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Engineering Dependable And Secure Machine Learning Systems
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Book Synopsis Engineering Dependable and Secure Machine Learning Systems by : Onn Shehory
Download or read book Engineering Dependable and Secure Machine Learning Systems written by Onn Shehory and published by Springer Nature. This book was released on 2020-11-07 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Security Engineering by : Ross Anderson
Download or read book Security Engineering written by Ross Anderson and published by John Wiley & Sons. This book was released on 2020-12-22 with total page 1232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now that there’s software in everything, how can you make anything secure? Understand how to engineer dependable systems with this newly updated classic In Security Engineering: A Guide to Building Dependable Distributed Systems, Third Edition Cambridge University professor Ross Anderson updates his classic textbook and teaches readers how to design, implement, and test systems to withstand both error and attack. This book became a best-seller in 2001 and helped establish the discipline of security engineering. By the second edition in 2008, underground dark markets had let the bad guys specialize and scale up; attacks were increasingly on users rather than on technology. The book repeated its success by showing how security engineers can focus on usability. Now the third edition brings it up to date for 2020. As people now go online from phones more than laptops, most servers are in the cloud, online advertising drives the Internet and social networks have taken over much human interaction, many patterns of crime and abuse are the same, but the methods have evolved. Ross Anderson explores what security engineering means in 2020, including: How the basic elements of cryptography, protocols, and access control translate to the new world of phones, cloud services, social media and the Internet of Things Who the attackers are – from nation states and business competitors through criminal gangs to stalkers and playground bullies What they do – from phishing and carding through SIM swapping and software exploits to DDoS and fake news Security psychology, from privacy through ease-of-use to deception The economics of security and dependability – why companies build vulnerable systems and governments look the other way How dozens of industries went online – well or badly How to manage security and safety engineering in a world of agile development – from reliability engineering to DevSecOps The third edition of Security Engineering ends with a grand challenge: sustainable security. As we build ever more software and connectivity into safety-critical durable goods like cars and medical devices, how do we design systems we can maintain and defend for decades? Or will everything in the world need monthly software upgrades, and become unsafe once they stop?
Download or read book Beyond Algorithms written by James Luke and published by CRC Press. This book was released on 2022-05-29 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focuses on the definition, engineering, and delivery of AI solutions as opposed to AI itself Reader will still gain a strong understanding of AI, but through the perspective of delivering real solutions Explores the core AI issues that impact the success of an overall solution including i. realities of dealing with data, ii. impact of AI accuracy on the ability of the solution to meet business objectives, iii. challenges in managing the quality of machine learning models Includes real world examples of enterprise scale solutions Provides a series of (optional) technical deep dives and thought experiments.
Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Ulf Brefeld
Download or read book Machine Learning and Knowledge Discovery in Databases written by Ulf Brefeld and published by Springer Nature. This book was released on 2020-05-01 with total page 799 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Developing and Monitoring Smart Environments for Intelligent Cities by : Mahmood, Zaigham
Download or read book Developing and Monitoring Smart Environments for Intelligent Cities written by Mahmood, Zaigham and published by IGI Global. This book was released on 2020-11-20 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, intelligent cities, also known as smart cities or cognitive cities, have become a perceived solution for improving the quality of life of citizens while boosting the efficiency of city services and processes. This new vision involves the integration of various sectors of society through the use of the internet of things. By continuing to enhance research for the better development of the smart environments needed to sustain intelligent cities, citizens will be empowered to provision the e-services provided by the city, city officials will have the ability to interact directly with the community as well as monitor digital environments, and smart communities will be developed where citizens can enjoy improved quality of life. Developing and Monitoring Smart Environments for Intelligent Cities compiles the latest research on the development, management, and monitoring of digital cities and intelligent environments into one complete reference source. The book contains chapters that examine current technologies and the future use of internet of things frameworks as well as device connectivity approaches, communication protocols, security challenges, and their inherent issues and limitations. Including unique coverage on topics such as connected vehicles for smart transportation, security issues for smart homes, and building smart cities for the blind, this reference is ideal for practitioners, urban developers, urban planners, academicians, researchers, and students.
Download or read book Federated Learning written by Qiang Yang and published by Springer Nature. This book was released on 2020-11-25 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
Book Synopsis Theory and Engineering of Dependable Computer Systems and Networks by : Wojciech Zamojski
Download or read book Theory and Engineering of Dependable Computer Systems and Networks written by Wojciech Zamojski and published by Springer Nature. This book was released on 2021-05-26 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Digital Image Enhancement and Reconstruction by : Shyam Singh Rajput
Download or read book Digital Image Enhancement and Reconstruction written by Shyam Singh Rajput and published by Academic Press. This book was released on 2022-10-06 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Computational Science – ICCS 2019 by : João M. F. Rodrigues
Download or read book Computational Science – ICCS 2019 written by João M. F. Rodrigues and published by Springer. This book was released on 2019-06-07 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Deep Learning on Graphs written by Yao Ma and published by Cambridge University Press. This book was released on 2021-09-23 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive text on foundations and techniques of graph neural networks with applications in NLP, data mining, vision and healthcare.
Book Synopsis Dependable Computing by : Ravishankar K. Iyer
Download or read book Dependable Computing written by Ravishankar K. Iyer and published by John Wiley & Sons. This book was released on 2024-04-18 with total page 852 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Dependable Embedded Systems by : Jörg Henkel
Download or read book Dependable Embedded Systems written by Jörg Henkel and published by Springer Nature. This book was released on 2020-12-09 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems.
Book Synopsis Cognitive Engineering for Next Generation Computing by : Kolla Bhanu Prakash
Download or read book Cognitive Engineering for Next Generation Computing written by Kolla Bhanu Prakash and published by John Wiley & Sons. This book was released on 2021-03-19 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: The cognitive approach to the IoT provides connectivity to everyone and everything since IoT connected devices are known to increase rapidly. When the IoT is integrated with cognitive technology, performance is improved, and smart intelligence is obtained. Discussed in this book are different types of datasets with structured content based on cognitive systems. The IoT gathers the information from the real time datasets through the internet, where the IoT network connects with multiple devices. This book mainly concentrates on providing the best solutions to existing real-time issues in the cognitive domain. Healthcare-based, cloud-based and smart transportation-based applications in the cognitive domain are addressed. The data integrity and security aspects of the cognitive computing main are also thoroughly discussed along with validated results.
Book Synopsis AI for Large Scale Communication Networks by : Kanthavel, R.
Download or read book AI for Large Scale Communication Networks written by Kanthavel, R. and published by IGI Global. This book was released on 2024-10-25 with total page 534 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) is rapidly becoming essential to large-scale communication networks. Driven by the need for greater efficiency, security, and optimization, AI has evolved into a powerful tool that processes vast data and delivers insights through real-time processing, predictive analysis, and adaptive learning. Because these advancements transform how we interact with data and services, applying AI to complex networks has never been more essential. AI for Large Scale Communication Networks explores how AI can enhance network performance, scalability, and security. With contributions from experts, this book covers topics such as algorithm optimization, machine learning improvements, and neural network applications. It also addresses critical challenges like fault tolerance and distributed computing, emphasizing the need for interdisciplinary collaboration. Designed for academics, practitioners, and students, this resource provides actionable insights and strategies to optimize communication networks using AI.
Book Synopsis Machine Learning Techniques and Analytics for Cloud Security by : Rajdeep Chakraborty
Download or read book Machine Learning Techniques and Analytics for Cloud Security written by Rajdeep Chakraborty and published by John Wiley & Sons. This book was released on 2021-12-21 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: MACHINE LEARNING TECHNIQUES AND ANALYTICS FOR CLOUD SECURITY This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions The aim of Machine Learning Techniques and Analytics for Cloud Security is to integrate machine learning approaches to meet various analytical issues in cloud security. Cloud security with ML has long-standing challenges that require methodological and theoretical handling. The conventional cryptography approach is less applied in resource-constrained devices. To solve these issues, the machine learning approach may be effectively used in providing security to the vast growing cloud environment. Machine learning algorithms can also be used to meet various cloud security issues, such as effective intrusion detection systems, zero-knowledge authentication systems, measures for passive attacks, protocols design, privacy system designs, applications, and many more. The book also contains case studies/projects outlining how to implement various security features using machine learning algorithms and analytics on existing cloud-based products in public, private and hybrid cloud respectively. Audience Research scholars and industry engineers in computer sciences, electrical and electronics engineering, machine learning, computer security, information technology, and cryptography.
Book Synopsis Sustainable Development Using Private AI by : Uma Maheswari V
Download or read book Sustainable Development Using Private AI written by Uma Maheswari V and published by CRC Press. This book was released on 2024-08-27 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the fundamental concepts of private AI and its applications. It also covers fusion of Private AI with cutting-edge technologies like cloud computing, federated learning and computer vision. Security Models and Applications for Sustainable Development Using Private AI reviews various encryption algorithms used for providing security in private AI. It discusses the role of training machine learning and Deep learning technologies in private AI. The book provides case studies of using private AI in various application areas such as purchasing, education, entertainment, medical diagnosis, predictive care, conversational personal assistants, wellness apps, early disease detection, and recommendation systems. The authors provide additional knowledge to handling the customer’s data securely and efficiently. It also provides multi-model dataset storage approaches along with the traditional approaches like anonymization of data and differential privacy mechanisms. The target audience includes undergraduate and postgraduate students in Computer Science, Information technology, Electronics and Communication Engineering and related disciplines. This book is also a one stop reference point for professionals, security researchers, scholars, various government agencies and security practitioners, and experts working in the cybersecurity Industry specifically in the R & D division.
Book Synopsis Machine Learning and Principles and Practice of Knowledge Discovery in Databases by : Irena Koprinska
Download or read book Machine Learning and Principles and Practice of Knowledge Discovery in Databases written by Irena Koprinska and published by Springer Nature. This book was released on 2023-01-30 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the papers of several workshops which were held in conjunction with the International Workshops of ECML PKDD 2022 on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022, held in Grenoble, France, during September 19–23, 2022. The 73 revised full papers and 6 short papers presented in this book were carefully reviewed and selected from 143 submissions. ECML PKDD 2022 presents the following five workshops: Workshop on Data Science for Social Good (SoGood 2022) Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2022) Workshop on Explainable Knowledge Discovery in Data Mining (XKDD 2022) Workshop on Uplift Modeling (UMOD 2022) Workshop on IoT, Edge and Mobile for Embedded Machine Learning (ITEM 2022) Workshop on Mining Data for Financial Application (MIDAS 2022) Workshop on Machine Learning for Cybersecurity (MLCS 2022) Workshop on Machine Learning for Buildings Energy Management (MLBEM 2022) Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2022) Workshop on Data Analysis in Life Science (DALS 2022) Workshop on IoT Streams for Predictive Maintenance (IoT-PdM 2022)
