- Table View
- List View
Scalable Uncertainty Management: 12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings (Lecture Notes in Computer Science #11142)
by Davide Ciucci Barbara Vantaggi Gabriella PasiThis book constitutes the refereed proceedings of the 12th International Conference on Scalable Uncertainty Management, SUM 2018, which was held in Milan, Italy, in October 2018. The 23 full, 6 short papers and 2 tutorials presented in this volume were carefully reviewed and selected from 37 submissions. The conference is dedicated to the management of large amounts of complex, uncertain, incomplete, or inconsistent information. New approaches have been developed on imprecise probabilities, fuzzy set theory, rough set theory, ordinal uncertainty representations, or even purely qualitative models.
Scalable Uncertainty Management: 13th International Conference, SUM 2019, Compiègne, France, December 16–18, 2019, Proceedings (Lecture Notes in Computer Science #11940)
by Martin Theobald Nahla Ben Amor Benjamin QuostThis book constitutes the refereed proceedings of the 13th International Conference on Scalable Uncertainty Management, SUM 2019, which was held in Compiègne, France, in December 2019. The 25 full, 4 short, 4 tutorial, 2 invited keynote papers presented in this volume were carefully reviewed and selected from 44 submissions. The conference is dedicated to the management of large amounts of complex, uncertain, incomplete, or inconsistent information. New approaches have been developed on imprecise probabilities, fuzzy set theory, rough set theory, ordinal uncertainty representations, or even purely qualitative models.
Scalable Uncertainty Management: 14th International Conference, SUM 2020, Bozen-Bolzano, Italy, September 23–25, 2020, Proceedings (Lecture Notes in Computer Science #12322)
by Jesse Davis Karim TabiaThis book constitutes the refereed proceedings of the 14th International Conference on Scalable Uncertainty Management, SUM 2020, which was held in Bozen-Bolzano, Italy, in September 2020. The 12 full, 7 short papers presented in this volume were carefully reviewed and selected from 30 submissions. Besides that, the book also contains 2 abstracts of invited talks, 2 tutorial papers, and 2 PhD track papers. The conference aims to gather researchers with a common interest in managing and analyzing imperfect information from a wide range of fields, such as artificial intelligence and machine learning, databases, information retrieval and data mining, the semantic web and risk analysis. Due to the Corona pandemic SUM 2020 was held as an virtual event.
Scalable Uncertainty Management: 15th International Conference, SUM 2022, Paris, France, October 17–19, 2022, Proceedings (Lecture Notes in Computer Science #13562)
by Florence Dupin de Saint-Cyr Meltem Öztürk-Escoffier Nico PotykaThis book constitutes the refereed proceedings of the 15th International Conference on Scalable Uncertainty Management, SUM 2022, which was held in Paris, France, in October 2022.The 19 full and 4 short papers presented in this volume were carefully reviewed and selected from 25 submissions. Besides that, the book also contains 3 abstracts of invited talks and 2 tutorial papers. The conference aims to gather researchers with a common interest in managing and analyzing imperfect information from a wide range of fields, such as artificial intelligence and machine learning, databases, information retrieval and data mining, the semantic web and risk analysis.The chapter "Defining and Enforcing Descriptive Accuracy in Explanations: the Case of Probabilistic Classifiers" is licensed under the terms of the Creative Commons Attribution 4.0 International License.
Scalable Uncertainty Management: 16th International Conference, SUM 2024, Palermo, Italy, November 27–29, 2024, Proceedings (Lecture Notes in Computer Science #15350)
by Maria Vanina Martinez Sébastien Destercke Giuseppe SanfilippoThis book constitutes the refereed proceedings of the 16th International Conference on Scalable Uncertainty Management, SUM 2024, held in Palermo, Italy, during November 27–29, 2024. The 28 full and 7 short papers presented in this volume were carefully reviewed and selected from 43 submissions. SUM 2024 solicited three types of paper submissions: Long papers reporting on original research or providing surveys that synthesize current research trends, short papers describing promising work in progress, systems, or positions on controversial issues, and extended abstracts.
Scalable and Near-Optimal Design Space Exploration for Embedded Systems
by Francky Catthoor Angeliki Kritikakou Costas GoutisThis book describes scalable and near-optimal, processor-level design space exploration (DSE) methodologies. The authors present design methodologies for data storage and processing in real-time, cost-sensitive data-dominated embedded systems. Readers will be enabled to reduce time-to-market, while satisfying system requirements for performance, area, and energy consumption, thereby minimizing the overall cost of the final design.
Scalable and Secure Internet Services and Architecture (Chapman & Hall/CRC Computer and Information Science Series)
by Cheng-Zhong XuScalable and Secure Internet Services and Architecture provides an in-depth analysis of many key scaling technologies. Topics include: server clusters and load balancing; QoS-aware resource management; server capacity planning; Web caching and prefetching; P2P overlay network; mobile code and security; and mobility support for adaptive grid computi
Scalatra in Action
by Ross Baker David Hrycyszyn Stefan OllingerSummaryScalatra in Actionintroduces the Scalatra framework and the Sinatra model. It covers the framework in its entirety, starting with concepts like request routing, input handling, actions, and HTTP responses, then proceeds to more advanced topics, such as data access, handling heavy load, asynchronicity, securing applications, designing and documenting RESTful APIs, and real-time web programming.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyScalatra is a lightweight Scala web framework similar to the popular Ruby-based Sinatra. It’s perfect for running real-time applications on multicore servers, and is a fast way to spin up web apps and build HTTP APIs for mobile, Backbone.js, and AngularJS apps.About the BookScalatra in Actioncovers the Scalatra framework in its entirety, starting with concepts such as request routing, input handling, actions, and HTTP responses. For readers who don’t already know Scala, the book introduces the Scala language and sbt, the Simple Build Tool. You’ll learn how to use Scalatra’s powerful templating engine, Scalate. It also covers advanced topics such as data access, handling heavy load, asynchronicity, securing your application, designing RESTful APIs, and real-time web programming. What's InsideMake clean templates using ScalateIntegrate with libraries that supplement ScalatraWrite tests using Specs2Integrate Scalatra with databasesAbout the ReaderReaders should be familiar with the basics of HTTP, REST, and web applications. No experience with Scalatra, Sinatra, or Scala is required.About the AuthorsDave Hrycyszyn is technical director for a London-based agency specializing in agile software design and development. Stefan Ollinger is an active Scalatra contributor. Ross A. Baker is a Senior Cloud Engineer, a Scalate commiter, and organizer of the Indy Scala meetup.Table of ContentsPART 1 INTRODUCTION TO SCALATRAIntroduction A taste of Scalatra Routing Working with user input PART 2 COMMON DEVELOPMENT TASKS Handling JSON Handling files Server-side templating Testing Configuration, build, and deployment Working with a database PART 3 ADVANCED TOPICS Authentication Asynchronous programming Creating a RESTful JSON API with Swagger
Scale Space and Variational Methods in Computer Vision: 10th International Conference, SSVM 2025, Dartington, UK, May 18–22, 2025, Proceedings, Part I (Lecture Notes in Computer Science #15667)
by Carola-Bibiane Schönlieb Tatiana A. Bubba Romina Gaburro Silvia Gazzola Kostas Papafitsoros Marcelo PereyraThe two-volume set LNCS 15667 and 15668 constitutes the proceedings of the 10th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2025, which took place in Dartington, UK, in May 2025. The total of 63 full papers accepted in the proceedings were carefully reviewed and selected from 81 submissions. They were organized in topical sections as follows: Part I: Inverse Problems in Imaging; machine and deep learning in imaging; Part II: Optimization for imaging: theory and methods; scale space, PDES, flow, motion and registration.
Scale Space and Variational Methods in Computer Vision: 10th International Conference, SSVM 2025, Dartington, UK, May 18–22, 2025, Proceedings, Part II (Lecture Notes in Computer Science #15668)
by Carola-Bibiane Schönlieb Tatiana A. Bubba Romina Gaburro Silvia Gazzola Kostas Papafitsoros Marcelo PereyraThe two-volume set LNCS 15667 and 15668 constitutes the proceedings of the 10th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2025, which took place in Dartington, UK, in May 2025. The total of 63 full papers accepted in the proceedings were carefully reviewed and selected from 81 submissions. They were organized in topical sections as follows: Part I: Inverse Problems in Imaging; machine and deep learning in imaging; Part II: Optimization for imaging: theory and methods; scale space, PDES, flow, motion and registration.
Scale Space and Variational Methods in Computer Vision: 7th International Conference, SSVM 2019, Hofgeismar, Germany, June 30 – July 4, 2019, Proceedings (Lecture Notes in Computer Science #11603)
by Martin Burger Jan Lellmann Jan ModersitzkiThis book constitutes the proceedings of the 7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019, held in Hofgeismar, Germany, in June/July 2019. The 44 papers included in this volume were carefully reviewed and selected for inclusion in this book. They were organized in topical sections named: 3D vision and feature analysis; inpainting, interpolation and compression; inverse problems in imaging; optimization methods in imaging; PDEs and level-set methods; registration and reconstruction; scale-space methods; segmentation and labeling; and variational methods.
Scale Space and Variational Methods in Computer Vision: 8th International Conference, SSVM 2021, Virtual Event, May 16–20, 2021, Proceedings (Lecture Notes in Computer Science #12679)
by Jalal Fadili Abderrahim Elmoataz Yvain Quéau Julien Rabin Loïc SimonThis book constitutes the proceedings of the 8th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021, which took place during May 16-20, 2021. The conference was planned to take place in Cabourg, France, but changed to an online format due to the COVID-19 pandemic.The 45 papers included in this volume were carefully reviewed and selected from a total of 64 submissions. They were organized in topical sections named as follows: scale space and partial differential equations methods; flow, motion and registration; optimization theory and methods in imaging; machine learning in imaging; segmentation and labelling; restoration, reconstruction and interpolation; and inverse problems in imaging.
Scale Space and Variational Methods in Computer Vision: 9th International Conference, SSVM 2023, Santa Margherita di Pula, Italy, May 21–25, 2023, Proceedings (Lecture Notes in Computer Science #14009)
by Marco Donatelli Luca Calatroni Serena Morigi Marco Prato Matteo SantacesariaThis book constitutes the proceedings of the 9th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2023, which took place in Santa Margherita di Pula, Italy, in May 2023. The 57 papers presented in this volume were carefully reviewed and selected from 72 submissions. They were organized in topical sections as follows: Inverse Problems in Imaging; Machine and Deep Learning in Imaging; Optimization for Imaging: Theory and Methods; Scale Space, PDEs, Flow, Motion and Registration.
Scaling Agile with Jira Align: A Practical Guide To Strategically Scaling Agile Across Teams, Programs, And Portfolios In Enterprises
by Dean MacNeil Aslam CaderThis book is for portfolio managers, program managers, product managers, product owners, executives, release train engineers, and scrum masters who want to empower their teams to deliver the right things at the right time and quickly respond to changes in the market. Familiarity with Jira Software is necessary; the book will teach you the rest.
Scaling Apache Solr
by Hrishikesh Vijay KarambelkarThis book is a step-by-step guide for readers who would like to learn how to build complete enterprise search solutions, with ample real-world examples and case studies. If you are a developer, designer, or architect who would like to build enterprise search solutions for your customers or organization, but have no prior knowledge of Apache Solr/Lucene technologies, this is the book for you.
Scaling Big Data with Hadoop and Solr
by Hrishikesh KarambelkarThis book is a step-by-step tutorial that will enable you to leverage the flexible search functionality of Apache Solr together with the Big Data power of Apache Hadoop.Scaling Big Data with Hadoop and Solr provides guidance to developers who wish to build high-speed enterprise search platforms using Hadoop and Solr. This book is primarily aimed at Java programmers who wish to extend the Hadoop platform to make it run as an enterprise search without any prior knowledge of Apache Hadoop and Solr.
Scaling Big Data with Hadoop and Solr - Second Edition
by Hrishikesh Vijay KarambelkarThis book is aimed at developers, designers, and architects who would like to build big data enterprise search solutions for their customers or organizations. No prior knowledge of Apache Hadoop and Apache Solr/Lucene technologies is required.
Scaling Cloud FinOps: Proven Strategies to Accelerate Financial Success
by Sasi Kanumuri Matthew ZeierResponding to the escalating demands placed on organizations and enterprises as they navigate the intricacies of cloud economics, this book offers pragmatic insights for establishing a sturdy foundation for cloud cost management. Scaling Cloud FinOps empowers you with the knowledge and strategies to harness efficient cloud technology usage to proficiently manage cloud costs, refine expenditure, and implement robust, scalable Cloud FinOps practices. At the same time, it arms engineering leaders and executives with the necessary tools to foster a culture of cost awareness critical to greater profitability. At the heart of the book lies author Sasi Kanumuri’s #Piggy-Bank Framework, an innovative approach to cloud cost governance that offers a practical blueprint to streamline cost reporting, provisioning, and resource management through automation, efficiency, and overall financial performance. You’ll also delve into the intricacies of the 6-factor formula, a proven approach to cloud cost management. From resource rightsizing and cost allocation models to automated guardrails and vendor management, each factor serves as a pillar to support your organization's financial goals. Looking beyond numbers, Scaling Cloud FinOps will give you the tools needed to orchestrate a cultural shift that can permeate every aspect of your organization. You'll learn how to cultivate a cost-aware engineering culture in which financial policies give every team member the knowledge and motivation to make data-driven decisions that drive efficiency, unlocking significant cost savings and cloud financial excellence. What You Will Learn Cultivate a culture of cost awareness and accountability within engineering teams, fostering collaboration and data-driven decision-making to enhance cloud efficiency Best practices from FinOps pioneers who've scaled world-class FinOps Teams at tech giants and startups Explore unique frameworks enriched with real-world case studies, providing invaluable insights into effective cloud cost management (CCM) Acquire expert techniques in cost optimization, automation, and vendor management, all proven to deliver significant savings and optimal efficacy Who This Book Is For Professionals and leaders across the cloud, IT, finance, and procurement industries interested in streamlining cloud expenditures, cultivating a culture of cost awareness across the organization, and establishing robust cloud cost management strategies. Whether you're a novice or seasoned in FinOps practices, this book equips you with the tools to maximize the business value of your cloud investments.
Scaling CouchDB: Replication, Clustering, and Administration
by Bradley HoltThis practical guide offers a short course on scaling CouchDB to meet the capacity needs of your distributed application. Through a series of scenario-based examples, this book lets you explore several methods for creating a system that can accommodate growth and meet expected demand. In the process, you learn about several tools that can help you with replication, load balancing, clustering, and load testing and monitoring.Apply performance tips for tuning your databaseReplicate data, using Futon and CouchDB’s RESTful interfaceDistribute CouchDB’s workload through load balancingLearn options for creating a cluster of CouchDB nodes, including BigCouch, Lounge, and PillowConduct distributed load testing with Tsung
Scaling Educational Innovations
by Chee-Kit Looi Laik Woon TehThis volume stimulates critical discussions of the different variants of implementation, translation and scaling research approaches. It presents an integrated collection of different implementation and scaling studies that analyse the different facets of co-design, learning design, curriculum development, technology development, professional development and programme implementation. It also provides critical reflections on their impact and efficacies on transforming practices, informing policy-making, and theory derivation and improvement. The chapters in this volume will provide readers a deeper understanding of scaling of educational innovations in diverse socio-cultural contexts.
Scaling Enterprise Solutions with Large Language Models: Comprehensive End-to-End Generative AI Solutions for Production-Grade Enterprise Solutions
by Arindam GangulyArtificial Intelligence (AI) is the bedrock of today's applications, propelling the field towards Artificial General Intelligence (AGI). Despite this advancement, integrating such breakthroughs into large-scale production-grade enterprise applications presents significant challenges. This book addresses these hurdles in the domain of large language models within enterprise solutions. By leveraging Big Data engineering and popular data cataloguing tools, you’ll see how to transform challenges into opportunities, emphasizing data reuse for multiple AI models across diverse domains. You’ll gain insights into large language model behavior by using tools such as LangChain and LLamaIndex to segment vast datasets intelligently. Practical considerations take precedence, guiding you on effective AI Governance and data security, especially in data-sensitive industries like banking. This enterprise-focused book takes a pragmatic approach, ensuring large language models align with broader enterprise goals. From data gathering to deployment, it emphasizes the use of low code AI workflow tools for efficiency. Addressing the challenges of handling large volumes of data, the book provides insights into constructing robust Big Data pipelines tailored for Generative AI applications. Scaling Enterprise Solutions with Large Language Models will lead you through the Generative AI application lifecycle and provide the practical knowledge to deploy efficient Generative AI solutions for your business. What You Will Learn Examine the various phases of an AI Enterprise Applications implementation. Turn from AI engineer or Data Science to an Intelligent Enterprise Architect. Explore the seamless integration of AI in Big Data Pipelines. Manage pivotal elements surrounding model development, ensuring a comprehensive understanding of the complete application lifecycle. Plan and implement end-to-end large-scale enterprise AI applications with confidence. Who This Book Is For Enterprise Architects, Technical Architects, Project Managers and Senior Developers.
Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch
by Adi PolakLearn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you beyond specific requirements and organizational goals--allowing data and ML practitioners to collaborate and understand each other better. Scaling Machine Learning with Spark examines several technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLflow, TensorFlow, and PyTorch. If you're a data scientist who works with machine learning, this book shows you when and why to use each technology. You will: Explore machine learning, including distributed computing concepts and terminologyManage the ML lifecycle with MLflowIngest data and perform basic preprocessing with SparkExplore feature engineering, and use Spark to extract featuresTrain a model with MLlib and build a pipeline to reproduce itBuild a data system to combine the power of Spark with deep learningGet a step-by-step example of working with distributed TensorFlowUse PyTorch to scale machine learning and its internal architecture
Scaling MongoDB: Sharding, Cluster Setup, and Administration
by Kristina ChodorowCreate a MongoDB cluster that will grow to meet the needs of your application. With this short and concise book, you'll get guidelines for setting up and using clusters to store a large volume of data, and learn how to access the data efficiently. In the process, you'll understand how to make your application work with a distributed database system.Scaling MongoDB will help you:Set up a MongoDB cluster through shardingWork with a cluster to query and update dataOperate, monitor, and backup your clusterPlan your application to deal with outagesBy following the advice in this book, you'll be well on your way to building and running an efficient, predictable distributed system using MongoDB.
Scaling Networks Companion Guide
by Cisco Networking Academy Program StaffScaling Networks Companion Guide is the official supplemental textbook for the Scaling Networks course in the Cisco® CCNA® Academy® This course describes the architecture, components, and operations of routers and switches in a large and complex network. You will learn how to configure routers and switches for advanced functionality. By the end of this course, you will be able to configure and troubleshoot routers and switches and resolve common issues with OSPF, EIGRP, STP, and VTP in both IPv4 and IPv6 networks. You will also develop the knowledge and skills needed to implement DHCP and DNS operations in a network. The Companion Guide is designed as a portable desk reference to use anytime, anywhere to reinforce the material from the course and organize your time. The book's features help you focus on important concepts to succeed in this course.
Scaling Python with Dask: From Data Science to Machine Learning
by Holden Karau Mika KimminsModern systems contain multi-core CPUs and GPUs that have the potential for parallel computing. But many scientific Python tools were not designed to leverage this parallelism. With this short but thorough resource, data scientists and Python programmers will learn how the Dask open source library for parallel computing provides APIs that make it easy to parallelize PyData libraries including NumPy, pandas, and scikit-learn.Authors Holden Karau and Mika Kimmins show you how to use Dask computations in local systems and then scale to the cloud for heavier workloads. This practical book explains why Dask is popular among industry experts and academics and is used by organizations that include Walmart, Capital One, Harvard Medical School, and NASA.With this book, you'll learn:What Dask is, where you can use it, and how it compares with other toolsHow to use Dask for batch data parallel processingKey distributed system concepts for working with DaskMethods for using Dask with higher-level APIs and building blocksHow to work with integrated libraries such as scikit-learn, pandas, and PyTorchHow to use Dask with GPUs