Engineering Dependable And Secure Machine Learning Systems

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Engineering Dependable And Secure Machine Learning Systems

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Author : Onn Shehory
language : en
Publisher: Springer Nature
Release Date : 2020-11-07

Engineering Dependable And Secure Machine Learning Systems written by Onn Shehory and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-07 with Computers categories.

This book constitutes the revised selected papers of the Third International Workshop on Engineering Dependable and Secure Machine Learning Systems, EDSMLS 2020, held in New York City, NY, USA, in February 2020. The 7 full papers and 3 short papers were thoroughly reviewed and selected from 16 submissions. The volume presents original research on dependability and quality assurance of ML software systems, adversarial attacks on ML software systems, adversarial ML and software engineering, etc.

Beyond Algorithms

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Author : James Luke
language : en
Publisher: CRC Press
Release Date : 2022-05-29

Beyond Algorithms written by James Luke and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-29 with Computers categories.

With so much artificial intelligence (AI) in the headlines, it is no surprise that businesses are scrambling to exploit this exciting and transformative technology. Clearly, those who are the first to deliver business-relevant AI will gain significant advantage. However, there is a problem! Our perception of AI success in society is primarily based on our experiences with consumer applications from the big web companies. The adoption of AI in the enterprise has been slow due to various challenges. Business applications address far more complex problems and the data needed to address them is less plentiful. There is also the critical need for alignment of AI with relevant business processes. In addition, the use of AI requires new engineering practices for application maintenance and trust. So, how do you deliver working AI applications in the enterprise? Beyond Algorithms: Delivering AI for Business answers this question. Written by three engineers with decades of experience in AI (and all the scars that come with that), this book explains what it takes to define, manage, engineer, and deliver end-to-end AI applications that work. This book presents: Core conceptual differences between AI and traditional business applications A new methodology that helps to prioritise AI projects and manage risks Practical case studies and examples with a focus on business impact and solution delivery Technical Deep Dives and Thought Experiments designed to challenge your brain and destroy your weekends

Machine Learning And Knowledge Discovery In Databases

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Author : Ulf Brefeld
language : en
Publisher: Springer Nature
Release Date : 2020-05-01

Machine Learning And Knowledge Discovery In Databases written by Ulf Brefeld and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-01 with Computers categories.

The three volume proceedings LNAI 11906 – 11908 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, held in Würzburg, Germany, in September 2019. The total of 130 regular papers presented in these volumes was carefully reviewed and selected from 733 submissions; there are 10 papers in the demo track. The contributions were organized in topical sections named as follows: Part I: pattern mining; clustering, anomaly and outlier detection, and autoencoders; dimensionality reduction and feature selection; social networks and graphs; decision trees, interpretability, and causality; strings and streams; privacy and security; optimization. Part II: supervised learning; multi-label learning; large-scale learning; deep learning; probabilistic models; natural language processing. Part III: reinforcement learning and bandits; ranking; applied data science: computer vision and explanation; applied data science: healthcare; applied data science: e-commerce, finance, and advertising; applied data science: rich data; applied data science: applications; demo track. Chapter "Heavy-tailed Kernels Reveal a Finer Cluster Structure in t-SNE Visualisations" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Theory And Engineering Of Dependable Computer Systems And Networks

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Author : Wojciech Zamojski
language : en
Publisher: Springer Nature
Release Date : 2021-05-26

Theory And Engineering Of Dependable Computer Systems And Networks written by Wojciech Zamojski and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-26 with Technology & Engineering categories.

This book contains papers on selected aspects of dependability analysis in computer systems and networks, which were chosen for discussion during the 16th DepCoS-RELCOMEX conference held in Wrocław, Poland, from June 28 to July 2, 2021. Their collection will be a valuable source material for scientists, researchers, practitioners and students who are dealing with design, analysis and engineering of computer systems and networks and must ensure their dependable operation. Being probably the most complex technical systems ever engineered by man (and also—the most dynamically evolving ones), organization of contemporary computer systems cannot be interpreted only as structures built on the basis of (unreliable) technical resources. Their evaluation must take into account a specific blend of interacting people (their needs and behaviours), networks (together with mobile properties, cloud organization, Internet of Everything, etc.) and a large number of users dispersed geographically and constantly producing an unconceivable number of applications. Ever-growing number of research methods being continuously developed for dependability analyses apply the newest techniques of artificial and computational intelligence. Selection of papers in these proceedings illustrates diversity of multi-disciplinary topics which are considered in present-day dependability explorations.

Neural Information Processing

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Author : Mufti Mahmud
language : en
Publisher: Springer Nature
Release Date : 2025-08-19

Neural Information Processing written by Mufti Mahmud and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-19 with Computers categories.

The sixteen-volume set, CCIS 2282-2297, constitutes the refereed proceedings of the 31st International Conference on Neural Information Processing, ICONIP 2024, held in Auckland, New Zealand, in December 2024. The 472 regular papers presented in this proceedings set were carefully reviewed and selected from 1301 submissions. These papers primarily focus on the following areas: Theory and algorithms; Cognitive neurosciences; Human-centered computing; and Applications.

Machine Learning Systems

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Author : Jeffrey Smith
language : en
Publisher: Simon and Schuster
Release Date : 2018-05-21

Machine Learning Systems written by Jeffrey Smith and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-21 with Computers categories.

Summary Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web app. Foreword by Sean Owen, Director of Data Science, Cloudera Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology If you’re building machine learning models to be used on a small scale, you don't need this book. But if you're a developer building a production-grade ML application that needs quick response times, reliability, and good user experience, this is the book for you. It collects principles and practices of machine learning systems that are dramatically easier to run and maintain, and that are reliably better for users. About the Book Machine Learning Systems: Designs that scale teaches you to design and implement production-ready ML systems. You'll learn the principles of reactive design as you build pipelines with Spark, create highly scalable services with Akka, and use powerful machine learning libraries like MLib on massive datasets. The examples use the Scala language, but the same ideas and tools work in Java, as well. What's Inside Working with Spark, MLlib, and Akka Reactive design patterns Monitoring and maintaining a large-scale system Futures, actors, and supervision About the Reader Readers need intermediate skills in Java or Scala. No prior machine learning experience is assumed. About the Author Jeff Smith builds powerful machine learning systems. For the past decade, he has been working on building data science applications, teams, and companies as part of various teams in New York, San Francisco, and Hong Kong. He blogs (https: //medium.com/@jeffksmithjr), tweets (@jeffksmithjr), and speaks (www.jeffsmith.tech/speaking) about various aspects of building real-world machine learning systems. Table of Contents PART 1 - FUNDAMENTALS OF REACTIVE MACHINE LEARNING Learning reactive machine learning Using reactive tools PART 2 - BUILDING A REACTIVE MACHINE LEARNING SYSTEM Collecting data Generating features Learning models Evaluating models Publishing models Responding PART 3 - OPERATING A MACHINE LEARNING SYSTEM Delivering Evolving intelligence

Computational Science Iccs 2019

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Author : João M. F. Rodrigues
language : en
Publisher: Springer
Release Date : 2019-06-07

Computational Science Iccs 2019 written by João M. F. Rodrigues and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-07 with Computers categories.

The five-volume set LNCS 11536, 11537, 11538, 11539 and 11540 constitutes the proceedings of the 19th International Conference on Computational Science, ICCS 2019, held in Faro, Portugal, in June 2019. The total of 65 full papers and 168 workshop papers presented in this book set were carefully reviewed and selected from 573 submissions (228 submissions to the main track and 345 submissions to the workshops). The papers were organized in topical sections named: Part I: ICCS Main Track Part II: ICCS Main Track; Track of Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Track of Agent-Based Simulations, Adaptive Algorithms and Solvers; Track of Applications of Matrix Methods in Artificial Intelligence and Machine Learning; Track of Architecture, Languages, Compilation and Hardware Support for Emerging and Heterogeneous Systems Part III: Track of Biomedical and Bioinformatics Challenges for Computer Science; Track of Classifier Learning from Difficult Data; Track of Computational Finance and Business Intelligence; Track of Computational Optimization, Modelling and Simulation; Track of Computational Science in IoT and Smart Systems Part IV: Track of Data-Driven Computational Sciences; Track of Machine Learning and Data Assimilation for Dynamical Systems; Track of Marine Computing in the Interconnected World for the Benefit of the Society; Track of Multiscale Modelling and Simulation; Track of Simulations of Flow and Transport: Modeling, Algorithms and Computation Part V: Track of Smart Systems: Computer Vision, Sensor Networks and Machine Learning; Track of Solving Problems with Uncertainties; Track of Teaching Computational Science; Poster Track ICCS 2019 Chapter “Comparing Domain-decomposition Methods for the Parallelization of Distributed Land Surface Models” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Digital Image Enhancement And Reconstruction

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Author : Shyam Singh Rajput
language : en
Publisher: Academic Press
Release Date : 2022-10-06

Digital Image Enhancement And Reconstruction written by Shyam Singh Rajput and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-06 with Computers categories.

Digital Image Enhancement and Reconstruction: Techniques and Applications explores different concepts and techniques used for the enhancement as well as reconstruction of low-quality images. Most real-life applications require good quality images to gain maximum performance, however, the quality of the images captured in real-world scenarios is often very unsatisfactory. Most commonly, images are noisy, blurry, hazy, tiny, and hence need to pass through image enhancement and/or reconstruction algorithms before they can be processed by image analysis applications. This book comprehensively explores application-specific enhancement and reconstruction techniques including satellite image enhancement, face hallucination, low-resolution face recognition, medical image enhancement and reconstruction, reconstruction of underwater images, text image enhancement, biometrics, etc. Chapters will present a detailed discussion of the challenges faced in handling each particular kind of image, analysis of the best available solutions, and an exploration of applications and future directions. The book provides readers with a deep dive into denoising, dehazing, super-resolution, and use of soft computing across a range of engineering applications. - Presents comprehensive coverage of digital image enhancement and reconstruction techniques - Explores applications across range of fields, including intelligent surveillance systems, human-computer interaction, healthcare, agriculture, biometrics, modelling - Explores different challenges and issues related to the implementation of various techniques for different types of images, including denoising, dehazing, super-resolution, and use of soft computing

Deep Learning For Multi Sensor Earth Observation

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Author : Sudipan Saha
language : en
Publisher: Elsevier
Release Date : 2025-02-03

Deep Learning For Multi Sensor Earth Observation written by Sudipan Saha and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-03 with Technology & Engineering categories.

Deep Learning for Multi-Sensor Earth Observation addresses the need for transformative Deep Learning techniques to navigate the complexity of multi-sensor data fusion. With insights drawn from the frontiers of remote sensing technology and AI advancements, it covers the potential of fusing data of varying spatial, spectral, and temporal dimensions from both active and passive sensors. This book offers a concise, yet comprehensive, resource, addressing the challenges of data integration and uncertainty quantification from foundational concepts to advanced applications. Case studies illustrate the practicality of deep learning techniques, while cutting-edge approaches such as self-supervised learning, graph neural networks, and foundation models chart a course for future development.Structured for clarity, the book builds upon its own concepts, leading readers through introductory explanations, sensor-specific insights, and ultimately to advanced concepts and specialized applications. By bridging the gap between theory and practice, this volume equips researchers, geoscientists, and enthusiasts with the knowledge to reshape Earth observation through the dynamic lens of deep learning. - Addresses the problem of unwieldy datasets from multi-sensor observations, applying Deep Learning to multi-sensor data integration from disparate sources with different resolution and quality - Provides a thorough foundational reference to Deep Learning applications for handling Earth Observation multi-sensor data across a variety of geosciences - Includes case studies and real-world data/examples allowing readers to better grasp how to put Deep Learning techniques and methods into practice

Dependable Computing

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Author : Ravishankar K. Iyer
language : en
Publisher: John Wiley & Sons
Release Date : 2024-04-18

Dependable Computing written by Ravishankar K. Iyer and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-18 with Computers categories.

Dependable Computing Covering dependability from software and hardware perspectives Dependable Computing: Design and Assessment looks at both the software and hardware aspects of dependability. This book: Provides an in-depth examination of dependability/fault tolerance topics Describes dependability taxonomy, and briefly contrasts classical techniques with their modern counterparts or extensions Walks up the system stack from the hardware logic via operating systems up to software applications with respect to how they are hardened for dependability Describes the use of measurement-based analysis of computing systems Illustrates technology through real-life applications Discusses security attacks and unique dependability requirements for emerging applications, e.g., smart electric power grids and cloud computing Finally, using critical societal applications such as autonomous vehicles, large-scale clouds, and engineering solutions for healthcare, the book illustrates the emerging challenges faced in making artificial intelligence (AI) and its applications dependable and trustworthy. This book is suitable for those studying in the fields of computer engineering and computer science. Professionals who are working within the new reality to ensure dependable computing will find helpful information to support their efforts. With the support of practical case studies and use cases from both academia and real-world deployments, the book provides a journey of developments that include the impact of artificial intelligence and machine learning on this ever-growing field. This book offers a single compendium that spans the myriad areas in which dependability has been applied, providing theoretical concepts and applied knowledge with content that will excite a beginner, and rigor that will satisfy an expert. Accompanying the book is an online repository of problem sets and solutions, as well as slides for instructors, that span the chapters of the book.