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This book presents recent research in the field of reuse and integration, and will help researchers and practitioners alike to understand how they can implement reuse in different stages of software development and in various domains, from robotics and security authentication to environmental issues. Indeed, reuse is not only confined to reusing code; it can be included in every software development step. The challenge today is more about adapting solutions from one language to another, or from one domain to another. The relative validation of the reused artifacts in their new environment is also necessary, at time even critical. The book includes high-quality research papers on these and many other aspects, written by experts in information reuse and integration, who cover the latest advances in the field. Their contributions are extended versions of the best papers presented at the IEEE International Conference on Information Reuse and Integration (IRI) and IEEE International Workshop on Formal Methods Integration (FMI), which were held in San Diego in August 2017.
This book presents recent research in the field of reuse and integration, and will help researchers and practitioners alike to understand how they can implement reuse in different stages of software development and in various domains, from robotics and security authentication to environmental issues. Indeed, reuse is not only confined to reusing code; it can be included in every software development step. The challenge today is more about adapting solutions from one language to another, or from one domain to another. The relative validation of the reused artifacts in their new environment is also necessary, at time even critical. The book includes high-quality research papers on these and many other aspects, written by experts in information reuse and integration, who cover the latest advances in the field. Their contributions are extended versions of the best papers presented at the IEEE International Conference on Information Reuse and Integration (IRI) and IEEE International Workshop on Formal Methods Integration (FMI), which were held in San Diego in August 2017.
This book presents recent research in the field of reuse and integration, and will help researchers and practitioners alike to understand how they can implement reuse in different stages of software development and in various domains, from robotics and security authentication to environmental issues. Indeed, reuse is not only confined to reusing code; it can be included in every software development step. The challenge today is more about adapting solutions from one language to another, or from one domain to another. The relative validation of the reused artifacts in their new environment is also necessary, at time even critical. The book includes high-quality research papers on these and many other aspects, written by experts in information reuse and integration, who cover the latest advances in the field. Their contributions are extended versions of the best papers presented at the IEEE International Conference on Information Reuse and Integration (IRI) and IEEE International Workshop on Formal Methods Integration (FMI), which were held in San Diego in August 2017.
The book is based on the best papers of IEEE IRI 2018 and IEEE FMI 2018, Salt Lake City, July, 2018. They have been enhanced and modified suitably for publication. The book comprises recent works covering several aspects of reuse in intelligent systems – including Scientific Theory and Technology-Based Applications. New data analytic algorithms, technologies, and tools are sought to be able to manage, integrate, and utilize large amounts of data despite hardware, software, and/or bandwidth constraints; to construct models yielding important data insights, and to create visualizations to aid in presenting and understanding the data. Furthermore, it addresses the representation, cleansing, generalization, validation, and reasoning strategies for the scientifically-sound and cost-effective advancement of all kinds of intelligent systems – including all software and hardware aspects. The book addresses problems such as, how to optimally select the information/data sets for reuse and how to optimize the integration of existing information/knowledge with new, developing information/knowledge sources!
This book will focus on utilizing statistical modelling of the software source code, in order to resolve issues associated with the software development processes. Writing and maintaining software source code is a costly business; software developers need to constantly rely on large existing code bases. Statistical modelling identifies the patterns in software artifacts and utilize them for predicting the possible issues.
Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.
An Integrated Approach to Managing the World's Water Resources Water Reuse: Issues, Technologies, and Applications equips water/wastewater students, engineers, scientists, and professionals with a definitive account of the latest water reclamation, recycling, and reuse theory and practice. This landmark textbook presents an integrated approach to all aspects of water reuse _ from public health protection to water quality criteria and regulations to advanced technology to implementation issues. Filled with over 500 detailed illustrations and photographs, Water Reuse: Issues, Technology, and Applications features: In-depth coverage of cutting-edge water reclamation and reuse applications Current issues and developments in public health and environmental protection criteria, regulations, and risk management Review of current advanced treatment technologies, new developments, and practices Special emphasis on process reliability and multiple barrier concepts approach Consideration of satellite and decentralized water reuse facilities Consideration of planning and public participation of water reuse Inside This Landmark Water/Wastewater Management Tool • Water Reuse: An Introduction • Health and Environmental Concerns in Water Reuse • Technologies and Systems for Water Reclamation and Reuse • Water Reuse Applications • Implementing Water Reuse
This two-volume set of LNCS 11643 and LNCS 11644 constitutes - in conjunction with the volume LNAI 11645 - the refereed proceedings of the 15th International Conference on Intelligent Computing, ICIC 2019, held in Nanchang, China, in August 2019. The 217 full papers of the three proceedings volumes were carefully reviewed and selected from 609 submissions. The ICIC theme unifies the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. The theme for this conference is “Advanced Intelligent Computing Methodologies and Applications.” Papers related to this theme are especially solicited, including theories, methodologies, and applications in science and technology.
Principles of Data Integration is the first comprehensive textbook of data integration, covering theoretical principles and implementation issues as well as current challenges raised by the semantic web and cloud computing. The book offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand. Readers will also learn how to build their own algorithms and implement their own data integration application. Written by three of the most respected experts in the field, this book provides an extensive introduction to the theory and concepts underlying today's data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. This text is an ideal resource for database practitioners in industry, including data warehouse engineers, database system designers, data architects/enterprise architects, database researchers, statisticians, and data analysts; students in data analytics and knowledge discovery; and other data professionals working at the R&D and implementation levels. Offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand Enables you to build your own algorithms and implement your own data integration applications
Machine Learning Theory and Applications Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and offers insights on executing machine learning applications across diverse hardware technologies such as CPUs, GPUs, and QPUs. Finally, the book explains how to deploy trained models through containerized applications using Kubernetes and OpenShift, as well as their integration through machine learning operations (MLOps). Additional topics covered in Machine Learning Theory and Applications include: Current use cases of AI, including making predictions, recognizing images and speech, performing medical diagnoses, creating intelligent supply chains, natural language processing, and much more Classical and quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, quantum neural networks, and quantum generative adversarial networks (qGANs) Different ways to manipulate data, such as handling missing data, analyzing categorical data, or processing time-related data Feature rescaling, extraction, and selection, and how to put your trained models to life and production through containerized applications Machine Learning Theory and Applications is an essential resource for data scientists, engineers, and IT specialists and architects, as well as students in computer science, mathematics, and bioinformatics. The reader is expected to understand basic Python programming and libraries such as NumPy or Pandas and basic mathematical concepts, especially linear algebra.