use GCP services for data science and big data projects. Readers will learn how to set up a Google Colaboratory account and run Jupyternotebooks, access GCP services and data from Colaboratory, use BigQuery for data analytics, and deploy machine learning models using Vertex AI. The book also covers how to visualize data using Looker Data Studio, run data processing pipelines using Google Cloud Dataflow and Dataprep, and store data using Google Cloud Storage and SQL. What You Will LearnSet up a GCP account and projectExplore BigQuery and its use cases, including machine learningUnderstand Google Cloud AI Platform and its capabilities Use Vertex AI for training and deploying machine learning modelsExplore Google Cloud Dataproc and its use cases for big data processingCreate and share data visualizations and reports with Looker Data StudioExplore Google Cloud Dataflow and its use cases for batch and stream data processing Run data processing pipelines on Cloud DataflowExplore Google Cloud Storageand its use cases for data storage Get an introduction to Google Cloud SQL and its use cases for relational databases Get an introduction to Google Cloud Pub/Sub and its use cases for real-time data streamingWho This Book Is ForData scientists, machine learning engineers, and analysts who want to learn how to use Google Cloud Platform (GCP) for their data science and big data projectsTable of ContentsChapter 1: Introduction to GCP.- Chapter 2: Google Colaboratory.- Chapter 3: Big Data and Machine Learning.- Chapter 4: Data Visualization and Business Intelligence.- Chapter 5: Data Processing and Transformation.- Chapter 6: Data Analytics and Storage.- Chapter 7: Advanced Topics.

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Book SynopsisThe disciplines of accounting and finance have been rapidly changing in recent years. The methods and techniques now being used have created a new language for managers, students, practitioners, academics and all those who are connected in some way with business and investment activities. To understand and work within an environment that is in a constant state of flux can be challenging and this book provides a resource of information and guidance.The Guide focuses specifically on the terms used in accounting and finance. Important terms and phrases are identified but with a much longer, in-depth explanation than you would normally find in a dictionary. Not only does each entry gives a thorough explanation of each term, most entries provide two or more references to academic articles that go into much greater depth. Hence, the entries give the reader immediate access to the literature.The Guide also comments on the contribution of the articles which adds to our knowledge. This approach allows the reader to obtain a much deeper level of understanding much more quickly than is available from the usual dictionary. At the end of the book, the full reference to all the articles that have been cited in the text is given including a list of the many acronyms used in the world of accounting and finance.

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Book SynopsisEnterprise Systems have been used for many years to integrate technology with the management of an organization but rapid technological disruptions are now creating new challenges and opportunities that require urgent consideration. This book reappraises the implementation and management of Enterprise Systems in the digital age and investigates the vital link between business processes, information technology and the Internet for an organization’s competitive advantage and success.This book primarily focuses on the implementation, operation, management and integration of Enterprise Systems with fastemerging disruptive technologies such as blockchains, big data, cryptocurrencies, artificial intelligence, cloud computing, data mining and data analytics. These disruptive technologies are now becoming mainstream and the book proposes several innovations that organizations need to adopt to remain competitive within this rapidly changing landscape. In addition, it examines Enterprise Systems, their components, architecture, and applications and enlightens readers on the benefits and shortcomings of implementing them. This book contains primary research on organizations, case studies, and benchmarks ERP implementation against international best practice.

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Book SynopsisEnterprise Systems have been used for many years to integrate technology with the management of an organization but rapid technological disruptions are now creating new challenges and opportunities that require urgent consideration. This book reappraises the implementation and management of Enterprise Systems in the digital age and investigates the vital link between business processes, information technology and the Internet for an organization’s competitive advantage and success.This book primarily focuses on the implementation, operation, management and integration of Enterprise Systems with fastemerging disruptive technologies such as blockchains, big data, cryptocurrencies, artificial intelligence, cloud computing, data mining and data analytics. These disruptive technologies are now becoming mainstream and the book proposes several innovations that organizations need to adopt to remain competitive within this rapidly changing landscape. In addition, it examines Enterprise Systems, their components, architecture, and applications and enlightens readers on the benefits and shortcomings of implementing them. This book contains primary research on organizations, case studies, and benchmarks ERP implementation against international best practice.

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Book SynopsisAcclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies. If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the customer relationship, with the main objective to maximize potential sales. On the transdisciplinary approach, engines and recommender systems brings together contributions linking information science and communications, marketing, sociology, mathematics and computing. It deals with the understanding of the underlying models for recommender systems and describes their historical perspective. It also analyzes their development in the content offerings and assesses their impact on user behavior.Table of ContentsPREFACE xi Gérald KEMBELLEC, Ghislaine CHARTRON and Imad SALEH CHAPTER 1. GENERAL INTRODUCTION TO RECOMMENDER SYSTEMS 1 Ghislaine CHARTRON and Gérald KEMBELLEC 1.1. Putting it into perspective 1 1.2. An interdisciplinary subject 2 1.3. The fundamentals of algorithms 4 1.3.1. Collaborative filtering 4 1.3.2. Content filtering 7 1.3.3. Hybrid methods 9 1.3.4. Conclusion on historical recommendation models 11 1.4. Content offers and recommender systems 11 1.4.1. Culture and recommender systems 11 1.4.2. Recommender systems and the e-commerce of content 16 1.4.3. The behavior of users 18 1.5. Current issues 19 1.6. Bibliography 19 CHAPTER 2. UNDERSTANDING USERS’ EXPECTATIONS FOR RECOMMENDER SYSTEMS: THE CASE OF SOCIAL MEDIA 25 Jean-Claude DOMENGET and Alexandre COUTANT 2.1. Introduction: the omnipresence of recommender systems 25 2.2. The social approach to prescription 27 2.2.1. The theory of the prescription and online interactions 27 2.2.2. Conditions for recognition of the prescription 29 2.2.3. The specificities of social media 30 2.3. Users who do not focus on the prescriptions of platforms 31 2.3.1. Facebook: the link, the type of activity and the context 32 2.3.2. Twitter: prescription between peers and explanation of prescription 38 2.3.3. Conditions for the recognition of a prescription: announcement and enunciation 44 2.4. A guide for considering recommender systems adapted to different forms of social media 45 2.5. Conclusion 48 2.6. Bibliography 49 CHAPTER 3. RECOMMENDER SYSTEMS AND SOCIAL NETWORKS: WHAT ARE THE IMPLICATIONS FOR DIGITAL MARKETING? 53 Maria MERCANTI-GUÉRIN 3.1. Social recommendations: an ancient practice revived by the digital age 54 3.1.1. Recommendations: a difficult management for brands 55 3.1.2. Internet recommendations: social presence and personalized recommendations 55 3.2. Social recommendations: how are they used for e-commerce? 58 3.2.1. Efficiency of recommender systems with regard to the performance of e-commerce websites 58 3.2.2. Recommender systems used by social networks: from e-commerce to social commerce 59 3.3. Conclusion 66 3.4. Bibliography 68 CHAPTER 4. RECOMMENDER SYSTEMS AND DIVERSITY: TAKING ADVANTAGE OF THE LONG TAIL AND THE DIVERSITY OF RECOMMENDATION LISTS 71 Muriel FOULONNEAU, Valentin GROUÈS, Yannick NAUDET and Max CHEVALIER 4.1. The stakes associated with diversity within recommender systems 72 4.1.1. Individual diversity or the individual perception of diversity 73 4.1.2. The stakes and impacts of aggregate diversity 74 4.2. Recommendation algorithms and diversity: trends, evaluation and optimization 77 4.2.1. The tendency for recommendation algorithms to focus on the head 78 4.2.2. The evaluation of diversity in recommender systems 80 4.2.3. Recommendation algorithms which favor individual diversity 81 4.2.4. Recommendation algorithms which favor aggregate diversity 81 4.2.5. The shift toward user-centered diversity approaches 82 4.3. Conclusion and new directions 85 4.4. Bibliography 87 CHAPTER 5. ISONTRE: INTELLIGENT TRANSFORMER OF SOCIAL NETWORKS INTO A RECOMMENDATION ENGINE ENVIRONMENT 93 Rana CHAMSI ABU QUBA, Salima HASSAS, Usama FAYYAD, Hammam CHAMSI and Christine GERTOSIO 5.1. Summary 93 5.2. Introduction 94 5.3. Latest developments, definition and history 97 5.3.1. Collaborative filtering techniques 97 5.3.2. General use social networks: what do they contain? 97 5.3.3. Social recommendation 99 5.3.4. The recommendation of concepts 100 5.4. iSoNTRE 101 5.4.1. iSoNTRE: transformer of social networks 102 5.4.2. iSoNTRE: the core of recommendation 107 5.5. Experiments 110 5.5.1. The preparation of data 110 5.5.2. Testing methodology 110 5.5.3. The creation of avatars 111 5.5.4. Results 112 5.5.5. Discussion 113 5.6. Conclusion 114 5.7. Bibliography 115 CHAPTER 6. A TWO-LEVEL RECOMMENDATION APPROACH FOR DOCUMENT SEARCH 119 Manel HMIMIDA and Rushed KANAWATI 6.1. Introduction 119 6.2. Tag recommendation: a brief state of the art 120 6.3. The hypertagging system 122 6.3.1. Metadata 122 6.3.2. Architecture 123 6.4. Recommendation approach 124 6.4.1. Presentation 124 6.4.2. Recommendation algorithm 126 6.5. Evaluation 127 6.5.1. Generation of facets 127 6.5.2. Generation of association rules 129 6.5.3. Evaluation of recommendation rules 130 6.6. Conclusion 131 6.7. Bibliography 132 CHAPTER 7. COMBINING CONFIGURATION AND RECOMMENDATION TO ENABLE AN INTERACTIVE GUIDANCE OF PRODUCT LINE CONFIGURATION 135 Raouia TRIKI , Raúl MAZO and Camille SALINESI 7.1. Introduction 135 7.2. Context 137 7.2.1. Configuration 137 7.2.2. Recommendation 139 7.2.3. Obstacles and challenges of interactive PL configuration 141 7.3. Overview of the proposed approach 142 7.4. Preliminary evaluation 148 7.5. Discussion and related work 148 7.5.1. Recommendation techniques 148 7.6. Conclusion and future work 151 7.7. Bibliography 151 CHAPTER 8. SEMIO-COGNITIVE SPACES: THE FRONTIER OF RECOMMENDER SYSTEMS 157 Hakim HACHOUR, Samuel SZONIECKY and Safia ABOUAD 8.1. Introduction 157 8.2. Latest developments: finalized activities, recommender systems and the relevance of information 159 8.2.1. Cognitive dynamics of finalized activities 159 8.2.2. The foundations of recommender systems 161 8.2.3. What information relevance? 166 8.3. Observable interests for decision theory: a combination of content-based, collaboration based and knowledge-based recommendations 169 8.3.1. Methodology: meta-analysis and modeling of the process 169 8.3.2. Analysis and modeling of a macro-process for responding to a call for R&D projects 171 8.3.3. Analysis and model of a socio-organizational tool for the management of customer complaints 173 8.4. Discussion and conclusions 177 8.4.1. Discussion: the performance of the filtering methods and semio-cognitive criteria for relevance 177 8.5. Conclusions: recommender systems linked to finalized activities 181 8.5.1. The localization of activities and geographical information systems: a new kind of data 182 8.5.2. Transparency of the use of personal data, data protection and ownership 183 8.6. Acknowledgments 185 8.7. Bibliography 185 CHAPTER 9. THE FRENCH-SPEAKING LITERARY PRESCRIPTION MARKET IN NETWORKS 191 Louis WIART 9.1. Introduction 191 9.2. The economy of prescription 193 9.2.1. The notion of prescription 193 9.2.2. From the advisors market to the prescription market 194 9.3. Methodology 196 9.4. The competitive structure of the market of online social networks of readers 197 9.4.1. Pure player networks and the audience strategy 199 9.4.2. Amateur networks and the survival strategy 201 9.4.3. Backed networks and the hybridization strategy 202 9.5. The organization of prescription 204 9.5.1. Social prescription 205 9.5.2. Editorial prescription 206 9.5.3. Algorithmic prescription 207 9.6. Conclusion: what legitimacy for literary prescription? 208 9.7. Appendix: list of interviews undertaken 210 9.8. Bibliography 210 CHAPTER 10. PRESENTATION OF OFFERED SERVICES: BABELIO, A RECOMMENDATION ENGINE DEDICATED TO BOOKS 213 Vassil STEFANOV, Guillaume TEISSEIRE and Pierre FRÉMAUX 10.1. Introduction 213 10.2. The problem of qualitative pertinence 216 10.3. The problem of quantitative pertinence 217 10.4. Balancing recall and precision 217 10.5. The issue of sparse data 218 10.6. Performance and scalability 218 10.7. A few issues specific to books 219 CHAPTER 11. PRESENTATION OF THE OFFER OF SERVICES: NOMAO, RECOMMENDER SYSTEMS AND INFORMATION SEARCH 221 Estelle DELPECH, Laurent CANDILLIER and Étienne CHAI 11.1. Introduction: the actors of Internet recommendation 221 11.2. Approaches to recommendation 222 11.3. Nomao: a local outlets search and recommendation engine 223 11.3.1. Popularity score 223 11.3.2. Affinity score 224 11.3.3. Social recommendation 225 11.4. Prospects: the move toward interactive recommender systems 225 11.5. Appendix 226 LIST OF AUTHORS 227 INDEX 231

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Book SynopsisThis book describes methods and tools that empower information providers to build and maintain knowledge graphs, including those for manual, semi-automatic, and automatic construction; implementation; and validation and verification of semantic annotations and their integration into knowledge graphs. It also presents lifecycle-based approaches for semi-automatic and automatic curation of these graphs, such as approaches for assessment, error correction, and enrichment of knowledge graphs with other static and dynamic resources.Chapter 1 defines knowledge graphs, focusing on the impact of various approaches rather than mathematical precision. Chapter 2 details how knowledge graphs are built, implemented, maintained, and deployed. Chapter 3 then introduces relevant application layers that can be built on top of such knowledge graphs, and explains how inference can be used to define views on such graphs, making it a useful resource for open and service-oriented dialog systems. Chapter 4 discusses applications of knowledge graph technologies for e-tourism and use cases for other verticals. Lastly, Chapter 5 provides a summary and sketches directions for future work. The additional appendix introduces an abstract syntax and semantics for domain specifications that are used to adapt schema.org to specific domains and tasks.To illustrate the practical use of the approaches presented, the book discusses several pilots with a focus on conversational interfaces, describing how to exploit knowledge graphs for e-marketing and e-commerce. It is intended for advanced professionals and researchers requiring a brief introduction to knowledge graphs and their implementation. Table of ContentsIntroduction: What is a Knowledge Graph?.- How to build a Knowledge Graph.- How to use a Knowledge Graph.- Why we need Knowledge Graphs: Applications.- Conclusions.- References.- Appendix.- Index.

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Book SynopsisThis 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. Table of Contents

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Book SynopsisThis book introduces the Internet access for vehicles as well as novel communication and computing paradigms based on the Internet of vehicles. To enable efficient and reliable Internet connection for mobile vehicle users, this book first introduces analytical modelling methods for the practical vehicle-to-roadside (V2R) Internet access procedure, and employ the interworking of V2R and vehicle-to-vehicle (V2V) to improve the network performance for a variety of automotive applications. In addition, the wireless link performance between a vehicle and an Internet access station is investigated, and a machine learning based algorithm is proposed to improve the link throughout by selecting an efficient modulation and coding scheme.This book also investigates the distributed machine learning algorithms over the Internet access of vehicles. A novel broadcasting scheme is designed to intelligently adjust the training users that are involved in the iteration rounds for an asynchronous federated learning scheme, which is shown to greatly improve the training efficiency. This book conducts the fully asynchronous machine learning evaluations among vehicle users that can utilize the opportunistic V2R communication to train machine learning models. Researchers and advanced-level students who focus on vehicular networks, industrial entities for internet of vehicles providers, government agencies target on transportation system and road management will find this book useful as reference. Network device manufacturers and network operators will also want to purchase this book. Table of ContentsOverview of Internet Access of Vehicular Networks.- Internet Access Modeling of Vehicular Internet Access.- V2X Interworking via Vehicular Internet Access.- Intelligent Link Management for Vehicular Internet Access.- Intelligent Networking enabled Vehicular Distributed Learning.- Conclusion and Future Works.

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Book SynopsisThis text highlights the difference between analytics and data science, using predictive analytic techniques to analyze different historical data, including aviation data and concrete data, interpreting the predictive models, and highlighting the steps to deploy the models and the steps ahead. The book combines the conceptual perspective and a hands-on approach to predictive analytics using SAS VIYA, an analytic and data management platform. The authors use SAS VIYA to focus on analytics to solve problems, highlight how analytics is applied in the airline and business environment, and compare several different modeling techniques. They decipher complex algorithms to demonstrate how they can be applied and explained within improving decisions.Table of ContentsChapter 1. Introduction to Analytics and Data Science. Chapter 2. Data Types Structure & Data Preparation Process. Chapter 3. Data Exploration and Data Visualization. Chapter 4. Evaluating Predictive Performance. Chapter 5. Decision Trees & Ensemble. Chapter 6. Regression Models. Chapter 7. Neural Networks. Chapter 8. Model Deployment.

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Book SynopsisThis book constitutes the refereed proceedings of the 6th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held during September 13-17, 2021. The workshop was planned to take place in Bilbao, Spain, but was held virtually due to the COVID-19 pandemic. The 12 full papers presented in this book were carefully reviewed and selected from 21 submissions. They focus on the following topics: Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Multivariate Time Series Co-clustering; Efficient Event Detection; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Cluster-based Forecasting; Explanation Methods for Time Series Classification; Multimodal Meta-Learning for Time Series Regression; and Multivariate Time Series Anomaly Detection. Table of ContentsOral Presentation.- Ranking by Aggregating Referees: Evaluating the Informativeness of Explanation Methods for Time Series Classification.- State Space approximation of Gaussian Processes for time-series forecasting.- Fast Channel Selection for Scalable Multivariate Time Series Classification.- Temporal phenotyping for characterisation of hospital care pathways of COVID patients.- A New Multivariate Time Series Co-clustering Non-Parametric Model Applied to Driving-Assistance Systems Validation.- TRAMESINO: Trainable Memory System for Intelligent Optimization of Road Traffic Control.- Detection of critical events in renewable energy production time series.- Poster Presentation.- Multimodal Meta-Learning for Time Series Regression.- Cluster-based Forecasting for Intermittent and Non-intermittent Time Series.- State discovery and prediction from multivariate sensor data.- RevDet: Robust and Memory Efficient Event Detection and Tracking in Large News Feeds.- From Univariate to Multivariate Time Series Anomaly Detection with Non-Local Information.

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Book SynopsisThis book bridges the gap between knowledge management and technology. It embraces the complete lifecycle of knowledge, information, and data from how knowledge flows through an organization to how end users want to handle it and experience it. Whether your intent is to design and implement a single technology or a complete collection of KM systems, this book provides the foundations necessary for success. It will help you understand your organization’s needs and opportunities, strategize and prioritize features and functions, design with the end user in mind, and finally build a system that your users will embrace and which will realize meaningful business value for your organization. The book is the culmination of the authors’ collective careers, a combined sixty years of experience doing exactly what is detailed in this book. Their guidance has been honed by their own successes and failures as well as many others they have researched in order to provide a comprehensive study on KM transformations and the technologies that help to enable them. They have successfully applied this knowledge as the founders and leaders of the world’s largest dedicated knowledge management consultancy, which runs these projects for many of the world’s most complex organizations. They are writing as practitioners directly to other practitioners with the intent to enable them to apply and benefit from their knowledge and experience.“Compelling reading for KM practitioners looking to ensure their technology decisions support their business and organizational objectives.” - Margot Brown, Director of Knowledge Management, World Bank Group "We are two years into our KM Transformation and if I’d had this book beforehand, it would have made the journey smoother and faster! This is a great playbook for how to plan, organize, and execute a KM transformation." - Stephanie Hill, Senior Director, Global Customer Services, PayPalTrade Review“This book … spans the crevasse between KM and IT and does so with considerable flair. … this is a very good overview of the importance of integrating KM and IT and should be on the desktop of all KM managers, especially in larger organisations with complex IT infrastructures. The experience of the authors is evident throughout and they write in an engaging style which makes for a very readable book.” (Martin White, intranetfocus.com, June 30, 2022)Table of Contents1. Knowledge Management Primer.- Part I: Knowledge Management Transformation Strategy and Planning.- 2. Assessing Your Organization’s KM Strengths and Weaknesses (Current State).- 3. Understanding Your Organization’s Future KM Needs (Target State).- 4. Creating the Target State Vision.- 5. Getting from Here to There (KM Transformation Roadmap).- Part II: Understanding KM Systems.- 6. Content Management Solutions.- 7. Collaboration Suites.- 8. Learning Management Systems.- 9. Enterprise Search.- 10. Taxonomy Management.- 11. Data Catalogs and Governance Tools.- 12. Text Analytics Tools.- 13. Graph Databases.- 14. KM as a Foundation for Enterprise Artificial Intelligence.- 15. Integration Patterns for KM Systems.- Part III: Running a KM Systems Project.- 16. Project Phases.- 17. Common KMS Project Challenges and Mistakes.- 18. Foundational Design Elements.- 19. Content.- 20. Operations and Iterative Improvements.- 21. Envisioning Success: Putting KM Solutions and Outcomes Together.

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Book SynopsisThis second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant focus is placed on topics like transformers, pre-trained language models, knowledge graphs, and question answering.Table of Contents1 An Introduction to Text Analytics.- 2 Text Preparation and Similarity Computation.- 3 Matrix Factorization and Topic Modeling.- 4 Text Clustering.- 5 Text Classification: Basic Models.- 6 Linear Models for Classification and Regression.- 7 Classifier Performance and Evaluation.- 8 Joint Text Mining with Heterogeneous Data.- 9 Information Retrieval and Search Engines.- 10 Language Modeling and Deep Learning.- 11 Attention Mechanisms and Transformers.- 12 Text Summarization.- 13 Information Extraction and Knowledge Graphs.- 14 Question Answering.- 15 Opinion Mining and Sentiment Analysis.- 16 Text Segmentation and Event Detection.

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Book SynopsisThis book constitutes the proceedings of the 7th International Conference on Business Intelligence, CBI 2022, which took place in Khouribga, Morocco, during May 26-28, 2022. The 23 full papers included in this book were carefully reviewed and selected from a total of 68 submissions. They were organized in topical sections as follows: decision support and artificial intelligence; business intelligence and database; and optimization and dynamic programming.Table of ContentsDecision Support and Artificial Intelligence.- Optimization Focused On Parallel Fuzzy Deep Belief Neural Network For Opinion Mining.- A Convolutional Neural Networks-Based Approach For Potato Disease Classification.- Performance Investigation of a Proposed CBIR Search Engine Using Deep Convolu-tional Neural Networks.- Decision Boundary to improve the sensitivity of deep neural networks models. - Facial Expression Recognition Using a Hybrid ViT-CNN Aggregator.- Machine Learning Approach to Automate Decision Support on Information System Attacks.- Deep Reinforcement Learning for Bitcoin Trading.- An exploration of student grade retention prediction using machine learning algorithms.- Deep Learning Model For Educational Recommender Systems.- Comparative Study of Deep Learning Models for detection and classification of intracranial hemorrhage.- Business Intelligence and Database.- Increasing Student Engagement in Lessons and Assessing MOOC Participants Through Artificial Intelligence. -Mining frequents itemset and association rules in diabetic dataset.- Automatic text summarization for Moroccan Arabic dialect using an artificial intelligence approach.- Automatic Change Detection based on the Independent Component Analysis and Fuzzy C-means Methods.- Sentiment analysis decision system for tracking climate change opinion in Twitter.- Analysis of Decision Tree Algorithms for Diabetes Prediction.- How far can Deep Learning improve Arabic Part of Speech Tagging.- Optimization and Dynamic programming.- Analysis of Several Algorithms for DOA Estimation in Two Different Communication Models by a Comparative Study.- A Novel hybrid Approach for improving the accuracy of the Supervised Link Prediction based on Graph Structure Features in Social Networks. - Intelligent system based on GAN model for decision support in brain Tumor segmentation.- Hospital room management for Covid-19 patients using Petri nets.- Dimensionality reduction of MI-EEG data via convolutional autoencoders with a low size dataset.- Car tracking technique for DLES Project.

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Book SynopsisThis book constitutes the refereed proceedings of the 15th IAPR International Workshop on Document Analysis Systems, DAS 2022, held in La Rochelle, France, in May 2022.The full papers presented were carefully reviewed and selected from numerous submissions addressing key techniques of document analysis.

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Book SynopsisThis volume constitutes the papers of several workshops which were held in conjunction with the 27th International Conference on Database Systems for Advanced Applications, DASFAA 2022, held as virtual event in April 2022. The 30 revised full papers presented in this book were carefully reviewed and selected from 65 submissions. DASFAA 2022 presents the following five workshops: · First workshop on Pattern mining and Machine learning in Big complex Databases (PMBD 2021) · 6th International Workshop on Graph Data Management and Analysis (GDMA 2022) · First International Workshop on Blockchain Technologies (IWBT2022) · 8th International Workshop on Big Data Management and Service (BDMS 2022) · First workshop on Managing Air Quality Through Data Science · 7th International Workshop on Big Data Quality Management (BDQM 2022). Table of ContentsAn Algorithm for Mining Fixed-Length High Utility Itemsets.- A Novel Method to Create Synthetic Samples with Autoencoder Multi-layer Extreme Learning Machine.- Pattern Mining: Current Challenges and Opportunities.- Why not to Trust Big Data: Identifying Existence of Simpson’s Paradox Localized Metric Learning for Large Multi-Class Extremely Imbalanced Face Database.- Top-k dominating queries on incremental datasets.- Collaborative Blockchain based Distributed Denial of Service Attack Mitigation approach with IP Reputation System.- Model-Driven Development of Distributed Ledger Applications Towards a Blockchain Solution for Customs Duty-Related Fraud.- Securing Cookies/Sessions through Non-Fungible Tokens.- Chinese Spelling Error Detection and Correction Based on Knowledge Graph Construction and Application of Event Logic Graph: A Survey.- Enhancing Low-resource Languages Question Answering with Syntactic Graph.- Profile Consistency Discrimination.- H-V：An Improved Coding Layout based on Erasure Coded Storage System.- Astral: An Autoencoder-based Model for Pedestrian Trajectory Prediction of Variable-Length.- A Survey on Spatiotemporal Data Processing Techniques in Smart Urban Rail.- Fast Vehicle Track Counting in Traffic Video.- Summary A Traffic Summarization System using Semantic Words.- Attention_Cooperated_Reinforcement_Learning_for_Multi_agent_Path_Planning.- Big Data-driven Stable Task Allocation in Ride-hailing Services.- Weighted_Mean_Field_Multi_Agent_Reinforcement_Learning_via_Reward_Attribution_Decomposition.- Evaluating Presto and SparkSQL with TPC-DS.- Optimizing the Age of Sensed Information in Cyber-Physical Systems.- Aggregate Query Result Correctness using pattern Tables.- Time Series Data Quality Enhancing based on pattern Alignment.- Research on Feature extraction method of data quality intelligent detection.- Big Data Resources to Support Research Opportunities on Air Pollution Analysis in India.- Air Quality Data Collection in Hyderabad Using Low-cost Sensors: Initial Experiences.- Visualizing Spatio-Temporal Variation of Ambient Air Pollution in Four Small Towns in India.

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Book SynopsisThis book constitutes the refereed proceedings of the 12th International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2022, held in Helsinki, Finland, in June 2022. The 13 full papers presented were carefully reviewed and selected from 21 submissions. The papers address various topics such as information and knowledge systems, including submissions that apply ideas, theories or methods from specific disciplines to information and knowledge systems. Examples of such disciplines are discrete mathematics, logic and algebra, model theory, databases, information theory, complexity theory, algorithmics and computation, statistics and optimization.Table of ContentsOn Sampling Representatives of Relational Schemas with a Functional Dependency.- On the expressive power of message-passing neural networks as global feature map transformers.- Assumption-Based Argumentation for Extended Disjunctive Logic Programming.- A graph based semantics for Logical Functional Diagrams in power plant controllers.- Database Repair via Event-Condition-Action Rules in Dynamic Logic.- Statistics of RDF store for querying knowledge graphs.- Can you answer while you wait?.- The implication problem for functional dependencies and variants of marginal distribution equivalences.- Approximate Keys and Functional Dependencies in Incomplete Databases With Limited domains.- The Fault-Tolerant Cluster-Sending Problem.- Optimizing multiset relational algebra queries using weak-equivalent rewrite rules.- Properties of System W and its Relationships to Other Inductive Inference Operators.- Towards the Evaluation of Action Reversibility in STRIPS using Domain Generators.

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Book SynopsisThe volume LNAI 13546 constitutes the refereed proceedings of the 23rd Annual Conference Towards Autonomous Robotic Systems, TAROS 2022, held in Culham, UK, in September 2022. The 14 full papers and 10 short papers were carefully reviewed and selected from 38 submissions. Organized in the topical sections "Algorithms" and "Systems", they discuss significant findings and advances in the following areas: Robotic Grippers and Manipulation; Soft Robotics, Sensing and Mobile Robots; Robotic Learning, Mapping and Planning; Robotic Systems and Applications.Table of ContentsA distributed approach to haptic simulation.- A Novel Two-Hand-Inspired Hybrid Robotic End-Effector Fabricated Using 3D Printing.- Investigating the relationship between posture and safety in teleoperational tasks: A pilot study in improved operational safety through enhanced human-machine interaction.- Design and Analysis of an End Effector Using the Fin Ray Structure for Integrated Limb Mechanisms.- Trigger-Assisted Ambidextrous Control Framework for Teleoperation of Two Legged Manipulators.- Teleoperating a Legged Manipulator through Whole-Body Control.- In-silico Design and Computational Modelling of Electroactive Polymer based Soft Robotics.- Exploration of Underwater Storage Facilities with Swarm of Micro-Surface Robots.- Characterization of an Inflatable Soft Actuator and Tissue Interaction for In Vitro Mechanical Stimulation of Tissue.- EMap: Real-time terrain estimation.- Design and Preliminary In-Classroom Evaluation\\of a Low-Cost Educational Mobile Robot.- Internal State-based Risk Assessment for Robots in Hazardous Environment.- Investigating Scene Visibility Estimation within ORB-SLAM3.- Tactile and Proprioceptive Online Learning in Robotic Contour Following.- Learning cooperative behaviours in adversarial multi-agent systems.- Task Independent Safety Assessment for Reinforcement Learning.- Sensing Anomalies as Potential Hazards: Datasets and Benchmarks.- Integration and robustness analysis of the Buzz swarm programming language with the Pi-puck robot platform.- Implementing and assessing a remote teleoperation setup with a Digital Twin using cloud networking.- Agent-Based Simulation of Multi-Robot Soil Compaction Mapping.- A-EMS: An Adaptive Emergency Management System for Autonomous Agents in Unforeseen Situations.- Towards Scalable Multi-Robot Systems by Partitioning the Task Domain.- Effectiveness of brush operational parameters for robotic debris removal.- Automatic, Vision-Based Tool Changing Solution for Dexterous Teleoperation Robots in a Nuclear Glovebox

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Book SynopsisThis book constitutes the refereed proceedings of the 25th International Symposium on Formal Methods, FM 2023, which took place in Lübeck, Germany, in March 2023. The 26 full paper, 2 short papers included in this book were carefully reviewed and selected rom 95 submissions. They have been organized in topical sections as follows: SAT/SMT; Verification; Quantitative Verification; Concurrency and Memory Models; Formal Methods in AI; Safety and Reliability. The proceedings also contain 3 keynote talks and 7 papers from the industry day. Table of Contents​Keynotes.- Symbolic Computation in Automated Program Reasoning.- The next big thing: from embedded systems to embodied actors.- Intelligent and Dependable Decision-Making Under Uncertainty.- A Coq formalization of Lebesgue Induction Principle and Tonelli’s Theorem.- SAT/SMT.- Railway Scheduling Using Boolean Satisfiability Modulo Simulations.- SMT Sampling via Model-Guided Approximation.- Efficient SMT-based Network Fault Tolerance Verification.- Verification I.- Formalising the Prevention of Microarchitectural Timing Channels by Operating Systems.- Can we Communicate? Using Dynamic Logic to Verify Team Automata.- The ScalaFix equation solver.- HHLPy: Practical Verification of Hybrid Systems using Hoare Logic.- Quantitative Verification.- symQV: Automated Symbolic Verification of Quantum Programs.- PFL: a Probabilistic Logic for Fault Trees.- Energy Buechi Problems.- QMaude: quantitative specification and verification in rewriting logic.- Concurrency and Memory Models.- Minimisation of Spatial Models using Branching Bisimilarity.- Reasoning about Promises in Weak Memory Models with Event Structures.- A fine-grained semantics for arrays and pointers under weak memory models.- VeyMont: Parallelising Verified Programs instead of Verifying Parallel Programs.- Verification 2.- Verifying At the Level of Java Bytecode.- Abstract Alloy Instances.- Monitoring the Internet Computer.- Word Equations in Synergy with Regular Constraints.- Formal Methods in AI.- Verifying Feedforward Neural Networks for Classification in Isabelle/HOL.- SMPT: A Testbed for Reachabilty Methods in Generalized Petri Nets.- The Octatope Abstract Domain for Verification of Neural Networks.- Program Semantics and Verification Technique for AI-centred Programs.- Safety and Reliability.- Tableaux for Realizability of Safety Specifications.- A Decision Diagram Operation for Reachability.- Formal Modelling of Safety Architecture for Responsibility-AwareAutonomous Vehicle via Event-B Refinement.- A Runtime Environment for Contract Automata.- Industry Day.- Formal and Executable Semantics of the Ethereum Virtual Machine in Dafny.- Shifting Left for Early Detection of Machine-Learning Bugs.- A Systematic Approach to Automotive Security.- Specification-Guided Critical Scenario Identification for Automated Driving.- Runtime Monitoring for Out-of-Distribution Detection in Object Detection Neural Networks.- Backdoor Mitigation in Deep Neural Networks via Strategic Retraining.- veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System.

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Book SynopsisThis book constitutes the refereed proceedings of the 8th International Symposium, UNet 2022, held in Montreal, QC, Canada, during October 25–27, 2022. The 17 full papers included in this book were carefully reviewed and selected from 43 submissions. Moreover, 4 additional invited papers have been also considered. They were organized in topical sections as follows: ​Spectrum Management and Channel Prediction, Resource Allocation in 5G/6G, Internet of Things and Vehicular Communications, Artificial Intelligence-Driven Communications, Pervasive Services and Cyber Security.Table of ContentsSpectrum Management and Channel Prediction.- On the Influence of Microscopic Mobility in Modelling Pedestrian Communication.- Low Profile CPW Fed Tri-Band Millimeter Wave Antenna Design for Future 5G Application.- Trading off Controlled System Energy and Wireless Communication Energy.- Statistical Moments of the Temporal Spectrum of Electromagnetic Waves in the Equatorial Ionosphere.- Resource Allocation in 5G/6G.- Towards Facilitating URLLC in UAV-Enabled MEC Systems for 6G Networks.- Resource Allocation and Power Control for Heterogeneous Cellular Network and D2D Communications.- Optimized Network Coding With Real-Time Loss Prediction for Hybrid 5G Networks.- TCP-RTA: Real-Time Topology Adaptiveness for Congestion Control in TCP.- Rio_DSA: Redirecting I/O Scheme for Dynamic Storage Allocation on Docker Container.- Internet of Things and Vehicular Communications.- VANET-Based Traffic Light Management for an Emergency Vehicle.- Deep Reinforcement Learning to Improve Vehicle-to-Vulnerable Road User Communications in C-V2X.- Pervasive Computing for Efficient Intra-UAV Connectivity: Based on Context-Awareness.- Road Accident Analysis of Dhaka City Using Counter Propagation Network.- Artificial Intelligence-Driven Communications.- Reinforcement Learning for Protocol Synthesis in Resource-Constrained Wireless Sensor and IoT Networks.- Distributional Reinforcement Learning for VoLTE Closed Loop Power Control in Indoor Small Cells.- Reinforcement Learning Aided Routing in Tactical Wireless Sensor Networks.- A Green and Scalable Clustering for Massive IoT Sensors with Selective Deactivation.- Pervasive Services and Cyber Security.- Threat Mitigation Model With Low False Alarm Rate Based on Hybrid Deep Belief Network.- On Feature Selection Algorithms for Effective Botnet Detection.- A Novel Hybrid Deep Learning Model for Crop Disease Detection Using BEGAN.- Multivariate Skewness and Kurtosis for Detecting Wormhole Attack in VANETs.

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Book SynopsisThis book constitutes the refereed post-conference proceedings the 14th TPC Technology Conference on Performance Evaluation and Benchmarking, TPCTC 2022, which was held in Sydney, NSW, Australia, on September 5, 2022.The 5 revised full papers presented were carefully selected from 12 submissions. The conference focuses on Pick and Mix Isolation Levels; Benchmarking considerations for Trustworthy and Responsible AI (Panel); Preliminary Scaling Characterization with TPCx-AI and New Initiatives. Table of ContentsPick and Mix Isolation Levels: Mixed Serialization Graph Testing.- BODS: A Benchmark on Data Sortedness.- Disaggregated Database Management Systems (Panel).- TPCx-AI on NVIDIA Jetson.- More the Merrier: Comparative evaluation of TPCx-AI and MLPerf Benchmarks for AI.- Preliminary Scaling Characterization with TPCx-AI.- 4mbench: Performance Benchmark of Manufacturing Business Database.- Benchmarking considerations for Trustworthy and Responsible AI (Panel).- TPCx-AI: First Adopter's Experience Report.- New Initiatives in the TPC.

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Book SynopsisThis book constitutes the joint refereed proceedings of the 22nd International Conference on Internet of Things, Smart Spaces, and Next Generation Networks and Systems, NEW2AN 2022, held in Tashkent, Uzbekistan, in December 2022.The 58 regular papers presented in this volume were carefully reviewed and selected from 282 submissions. The papers of NEW2AN address various aspects of next-generation data networks, while special attention is given to advanced wireless networking and applications. In particular, the authors have demonstrated novel and innovative approaches to performance and efficiency analysis of 5G and beyond systems, employed game-theoretical formulations, advanced queuing theory, and machine learning. It is also worth mentioning the rich coverage of the Internet of Things, optics, signal processing, as well as digital economy and business aspects.Table of ContentsTangential shear stress in an oscillatory flow of a viscoelastic fluid in a flat channel.- Comparison of Finite Difference Schemes of Different Orders of Accuracy for the Burgers Wave Equation Problem.- Numerical solution of the combustion process using the computer package ANSYS fluent.- Simulation Modeling of Reliability of Packet Switching Unit.- Analytical Model for Assessing the Reliability of the Functioning of the Adaptive Switching Node.- Artificial intelligence software architecture in the field of cardiology and application in the cardio vessel project using CJM and customer development methods.- Using discretization and numerical methods of problem 1d-3d-1d model for blood vessel walls with Navier-Stokes.- Numerical simulation of a flow in a two-dimensional channel on the basis of a two-liquid turbulence model.- Application fuzzy neural network methods to detect cryptoattacks on financial information systems based on blockchain technology.- Method authentication of objects information communication systems.- TEDCTSSA: Trust Enabled Data Collection Technique based Sparrow Search Algorithm for WSN-based Applications.- ISTOA: An improved Sooty Tern Optimization Algorithm for multilevel threshold image segmentation.- Implementing digital transformation in the logistics system of Uzbekistan.- Numerical modeling of vertical axis wind turbines using ANSYS FLUENT software.- "i’ll wait 4 ur answr!” A Study on Modern Style of Cyber-Writing and User Reactions.- Improvement of information support in intelligent information energy systems.- The Assessment of the Effectiveness of the Development of Digital Technologies in Commercial Banks in Uzbekistan.- The Study of the Impact of the Digital Economy on the Growth of E-Government Services in Uzbekistan.- Deep learning algorithm for classifying dilated cardiomyopathy and hypertrophy cardiomyopathy in transport workers.- eCommerce benchmarking: theoretical background, variety of types, and application of competitive-integration benchmarking.- Cryptocurrencies as the money of the future.- A Data Security Technique Combining Asymmetric Cryptography and Compressive Sensing for IoT Enabled Wireless Sensor Networks.- Energy Efficient and Secure Scheme based Compressive Sensing method for Internet of Vehicles.- Impact of digital technologies on women’s employment.- The impact of digitalisation on the safe development of individuals in society.- Econometric Evaluation of the Efficiency of the Management of theEnterprise through the Supply of Raw Materials in Oil Enterprises in the Conditions of the Digital Economy.- The impact of digital infrastructure, foreign direct investment and trade openness on economic growth: In the case of Uzbekistan.- The impact of the financial ratios on the financial performance. A caseof Chevron Corporation (CVX).- The impact of the digitalisation of payment systems on the profitabilityof commercial banks.- The main aspects and benefits of digital transformation of business entities.- The influence of the capital structure of state enterprises on the profitability of the enterprise.- Exploring the development of China’s digital trade in the context of the domestic and international double cycle.- A systematic mapping study of using the cutting-edge technologies in marketing: the state of the art of four key new-age technologies.- Social Media Marketing for Educational Purposes: Goals, Objectivesand Content of the Training Course.- Digital Marketing and Smart Technology Marketing Systems as thefuture of metaverse.- The impact of the digital economy on the development of higher education.- What is the state-of-the-art contribution of the higher education system to the digital economy: a systematic mapping study on changes and challenges.- Innovating primary education of promoting students’ languagecompetencies through mobile assisted language learning approach: Selection framework of innovative digital technologies.- Econometric assessment of the dynamics of development of the export authority of small business and private business subjects in the conditions of the digital economy.- The significance of the Internet of Things for ensuring the smooth operation of network functions in fintech.- Impact of E-government on Poverty Rate: a Cross- Country Empirical Assessment.- An empirical investigation of the relationship between e-government development and multidimensional poverty.- On Digital Twin Software and Cyber Threats.- On local services based on non-standard Wi-Fi Direct usage model.- Compatibility analysis between 5G NR and ultra-wideband devices in the 6425-7125 MHz frequency band.- 6 GHz band sharing study for FWA base stations and GEO satellite receivers.- Federated Learning Strategies Over Wireless Channels.- Data Routing in UAV Networks with Multiple Data Sources using Steiner Tree.- Reduced complexity distributed arithmetic architecture for FIR filters.- Blockchain-driven Hybrid Model for IoT Authentication.- An Heuristic Approach for Mapping of Service Function Chains in Softwarized 5G Networks.- Multi-threshold hysteresis-Based Congestion Control for UAV-based Detection Sensor Network.- Analysis of the capacity gain of Probability Shaping QAM.- LoRa Mesh Network for Image Transmission: An Experimental Study.- Blockchain Technology – Innovation for Better Collaboration and Increased Efficieny. The U.S. Logistics and Trucking Industry Case.- Econometric Study of the Impact of the Digital Economy on the Gross Product in Anti-monopoly Conditions.- Predictive models for effective management of e- commerce in New Uzbekistan.- The role of IT on transportation, logistics and the economic growth among Central Asian countries.

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Book SynopsisThis book constitutes the proceedings of the 17th International Conference on Formal Concept Analysis, ICFCA 2023, which took place in Kassel, Germany, in July 2023.The 13 full papers presented in this volume were carefully reviewed and selected from 19 submissions. The International Conference on Formal Concept Analysis serves as a platform for researchers from FCA and related disciplines to showcase and exchange their research findings. The papers are organized in two topical sections, first "Theory" and second "Applications and Visualization".Table of Contents​Theory: Approximating fuzzy relation equations through concept lattices.- Doubly-Lexical Order Supports Standardisation and Recursive Partitioning of Formal Context.- Graph-FCA Meets Pattern Structures.- On the commutative diagrams among Galois connections involved in closure structures.- Scaling Dimension.- Three Views on Dependency Covers from an FCA Perspective.- A Triadic Generalisation of the Boolean Concept Lattice.- Applications and Visualization: Computing witnesses for centralising monoids on a three-element set.- Description Quivers for Compact Representation of Concept Lattices and Ensembles of Decision Trees.- Examples of clique closure systems.- On the maximal independence polynomial of the covering graph of the hypercube up to n=6.- Relational Concept Analysis in Practice: Capitalizing on Data Modeling using Design Patterns.- Representing Concept Lattices with Euler Diagrams.

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Book SynopsisThis book constitutes the refereed proceedings of the 24th Annual Conference Towards Autonomous Robotic Systems, TAROS 2023, held in Cambridge, UK, during September 13–15, 2023.The 40 full papers presented in this book were carefully reviewed and selected from 70 submissions.They cover a wide range of different topics such as: agri-food robotics; autonomy; collaborative and service robotics; locomotion and manipulation; machine vision; multi-robot systems; soft robotics; tactile sensing; and teleoperation.Table of Contents​Agri-food Robotics.- Plant phenotyping using DLT method: Towards retrieving the delicate features in a dynamic environment.- Rapid Development and Performance Evaluation of a Potato Planting Robot.- An Automated Precision Spraying Evaluation System.- Smart Parking System Using Heuristic Optimization For Autonomous Transportation Robots In Agriculture.- Closed-Loop Robotic Cooking of Soups with Multi-modal Taste Feedback.- Folding Morphing-wheg Duct-entry Robot for Nuclear Characterisation.- Autonomy.- Occupancy Map Abstraction for Higher Level Mission Planning of Autonomous Robotic Exploration in Hazardous Nuclear Environments.- Spiral Sweeping Protocols for Detection of Smart Evaders.- Action Recognition for Improving Pedestrian Intent Prediction.- Evaluation of SLAM algorithms for Search and Rescue applications.- Developing an Integrated Runtime Verification for Safety and Security of Industrial Robot Inspection System.- Collaborative and Service Robotics.- Sonification of Ionising Radiation Data for Robot Operators.- Automating a Telepresence Robot for Human Detection, Tracking, and Following.- Towards Multimodal Sensing and Interaction for Assistive Autonomous Robots.- Locomotion and Manipulation.- CPG-based locomotion control of a quadruped robot with an active spine.- Low-resolution sensing for sim-to-real complex terrain robots.- Towards wait-and-catch routine of a dynamic swinging object using a prototype robotic arm manipulator.- Simultaneous Base and Arm Trajectories for Multi-Target Mobile Agri-Robot.- Design and kinematic analysis of a 3D-printed 3DOF robotic manipulandum.- Sim-to-Real Deep Reinforcement Learning with Manipulators for Pick-and-place.- Machine Vision.- Fast 3D Semantic Segmentation Using a Self Attention Network and Random Sampling.- An assessment of self-supervised learning for data efficient potato instance segmentation.- Automated 3D Mapping, localization and pavement inspection with low cost RGB-D cameras and IMUs.- Optimized Custom Dataset for Efficient Detection of Underwater Trash.- A Geometric Algebra Solution to the 3D Registration Problem.- Active Anomaly Detection for Autonomous Robots: a Benchmark.- Multi-robot Systems.- Hardware Validation of Adaptive Fault Diagnosis in Swarm Robots.- Mobile Robots For Collaborative Manipulation Over Uneven Ground Using Decentralised Impedance Control.- Multi-agent Collaborative Target Search Based on Curiosity Intrinsic Motivation.- Simulation of Collective Bernoulli-Ball System for Characterizing Dynamic Self-stability.- Soft Robotics.- Casting vs injection moulding: a comparison study for in-lab low-cost soft robot fabrication.- Estimation of Soft Body Deformation by Using Light.- Reduced-Order Modeling of a Soft Anthropomorphic Finger for Piano Keystrokes.- Tactile Sensing.- Multi-directional Force and Tactile Sensor Sleeves for Micro Catheters and Cannulas.- Towards smooth human-robot handover with a vision-based tactile sensor.- Feeling Good: Validation of Bilateral Tactile Telemanipulation for a Dexterous Robot.- Teleoperation.- Comparative study of hand-tracking and traditional control interfaces for remote palpation.- 5G-based Low-Latency Teleoperation: Two-way Timeout Approach.- Generative Model-based Simulation of Driver Behavior when Using Control Input Interface for Teleoperated Driving in Unstructured Canyon Terrains.- Implementation of a Stereo Vision System for a Mixed Reality Robot Teleoperation Simulator.

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Book SynopsisThis book focuses in detail on data science and data analysis and emphasizes the importance of data engineering and data management in the design of big data applications. The author uses patterns discovered in a collection of big data applications to provide design principles for hypothesis generation, integrating big data processing and management, machine learning and data mining techniques. The book proposes and explains innovative principles for interpreting hypotheses by integrating micro-explanations (those based on the explanation of analytical models and individual decisions within them) with macro-explanations (those based on applied processes and model generation). Practical case studies are used to demonstrate how hypothesis-generation and -interpretation technologies work. These are based on “social infrastructure” applications like in-bound tourism, disaster management, lunar and planetary exploration, and treatment of infectious diseases. The novel methods and technologies proposed in Hypothesis Generation and Interpretation are supported by the incorporation of historical perspectives on science and an emphasis on the origin and development of the ideas behind their design principles and patterns. Academic investigators and practitioners working on the further development and application of hypothesis generation and interpretation in big data computing, with backgrounds in data science and engineering, or the study of problem solving and scientific methods or who employ those ideas in fields like machine learning will find this book of considerable interest.Table of ContentsBasic Concept.- Hypothesis.- Science and Hypothesis.- Regression.- Machine Learning and Integrated Approach.- Hypothesis Generation by Difference.- Methods for Integrated Hypothesis Generation.- Interpretation.

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Book SynopsisThis book constitutes the refereed proceedings of the 19th International Conference on Advanced Data Mining and Applications, ADMA 2023, held in Shenyang, China, during August 21–23, 2023.The 216 full papers included in this book were carefully reviewed and selected from 503 submissions. They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.Table of ContentsPharmaceutical Data Analysis.- Drug-target interaction prediction based on drug subgraph fingerprint extraction strategy and subgraph attention mechanism.- Soft Prompt Transfer for Zero-Shot and Few-Shot Learning in EHR Understanding.- Graph Convolution Synthetic Transformer for Chronic Kidney Disease Onset Prediction.- MTFL: Multi-task feature learning with joint correlation structure learning for Alzheimer’s disease cognitive performance prediction.- Multi-Level Transformer for Cancer Outcome Prediction in Large-Scale Claims Data.- Individual Functional Network Abnormalities Mapping via Graph Representation-based Neural Architecture Search.- A novel application of a mutual information measure for analysing temporal changes in healthcare network graphs.- Drugs Resistance Analysis from Scarce Health Records via Multi-task Graph Representation.- Text Classification.- ParaNet:Parallel Networks with Pre-trained Models for Text Classification.- Open Text Classification Based on Dynamic Boundary Balance.- A Prompt Tuning Method for Chinese Medical Text Classification.- TabMentor: Detect Errors on Tabular Data with Noisy Labels.- Label-aware Hierarchical Contrastive Domain Adaptation for Cross-network Node Classification.- Semi-supervised classification based on Graph Convolution Encoder Representations from BERT.- Global Balanced Text Classification for Stable Disease Diagnosis.- Graph.- Dominance Maximization in Uncertain Graphs.- LAGCL: Towards Stable and Automated Graph Contrastive Learning.- Discriminative Graph-level Anomaly Detection via Dual-students-teacher Model.- Common-Truss-based Community Search on Multilayer Graphs.- Learning To Predict Shortest Path Distance.- Efficient Regular Path Query Evaluation with Structural Path Constraints.EnSpeciVAT: Enhanced SpeciVAT for Cluster Tendency Identification in Graphs.- Pessimistic Adversarially Regularized Learning for Graph Embedding.- M2HGCL: Multi-Scale Meta-Path Integrated Heterogeneous Graph Contrastive Learning.

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Book SynopsisThis volume comprises the proceedings of the International Workshop, ShapeMI 2023, which took place alongside MICCAI 2023 on October 8, 2023, in Vancouver, British Columbia, Canada.The 23 selected full papers deal with all aspects of leading methods and applications for advanced shape analysis and geometric learning in medical imaging.Table of ContentsAnatomy Completor: A Multi-class Completion Framework for 3D Anatomy Reconstruction.- C3Fusion: Consistent Contrastive Colon Fusion, Towards Deep SLAM in Colonoscopy.- Anatomy-Aware Masking for Inpainting in Medical Imaging.- Particle-Based Shape Modeling for Arbitrary Regions-of-Interest.- Optimal coronary artery segmentation based on transfer learning and UNet architecture.- Unsupervised Learning of Cortical Surface Registration using Spherical Harmonics.- Unsupervised correspondence with combined geometric learning and imaging for radiotherapy applications.- ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images.- Body Fat Estimation from Surface Meshes using Graph Neural Networks.- Geometric Learning-Based Transformer Network for Estimation of Segmentation Errors.- On the Localization of Ultrasound Image Slices within Point Distribution Models.- FSJP-Net: Foreground and Shape Joint Perception Network for Glomerulus Detection.- Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models.- Muscle volume quantification: guiding transformers with anatomical priors.- Geodesic Logistic Analysis of Lumbar Spine Intervertebral Disc Shapes in Supine and Standing Positions.- SlicerSALT: From medical images to quantitative insights of anatomy.- Predicting Shape Development: A Riemannian Method.- AReg IOS: Automatic Registration on IntraOralScans.- Modeling Longitudinal Optical Coherence Tomography Images for Monitoring and Analysis of Glaucoma Progression.- IcoConv : Explainable brain cortical surface analysis for ASD classification.- DeCA: A Dense Correspondence Analysis Toolkit for Shape Analysis.- 3D Shape Analysis of Scoliosis.- SADIR: Shape-Aware Diffusion Models for 3D Image Reconstruction.

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Book SynopsisThe two-volume set LNCS 14388 and 14389 constitutes the refereed proceedings of the 9th Russian Supercomputing Days International Conference (RuSCDays 2023) held in Moscow, Russia, during September 25-26, 2023.The 44 full papers and 1 short paper presented in these proceedings were carefully reviewed and selected from 104 submissions. The papers have been organized in the following topical sections: supercomputer simulation; distributed computing; and HPC, BigData, AI: algorithms, technologies, evaluation.Table of Contents​Supercomputer Simulation: Application of HPC for Simulation of Sealant Influence on the Aircraft Assembly Process.- Block Algebraic Multigrid Method for Saddle-Point Problems of Various Physics.- Computational Efficiency of Iterative Methods for Solving Inverse Problems of Ultrasound Tomography.- Computer Memory Requirements for Matrix-Forming Approach to Global Stability Analysis of Fluid Flows.- CubicEoS.jl: Extensible, Open-Source Isothermal Phase Equilibrium Calculations for Fluids.- Efficiency and Accuracy of High-performance Calculations of the Electrostatic Energy of Thin Films Atomistic Clusters.- Field-Split Iterative Solver vs Direct One for Quasi-Static Biot Equation.- GPU-Accelerated Matrix Exponent for Solving 1D Time-Dependent Schrodinger Equation.- How to Make Lanczos-Montgomery Fast on Modern Supercomputers?.- Multi-Node GPU-Enabled Pseudo-Spectral Solver for Turbulence Problems.- Multicriteria Optimization of Chemical Reactions Using Interval Analysis. Parallel Scheme for Implementing the Computational Process.- Parallel Numerical Implementation 3D Wave Hydrodynamics and SWAN Models.- Parallel Numerical Simulation of Sonic Log for 3D Heterogeneous TTI Media.- Quantum-Chemical Study of Some Tris(pyrrolo)benzenes and Tris(pyrrolo)-1,3,5-triazines.- Reduced Precision Computations in the SL-AV Global Atmosphere Model.- Scalability of the INM RAS Earth System Model.- Simulation of Free-Surface Fluid Dynamics: Parallelization for GPUs.- Software Package USPARS for Large Sparse Systems of Linear Equations.- Supercomputer Search for Coagulation Factor XIIa Inhibitors in the Chinese National Compound Library.- Supercomputer Technologies for Ultrasound Nondestructive Imaging of Low-contrast Defects in Solids.- The Effect of Data Structuring on the Parallel Efficiency of the HydroBox3D Relativistic Code.- The Efficiency Optimization Study of a Geophysical Code on Manycore Computing Architectures.- Using Parallel Technologies to Calculate Fluid Dynamic Processes in a Fractured-Porous Reservoir Taking into Account Non-Isothermality.

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Book SynopsisThis book constitutes the proceedings of the 11th International Conference on Big Data and Artificial Intelligence, BDA 2023, held in Delhi, India, during December 7–9, 2023. The17 full papers presented in this volume were carefully reviewed and selected from 67 submissions. The papers are organized in the following topical sections: ​Keynote Lectures, Artificial Intelligence in Healthcare, Large Language Models, Data Analytics for Low Resource Domains, Artificial Intelligence for Innovative Applications and Potpourri. Table of Contents​Keynote Lectures.- Representation Learning for Dialog Models.- Sparsity, modularity, and structural plasticity in deep neural networks.- Artificial Intelligence in Healthcare.- Tuberculosis disease diagnosis using controlled super resolution.- GREAT AI in Medical Appropriateness and Value-Based-Care.- Large Language Models.- KG-CTG: Citation Generation through Knowledge Graph-guided Large Language Models.- SciPhyRAG - Retrieval Augmentation to Improve LLMs on Physics Q&A.- Revolutionizing High School Physics Education: A Novel Dataset.- Context-Enhanced Language Models for Generating Multi-Paper Citations.- GEC-DCL: Grammatical Error Correction Model with Dynamic Context Learning for Paragraphs & Scholarly Papers.- Data Analytics for Low Resource Domains.- A Deep Learning Emotion Classification Framework for Low Resource Languages.- Assessing the Efficacy of Synthetic Data for Enhancing Machine Translation Models in Low Resource Domains.- Artificial Intelligence for Innovative Applications.- Evaluation of Hybrid Quantum Approximate Inference Methods on Bayesian Networks.- IndoorGNN: A Graph Neural Network based approach for Indoor Localization using WiFi RSSI.- Ensemble-Based Road Surface Crack Detection: A Comprehensive Approach.- Potpourri.- Fast similarity search in large-scale Iris databases using high-dimensional hashing.- Explaining Finetuned Transformers on Hate Speech Predictions using Layerwise Relevance Propagation.- Multilingual Speech Sentiment Recognition using Spiking Neural Networks.- FopLAHD: Federated optimization using Locally Approximated Hessian Diagonal.- A Review of Approaches on Facets for Building IT-based Career Guidance Systems.

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Book SynopsisThis book collects together selected peer-reviewed contributions presented at the European Conference on Data Analysis, ECDA 2022, held in Naples, Italy, September 14-16, 2022. Highlighting the role of statistics in discovering novel and interesting patterns in the era of big data, it follows the motto of the conference: “Avoiding drowning in the data: recent trends and future challenges in learning from data”. The central focus is on multidisciplinary approaches to data analysis, classification, and the interface between computer science, data mining and statistics. Both methodological and applied topics are covered. The former includes supervised and unsupervised techniques with particular emphasis on advances in regression and clustering analysis and constructing composite indicators. The applications are mainly in risk analysis, biology, and education. The volume is organized into four main macro themes: methodological contributions in the social sciences and education, multivariate analysis methods for big data, innovative contributions for applications inspired by biology, and strategies for analyzing complex data in finance.Table of ContentsPreface.- Building hierarchies of factors with disjoint factor analysis.- Uncertainty in Latent Trait Models and dimensionality reduction methods for complex data: an analysis of taxpayer perception on the Fiscal System.- The predictivity of access tests for university success.- Asynchronous and synchronous-asynchronous particle swarms.- The impact of the Covid-19 pandemic on modelling volatility and risk analysis of returns in selected European financial markets.- Asymmetric binary regression models for imbalanced datasets: an application to students’ churn.- Computational models supporting decision-making in managing publication activity at Polish universities.- Stability of nonparametric methods for cognitive diagnostic assessment.- SMARTS: SeMi-supervised clustering for Assessment of Reviews using Topic and Sentiment.- The equitable and sustainable wellbeing through the pandemic. A first study to assess changes at local level in Italy.- Choice-Based Optimization under High-Dimensional MNL.- A first glance on co-evolution of Boolean networks to simulate the development of cross-talking systems in molecular biology.- Classification on polish fund market during COVID-19 pandemic - extreme risk modeling approach.

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Book SynopsisSkin Image  Analysis.- Computer-Aided Pelvic Imaging for Female Health.

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Book Synopsis.- Identity Management, Authentication, Access Control & FormalModeling..- Toward Secure and Trustworthy Identity Management Systems: AKnowledge-base Driven Approach..- Always Authenticated, Never Exposed: Continuous Authentication viaZero-Knowledge Proofs..- Pragmatic guidelines for formal modeling of security ceremonies..- Access Control Administration for Smart Homes..- AI & Machine Learning for Security..- Parameter-Efficient Fine-Tuning of LLMs for Intrusion Detection andFirewall Rule Generation: A Comparative Study..- ABusing social media & sentiment analysis for stock market prediction..- DrATC+: A Divide et Impera Extension to Trust-Based Dynamic Routing..- Cybersecurity Strategies, Regulations, Privacy..- How to Train Your Guardian: Evaluating Cyber Security ExercisesUsing Situation Awareness..- Towards an architecture for managing security under the EU CyberResilience Act..- Google Tag Manager and its Privacy Issues..- Cryptography & Threat Analysis..- Measuring Modern Phishing Tactics: A Quantitative Study of BodyObfuscation Prevalence, Co-occurrence, and Filter Impact..- Nicknames for Group Signatures..- The Impact of Filtering in Differential Cryptanalysis: A Case Study onFEAL-8.

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Book SynopsisThis book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.Trade Review“Charu Aggarwal, a well-known, reputable IBM researcher, has taken the time to distill the advances in the design of recommender systems since the advent of the web … . Extensive bibliographic notes at the end of each chapter and more than 700 references in the book bibliography make this monograph an excellent resource for both practitioners and researchers. … Without a doubt, this is an excellent addition to my bookshelf!” (Fernando Berzal, Computing Reviews, February, 2017)Table of ContentsAn Introduction to Recommender Systems.- Neighborhood-Based Collaborative Filtering.- Model-Based Collaborative Filtering.- Content-Based Recommender Systems.- Knowledge-Based Recommender Systems.- Ensemble-Based and Hybrid Recommender Systems.- Evaluating Recommender Systems.- Context-Sensitive Recommender Systems.- Time- and Location-Sensitive Recommender Systems.- Structural Recommendations in Networks.- Social and Trust-Centric Recommender Systems.- Attack-Resistant Recommender Systems.- Advanced Topics in Recommender Systems.

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Book SynopsisIn this book the author argues that the basis of what we consider computer intelligence has algorithmic roots, and he presents this with a holistic view, showing examples and explaining approaches that encompass theoretical computer science and machine learning via engineered algorithmic solutions.Part I of the book introduces the basics. The author starts with a hands-on programming primer for solving combinatorial problems, with an emphasis on recursive solutions. The other chapters in the first part of the book explain shortest paths, sorting, deep learning, and Monte Carlo search. A key function of computational tools is processing Big Data efficiently, and the chapters in Part II of the book examine traditional graph problems such as finding cliques, colorings, independent sets, vertex covers, and hitting sets, and the subsequent chapters cover multimedia, network, image, and navigation data. The highly topical research areas detailed in Part III are machine learning, problem solving, action planning, general game playing, multiagent systems, and recommendation and configuration. Finally, in Part IV the author uses application areas such as model checking, computational biology, logistics, additive manufacturing, robot motion planning, and industrial production to explain how the techniques described may be exploited in modern settings.The book is supported with a comprehensive index and references, and it will be of value to researchers, practitioners, and students in the areas of artificial intelligence and computational intelligence.Table of ContentsPreface.- Towards a Characterization.- Part I, Basics.- 1. Programming Primer.- 2. Shortest Paths.- 3. Sorting.- 4. Deep Learning.- 5. Monte-Carlo Search.- Part II, Big Data.- 6. Graph data.- 7. Multimedia Data.- 8. Network Data.- 9. Image Data.- 10. Navigation Data.- Part III, Research Areas.- 11. Machine Learning.- 12. Problem Solving.- 13. Card Game Playing.- 14. Action Planning.- 15. General Game Playing.- 16. Multiagent Systems.- 17. Recommendation and Configuration Part IV, Applications.- 18. Adversarial Planning.- 19. Model Checking.- 20. Computational Biology.- 21. Logistics.- 22. Additive Manufacturing.- 23. Robot Motion Planning.- 24. Industrial Production.- 25. Further Application Areas. - Index and References

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Book SynopsisTable of Contents1 Einleitung.- 2 Einstieg in das Arbeiten mit AutoCAD.- 2.1 Anstarten und Beenden des Arbeitens mit AutoCAD.- 2.2 Das Arbeiten mit Dateien.- 2.3 Hilfen für den Benutzer.- 2.4 Festlegen der Zeichnungsgröße.- 3 Das Zeichnen gerader Linien.- 3.1 Der Befehl LINIE.- 3.2 Löschen von Zeichnungselementen.- 3.3 Zeichnen von Linien im Ortho-Modus.- 4 Hilfen beim Zeichnen.- 4.1 Das Arbeiten mit Rastern.- 4.2 Das Arbeiten mit verschiedenen Objektfang-Modi.- 5 Das Arbeiten mit Bildausschnitten.- 5.1 Der Befehl ZOOM.- 5.2 Der Befehl PAN.- 6 Kreise, Kreisbögen und Abrundungen.- 6.1 Das Zeichnen von Kreisen.- 6.2 Das Zeichnen von Kreisbögen.- 6.3 Das Abrunden sich schneidender Linien.- 7 Die Bemaßung von Zeichnungen.- 7.1 Allgemeines zum Bemaßen.- 7.2 Ausführen von Linearbemaßungen.- 7.3 Winkelbemaßungen.- 7.4 Durchmesser- und Radienbemaßung.- 7.5 Weitere Optionen des Bemaßungsbefehls BEM.- 8 Das Arbeiten mit Texten.- 8.1 Der Befehl TEXT.- 8.2 Der Befehl DTEXT.- 8.3 Der Befehl STIL.- 9 Regenerieren von Zeichnungen.- 10 Das Zeichnen von Bändern und Flächen.- 10.1 Der Befehl BAND.- 10.2 Der Befehl SOLID.- 11 Das Schraffieren von Flächen.- 11.1 Das eigentliche Schraffieren.- 11.2 Befehle zum Löschen von Zeichnungsteilen.- 12 Das Zeichnen von Freihandlinien.- 13 Abfrage- und Anzeigebefehle.- 14 Layer-Technik.- 14.1 Der Befehl LAYER.- 14.2 Umbenennen und Löschen von Layern.- 15 Das Arbeiten mit Blöcken.- 15.1 Das Erzeugen von Blöcken.- 15.2 Das Einfügen von Blöcken.- 16 Das Manipulieren von Zeichnungen.- 16.1 Das Verschieben und Spiegeln von Objekten.- 16.2 Das Kopieren von Objekten.- 16.3 Das Ändern von Objekten.- 17 Das Erstellen von Zusammenbau-Zeichnungen.- A Lösungen.- B Zusätzliche Übungsaufgaben.- C Standard-Schraffuren.- D Referenzliste.

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Book SynopsisMachine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorithms are also discussed. This book offers a valuable resource for postgraduates, researchers and other professionals in the fields of data mining, fuzzy computing and uncertainty reasoning.Zengchang Qin is an associate professor at the School of Automation Science and Electrical Engineering, Beihang University, China; Yongchuan Tang is an associate professor at the College of Computer Science, Zhejiang University, China.

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Book SynopsisDieses essential geht präzise auf die Anforderungen und Lösungsansätze in Datenmigrations- und Konsolidierungsszenarien im SAP-Umfeld ein und präsentiert Fallbeispiele von SAP-Kunden. Der Beitrag unterstützt Unternehmen, die vor Fusionen, Zu- oder Verkäufen oder Umstrukturierungsvorhaben stehen und effiziente und verlässliche Lösungen zur Datenmigration benötigen, um die Organisationsstrukturen und Prozesse innerhalb ihrer Systemlandschaft an die betrieblichen Veränderungen anzupassen. Die Autoren zeigen, wie Unternehmen ihre Systemlandschaft harmonisieren und konsolidieren können und somit einheitliche Strukturen etabliert, die Systemkomplexität reduziert und damit der Geschäftsbetrieb verbessert werden können.Table of ContentsWas Sie in diesem Essential finden können.- Die Bedeutung von Datenmigration und Systemkonsolidierung für den Unternehmenserfolg.- Vereinfachung und Standardisierung bei großen Datenvolumen (Big Data) durch Systemkonsolidierung.- Datenmigration.- Was Sie aus diesem Essential mitnehmen können.

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Book SynopsisFür Leser, die bereits die Grundlagen der Wissensverarbeitung und Computernetzwerke beherrschen, gibt das Buch einen Überblick über innovative Verfahren, die die automatisierte Suche, Recherche, Klassifikation und Verwaltung von Texten im Kontext dezentraler Systeme und vor allem im WWW erlauben. Besondere Aufmerksamkeit wird dabei auf eine personalisierte Verarbeitung gerichtet, die auch zeitliche Aspekte, wie z. B. das digitale Vergessen, einbeziehen. An vielen Stellen werden auf interessante und neuartige Art und Weise Analogien aus anderen Wissensgebieten, so z. B. zur Verarbeitung von Informationen und zum Lernen im menschlichen Gehirn sowie der Natur schlechthin genutzt.Table of ContentsWissensverarbeitung im menschlichen Gehirn - Lernen - Netzwerke für die Textanalyse - Digitale Updates und digitales Vergessen - Exploration von Netzwerkstrukturen - Konzepte des Text Minings in dezentralen Systemen - Informationsmanagement im Web

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Book SynopsisWith this book, Christopher Kormanyos delivers a highly practical guide to programming real-time embedded microcontroller systems in C++. It is divided into three parts plus several appendices. Part I provides a foundation for real-time C++ by covering language technologies, including object-oriented methods, template programming and optimization. Next, part II presents detailed descriptions of a variety of C++ components that are widely used in microcontroller programming. It details some of C++’s most powerful language elements, such as class types, templates and the STL, to develop components for microcontroller register access, low-level drivers, custom memory management, embedded containers, multitasking, etc. Finally, part III describes mathematical methods and generic utilities that can be employed to solve recurring problems in real-time C++. The appendices include a brief C++ language tutorial, information on the real-time C++ development environment and instructions for building GNU GCC cross-compilers and a microcontroller circuit. For this fourth edition, the most recent specification of C++20 is used throughout the text. Several sections on new C++20 functionality have been added, and various others reworked to reflect changes in the standard. Also several new example projects ranging from introductory to advanced level are included and existing ones extended, and various reader suggestions have been incorporated. Efficiency is always in focus and numerous examples are backed up with runtime measurements and size analyses that quantify the true costs of the code down to the very last byte and microsecond. The target audience of this book mainly consists of students and professionals interested in real-time C++. Readers should be familiar with C or another programming language and will benefit most if they have had some previous experience with microcontroller electronics and the performance and size issues prevalent in embedded systems programming.Table of ContentsPart I: Language Technologies for Real-Time C++.- 1. Getting Started with Real-Time C++.- 2. Working with a Real-Time C++ Program on a Board.- 3. An Easy Jump Start in Real-Time C++.- 4. Object-Oriented Techniques for Microcontrollers.- 5. C++ Templates for Microcontrollers.- 6. Optimized C++ Programming for Microcontrollers.- Part II: Components for Real-Time C++.- 7. Accessing Microcontroller Registers.- 8. The Right Start.- 9. Low-Level Hardware Drivers in C++.- 10. Custom Memory Management.- 11. C++ Multitasking.- Part III: Mathematics and Utilities for Real-Time C++.- 12. Floating-Point Mathematics.- 13. Fixed-Point Mathematics.- 14. High-Performance Digital Filters.- 15. C++ Utilities.- 16. Extending the C++ Standard Library and the STL.- 17. Using C-Language Code in C++.- Additional Reading.

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Book SynopsisThe 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 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 54th issue of Transactions on Large-Scale Data and Knowledge-Centered Systems, contains three fully revised and extended papers and two additional extended keynotes selected from the 38th conference on Data Management - Principles, Technologies and Applications, BDA 2022. The topics cover a wide range of timely data management research topics on temporal graph management, tensor-based data mining, time-series prediction, healthcare analytics over knowledge graphs, and explanation of database query answers.Table of ContentsClock-G: Temporal graph management system.- TSPredIT: Integrated tuning of data preprocessing and time series prediction models.- A guide to the Tucker tensor decomposition for data mining: exploratory analysis, clustering and classification.- Challenges for Healthcare Data Analytics over Knowledge Graphs.- From Database Repairs to Causality in Databases and Beyond.

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Book SynopsisAnwendungssoftware soll zur Optimierung von Geschäftsprozessen beitragen. Vor diesem Hintergrund werden in diesem Lehrbuch anhand eines durchgängigen Fallbeispiels Geschäftsprozesse mit UML modelliert und die fachlichen Anforderungen an das Software-System systematisch abgeleitet. Aufgrund nachvollziehbarer Regeln und Prinzipien werden Klassenmodelle konstruiert. Alternative System-Architekturen werden vorgestellt und erläutert. Es werden Entwurfsmuster anhand anschaulicher Beispiele präsentiert und die Umsetzung des Fallbeispiels in Java anhand von annotiertem Code gezeigt.Table of ContentsObjektorientierte Software-Entwicklung - Geschäftsprozessmodellierung und Anforderungsanalyse - Analyse und Entwurf in der Spezifikationsphase - Gestaltung der Mensch-Computer-Interaktion - CASE und MDA - Entwurf der Systemarchitektur - Ausgewählte Entwurfsmuster mit Beispielen - Anwendungsbeispiel

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Book SynopsisDeep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations. The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.Table of Contents

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Book SynopsisThis book discusses various aspects of text data mining. Unlike other books that focus on machine learning or databases, it approaches text data mining from a natural language processing (NLP) perspective. The book offers a detailed introduction to the fundamental theories and methods of text data mining, ranging from pre-processing (for both Chinese and English texts), text representation and feature selection, to text classification and text clustering. It also presents the predominant applications of text data mining, for example, topic modeling, sentiment analysis and opinion mining, topic detection and tracking, information extraction, and automatic text summarization. Bringing all the related concepts and algorithms together, it offers a comprehensive, authoritative and coherent overview. Written by three leading experts, it is valuable both as a textbook and as a reference resource for students, researchers and practitioners interested in text data mining. It can also be used for classes on text data mining or NLP.Table of ContentsChapter 1. Introduction.- Chapter 2. Data Annotation and Preprocessing.- Chapter 3. Text Representation.- Chapter 4. Text Representation with Pretraining and Fine-tuning.- Chapter 5. Text classification.- Chapter 6. Text Clustering.- Chapter 7. Topic Model.- Chapter 8. Sentiment Analysis and Opinion Mining.- Chapter 9. Topic Detection and Tracking.- Chapter 10. Information Extraction.- Chapter 11. Automatic Text Summarization.

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Book SynopsisWith the proliferation of GPS devices in daily life, trajectory data that records where and when people move is now readily available on a large scale. As one of the most typical representatives, it has now become widely recognized that taxi trajectory data provides rich opportunities to enable promising smart urban services. Yet, a considerable gap still exists between the raw data available, and the extraction of actionable intelligence. This gap poses fundamental challenges on how we can achieve such intelligence. These challenges include inaccuracy issues, large data volumes to process, and sparse GPS data, to name but a few. Moreover, the movements of taxis and the leaving trajectory data are the result of a complex interplay between several parties, including drivers, passengers, travellers, urban planners, etc. In this book, we present our latest findings on mining taxi GPS trajectory data to enable a number of smart urban services, and to bring us one step closer to the vision of smart mobility. Firstly, we focus on some fundamental issues in trajectory data mining and analytics, including data map-matching, data compression, and data protection. Secondly, driven by the real needs and the most common concerns of each party involved, we formulate each problem mathematically and propose novel data mining or machine learning methods to solve it. Extensive evaluations with real-world datasets are also provided, to demonstrate the effectiveness and efficiency of using trajectory data. Unlike other books, which deal with people and goods transportation separately, this book also extends smart urban services to goods transportation by introducing the idea of crowdshipping, i.e., recruiting taxis to make package deliveries on the basis of real-time information. Since people and goods are two essential components of smart cities, we feel this extension is bot logical and essential. Lastly, we discuss the most important scientific problems and open issues in mining GPS trajectory data.Table of Contents1. Trajectory data map-matching 1.1 Introduction 1.2 Definitions and problem formulation 1.3 SD-Matching algorithm 1.4 Evaluations 1.5 Conclusions and discussions 2. Trajectory data compression 2.1 Introduction 2.2 Basic concepts and system overview 2.3 HCC algorithm 2.4 System implementation 2.5 Evaluations 2.6 Conclusions 3. Trajectory data protection 3.1 Introduction 3.2 Preliminary 3.3 Trajectory protection mechanism 3.4 Performance evaluations 3.5 Conclusions Part II: Enabling Smart Urban Services: Travellers 4. TripPlanner: Personalized trip planning leveraging heterogeneous trajectory data 4.1 Introduction 4.2 TripPlanner System 4.3 Dynamic network modelling 4.4 The two-phase approach 4.5 System evaluations 4.6 Conclusions and future work 5. ScenicPlanner: Recommending the most beautiful driving routes 5.1 Introduction 5.2 Preliminary 5.3 The two-phase approach 5.4 Experimental evaluations 5.5 Conclusion and future work Part III: Enabling Smart Urban Services: Drivers 6. GreenPlanner: Planning fuel-efficient driving routes 6.1 Introduction 6.2 Basic concepts and problem formulation 6.3 Personal fuel consumption model building 6.4 Fuel-efficient driving route planning 6.5 Evaluations 6.6 Conclusions and future work 7. Hunting or waiting: Earning more by understanding taxi service strategies 7.1 Introduction 7.2 Empirical study 7.3 Taxi strategy formulation 7.4 Understanding taxi service strategies 7.5 Conclusions Part IV: Enabling Smart Urban Services: Passengers 8. iBOAT: Real-time detection of anomalous taxi trajectories from GPS traces 8.1 Introduction 8.2 Preliminaries and problem definition 8.3 Isolation-based online anomalous trajectory detection 8.4 Empirical evaluations 8.5 Fraud behaviour analysis 8.6 Conclusions and future work 9. Real-Time imputing trip purpose leveraging heterogeneous trajectory data 9.1 Introduction 9.2 Basic concepts and problem statement 9.3 Imputing trip purposes 9.4 Enabling real-time response 9.5 Evaluations 9.6 Conclusions and future work Part V: Enabling Smart Urban Services: Urban Planners 10. GPS environment friendliness estimation with trajectory data 10.1 Introduction 10.2 Basic concepts 10.3 Methodology 10.4 Experiments 10.5 Limitations and future work 10.6 Conclusions 11. B-Planner: Planning night bus routes using taxi trajectory data 11.1 Introduction 11.2 Candidate bus stop identification 11.3 Bus route selection 11.4 Experimental evaluations 11.5 Conclusions and future work 12. VizTripPurpose: Understanding city-wide passengers’ travel behaviours 12.1 Introduction 12.2 System overview 12.3 Trip2Vec model 12.4 User interfaces 12.5 Case studies 12.6 Conclusions and future work Part VI: Enabling Smart Urban Services: Beyond People Transportation 13. CrowdDeliver: Arriving as soon as possible 13.1 Introduction 13.2 Basic concepts, assumptions and problem statement 13.3 Overview of CrowdDeliver 13.4 Two-phase approach 13.5 Evaluations 13.6 Conclusions and future work 14. CrowdExpress: Arriving by the user-specified deadline 14.1 Introduction 14.2 Preliminary, problem statement and system overview 14.3 Offline package transport network building 14.4 Online taxi scheduling and package routing 14.5 Experimental evaluations 14.6 Conclusions and future work Part VII: Open Issues and Conclusions 15. Open Issues 16. Conclusions

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