High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Understanding Quantum Technologies 2022

Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma

Técnicas de computación social e información contextual para el desarrollo de actividades de aprendizaje colaborativo

[EN]Educational innovation is a field in which its processes has been greatly enriched by the use of Information and Communication Technologies (ICT). Thanks to technological advances, the use of learning models where information comes from many different sources is now usual. Likewise, student-student, student-device and device-device collaborations provides added value to the learning processes thanks to the fact that, through it, aspects such as communication, achievement of common goals or sharing resources. Within the educational innovation, we find as a great challenge the development of tools that facilitate the creation of innovative collaborative learning processes that improve the achievement of the objectives sought, with respect to individualized processes, and the fidelity of the students to the process through the use of contextual information. Moreover, the development of these solutions, that facilitate the work of teachers, developers and technicians encouraging the production of educational processes more attractive to students, presents itself as an ambitious challenge in which the perspectives of Ambient Intelligence and Social Computing play a key role. The doctoral dissertation presented here describes and evaluates CAFCLA, a framework specially conceived for the design, development and implementation of collaborative learning activities that make use of contextual information and that is based on the paradigms of Ambient Intelligence and Social Computing. CAFCLA is a flexible framework that covers the entire process of developing collaborative learning activities and hides all the difficulties involved in the use and integration of multiple technologies to its users. In order to evaluate the validity of the proposal, CAFCLA has supported the implementation of three concrete and different use cases. These experimental use cases have shown that, among other benefits, the use of Social Computing customizes the learning process, encourages collaboration, improves relationships, increases commitment, promotes behaviour change in users and enables learning to be maintained over time. In addition, in order to demonstrate the flexibility of the framework, these use cases have been developed in different scenarios (such as a museum, a public building or at home), different types of learning have been proposed (serious games, recommendations system orWebQuest) and different learning objectives have been chosen (academic, social and energy-efficient).[ES]La innovación educativa es un campo que ha sido enormemente enriquecido por el uso de las Tecnologías de la Información y las Comunicaciones (TIC) en sus procesos. Gracias a los avances tecnológicos, actualmente es habitual el uso de modelos de aprendizaje donde la información proviene de numerosas y diferentes fuentes. De igual forma, la colaboración estudiante-estudiante, estudiante-dispositivo y dispositivo-dispositivo, proporciona un valor añadido a los procesos de aprendizaje gracias a que, a través de ella, se fomentan aspectos como la comunicación, la consecución de una meta común, o la compartición de recursos. Dentro de la innovación educativa encontramos como un gran desafío el desarrollo de herramientas que faciliten la creación de procesos de aprendizaje colaborativo innovadores que mejoren los resultados obtenidos, respecto a los procesos individualizados, y la fidelidad de los estudiantes al proceso mediante el uso de información contextual.Más aún, el desarrollo de soluciones que faciliten el trabajo a profesores, desarrolladores y técnicos, fomentando la producción de procesos educativos más atractivos para los estudiantes, se presenta como un ambicioso reto en el que las perspectivas de la Inteligencia Ambiental y la Computación Social juegan un papel fundamental. La tesis doctoral aquí presentada describe y evalúa CAFCLA, un framework especialmente concebido para el diseño, desarrollo e implementación de actividades de aprendizaje colaborativo que hagan uso de información contextual basándose en los paradigmas de la Inteligencia Ambiental y la Computación Social. CAFCLA es un framework flexible que abarca todo el proceso de desarrollo de actividades de aprendizaje colaborativo y oculta todas las dificultades que implican el uso e integración de múltiples tecnologías a sus usuarios. Para evaluar la validez de la propuesta realizada, CAFCLA ha soportado la implementación de tres casos de uso concretos y diferentes entre sí. Estos casos de uso experimentales han demostrado que, entre otros beneficios, el uso de la Computación Social personaliza el proceso de aprendizaje, fomenta la colaboración, mejora las relaciones, aumenta el compromiso, favorecen el cambio de comportamiento en los usuarios y mantiene su implicación en el proceso a lo largo del tiempo. Además, con el objetivo de demostrar la flexibilidad del framework, estos casos de uso se han desarrollado en diferentes escenarios (como un museo, un edificio público o el hogar), se han propuesto diferente tipos de aprendizaje (juegos serios, sistema de recomendaciones o WebQuest) y se han elegido diferentes objetivos de aprendizaje (académicos, sociales y de eficiencia energética)

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Queensland University of Technology: Handbook 1998

The Queensland University of Technology handbook gives an outline of the faculties and subject offerings available that were offered by QUT