الصفحة 96
الصفحة 96
Big data-enabled internet of things
Covers analytical techniques for handling the huge amount of data generated by the Internet of Things, from architectures and platforms to security and privacy issues, applications, and challenges as well as future directions. The fields of Big Data and the Internet of Things (IoT) have seen tremendous advances, developments, and growth in recent years. The IoT is the inter-networking of connected smart devices, buildings, vehicles and other items which are embedded with electronics, software, sensors and actuators, and network connectivity that enable these objects to collect and exchange data. The IoT produces a lot of data. Big data describes very large and complex data sets that traditional data processing application software is inadequate to deal with, and the use of analytical methods to extract value from data. This edited book covers analytical techniques for handling the huge amount of data generated by the Internet of Things, from architectures and platforms to security and privacy issues, applications, and challenges as well as future directions.
Big Data Recommender Systems ; Vol.2 : Application Paradigms
Combines experimental and theoretical research on big data recommender systems to help computer scientists develop new concepts and methodologies for complex applications. It includes original scientific contributions in the form of theoretical foundations, comparative analysis, surveys, case studies, techniques and tools. First designed to generate personalized recommendations to users in the 90s, recommender systems apply knowledge discovery techniques to users’ data to suggest information, products, and services that best match their preferences. In recent decades, we have seen an exponential increase in the volumes of data, which has introduced many new challenges. recommender systems. Volume 2 covers a broad range of application paradigms for recommender systems over 22 chapters
Big Data Recommender Systems ; Vol.1 : Algorithms, Architectures, Big Data, Security and Trust
Combines experimental and theoretical research on big data recommender systems to help computer scientists develop new concepts and methodologies for complex applications. It includes original scientific contributions in the form of theoretical foundations, comparative analysis, surveys, case studies, techniques and tools.
Big data analysis of nanoscience bibliometrics, patent, and funding data (2000-2019)
Presents an evaluation of nanotechnologies outputs (academic outputs and patents) and their impact from 2000-2019. The evaluation uses Elsevier’s Scopus (the largest abstract and citation database of peer-reviewed literature), SciVal (a scientific research analysis platform), Funding Institutional (a funding database), and PatentSight (a patent analysis platform). It covers four key topics regarding nanoscience research, including: 1) An overview of nano-related scholarly output, 2) Nanoscience and its contribution to basic science, 3) Nanoscience and its impact on and collaboration with industry partners, and 4) Key factors that promote the development of nanoscience.
Big Data : Conceptual Analysis and Applications
The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data. To solve these problems, a group of new methods and tools is used, based on the self-organization of computational processes, the use of crisp and fuzzy cluster analysis methods, hybrid neural-fuzzy networks, and others. The book solves various practical problems. In particular, for the tasks of 3D image recognition and automatic speech recognition large-scale neural networks with applications for Deep Learning systems were used.
Big Data – BigData 2020; 9th International Conference, Held as Part of the Services Conference Federation, SCF 2020, Honolulu, HI, USA, September 18-20, 2020, Proceedings
Constitutes the proceedings of the 9th International Conference on Big Data, BigData 2020, held as part of SCF 2020, during September 18-20, 2020. The conference was planned to take place in Honolulu, HI, USA and was changed to a virtual format due to the COVID-19 pandemic. The 16 full and 3 short papers presented were carefully reviewed and selected from 52 submissions. The topics covered are Big Data Architecture, Big Data Modeling, Big Data As A Service, Big Data for Vertical Industries (Government, Healthcare, etc.), Big Data Analytics, Big Data Toolkits, Big Data Open Platforms, Economic Analysis, Big Data for Enterprise Transformation, Big Data in Business Performance Management, Big Data for Business Model Innovations and Analytics, Big Data in Enterprise Management Models and Practices, Big Data in Government Management Models and Practices, and Big Data in Smart Planet Solutions.
Bidding Strategies in Agent-Based Continuous Double Auctions
Presents a new bidding strategy for agents to adopt in CDAs and propose tools to enhance the performance of existing bidding strategies in CDAs. The superior performance of the new bidding strategy as well as the tools presented in this book are illustrated through extensive experiments.
Bézier and Splines in image processing and machine vision
Digital image processing and machine vision have grown considerably during the last few decades. Of the various techniques, developed so far splines play a positive and significant role in many of them. This book deals with various image processing and machine vision problems efficiently with splines.
Beyond the Worst-Case Analysis of Algorithms
There are no silver bullets in algorithm design, and no single algorithmic idea is powerful and flexible enough to solve every computational problem. Nor are there silver bullets in algorithm analysis, as the most enlightening method for analyzing an algorithm often depends on the problem and the application. However, typical algorithms courses rely almost entirely on a single analysis framework, that of worst-case analysis, wherein an algorithm is assessed by its worst performance on any input of a given size. The purpose of this book is to popularize several alternatives to worst-case analysis and their most notable algorithmic applications, from clustering to linear programming to neural network training. Forty leading researchers have contributed introductions to different facets of this field, emphasizing the most important models and results, many of which can be taught in lectures to beginning graduate students in theoretical computer science and machine learning.
Best practices in software measurement : How to use metrics to improve project and process performance
Not everything that counts can be counted. Not everything that is counted counts. Albert Einstein This is a book about software measurement from the practitioner’s point of view and it is a book for practitioners. Software measurement needs a lot of practical guidance to build upon experiences and to avoid repeating errors. This book t- gets exactly this need, namely to share experiences in a constructive way that can be followed. It tries to summarize experiences and knowledge about software measurement so that it is applicable and repeatable. It extracts experiences and lessons learned from the narrow context of the specific industrial situation, thus facilitating transfer to other contexts. Software measurement is not at a standstill. With the speed software engine- ing is evolving, software measurement has to keep pace. While the underlying theory and basic principles remain invariant in the true sense (after all, they are not specific to software engineering), the application of measurement to specific contexts and situations is continuously extended. The book thus serves as a ref- ence on these invariant principles as well as a practical guidance on how to make software measurement a success.
Beginning Scala 3 : A functional and Object-Oriented Java Language
Introduces you to the Scala programming language, its object-oriented and functional programming characteristics, and then guides you through Scala constructs and libraries that allow you to assemble small components into high-performance, scalable systems. You will understand why Scala is judiciously used for critical business applications by leading companies such as Twitter, LinkedIn, Foursquare, the Guardian, Morgan Stanley, Credit Suisse, UBS, and HSBC – and you will be able to use it in your own projects. You will: Get started with Scala 3 or Scala language programming in general / Understand how to utilitze OOP in Scala / Perform functional programming in Scala / Master the use of Scala collections, traits and implicits / Leverage Java and Scala interopability / Employ Scala for DSL programming / Use patterns and best practices in Scala
Beginning Java Data Structures and Algorithms : Sharpen your problem solving skills by learning core computer science concepts in a pain-free manner
Teaches you tools that you can use to build efficient applications. It starts with an introduction to algorithms and big O notation, later explains bubble, merge, quicksort, and other popular programming patterns. You’ll also learn about data structures such as binary trees, hash tables, and graphs. The book progresses to advanced concepts, such as algorithm design paradigms and graph theory. By the end of the book, you will know how to correctly implement common algorithms and data structures within your applications.
Beginning Java 17 Fundamentals : Object-Oriented Programming in Java 17
Learn the fundamentals of the Java 17 LTS or Java Standard Edition version 17 Long Term Support release, including basic programming concepts and the object-oriented fundamentals necessary at all levels of Java development. You will: Write your first Java programs with emphasis on learning object-oriented programming / How to work with switch expressions, value types (records), local variable type inference, pattern matching switch and more from Java 17 / Handle exceptions, assertions, strings and dates, and object formatting / Learn about how to define and use modules / Dive in depth into classes, interfaces, and inheritance in Java / Use regular expressions / Take advantage of the JShell REPL tool
Bayesian Networks and Decision Graphs
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams.It contians two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems.
Bayesian Methods in the Search for MH370
This book demonstrates how nonlinear/non-Gaussian Bayesian time series estimation methods were used to produce a probability distribution of potential MH370 flight paths. It provides details of how the probabilistic models of aircraft flight dynamics, satellite communication system measurements, environmental effects and radar data were constructed and calibrated. The probability distribution was used to define the search zone in the southern Indian Ocean. The book describes particle-filter based numerical calculation of the aircraft flight-path probability distribution and validates the method using data from several of the involved aircraft’s previous flights. Finally it is shown how the Reunion Island flaperon debris find affects the search probability distribution.
Bayesian core : A practical approach to computational Bayesian statistics
This Bayesian modeling book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models.
Barriers and Biases in Computer-Mediated Knowledge Communication : And How They May Be Overcome
This books deals with computer-mediated cooperation and communication scenarios in teaching and learning situations, leisure activities (e.g. laypersons looking for expert information on the internet), and net-based communication at work. Such scenarios will become increasingly important. But the successful use of such computer-mediated settings is not trivial. Cooperative learning and work itself requires special skills and strategies. And the technical settings with sometimes restricted, sometimes new possibilities for communication add problems on top of the cooperation itself. What are the barriers in computer-mediated communication for cooperative learning and work? Which are the most relevant biases in computer-mediated information processing? Based on empirical research the contributors from psychology, education and computer sciences offer different perspectives on the nature and causes of such barriers.
Balancing Agility and Formalism in Software Engineering ; 2nd IFIP TC 2 Central and East European Conference on Software Engineering Techniques, CEE-SET 2007, Poznan, Poland, October 10-12, 2007, Revised Selected Papers
This book constitutes the thoroughly refereed post-conference proceedings of the Second IFIP TC 2 Central and East Conference on Software Engineering Techniques, CEE-SET 2007, held in Poznan, Poland, in October 2007.
Balanced Website Design : Optimising Aesthetics, Usability and Purpose
Balanced Website Design (BWD) is a new methodology that fuses the strengths of traditional structured, stepped, and iterative approaches with a sharp focus on defining and achieving the desired characteristics of purpose, usability and aesthetics – absolutely essential requirements for any website. The book includes discussions of new perspectives on usability and aesthetics in the special context of website design. BWD is suitable for all types of websites, for individual and/or team projects, and should prove to be of significant value for even the most experienced of website designers. BWD provides guidance, structure and detailed documentation/process support for the activity of designing and implementing your next website – helping you to maximise its effectiveness and relevance.
B 2007 : Formal Specification and Development in B ; 7th International Conference of B Users, Besancon, France, January 7-19, 2007, Proceedings
These proceedingsrecordthe papers presented at the Seventh InternationalC- ference of B Users (B 2007), held in the city of Besan¸ con in the east of France. All the submitted papers in these proceedings were peer reviewed by at least three reviewers drawn from the B committee, depending on the subject matter of the paper. The authorsof the papersforB 2007werefrom Australia,Canada, Finland, Germany, France, Switzerland, and the UK. The conference featured a rangeof contributions by distinguished invited speakers drawn from both ind- try and academia.
