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Brain-inspired computing ; 4th International Workshop, BrainComp 2019, Cetraro, Italy, July 15–19, 2019, Revised Selected Papers
The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures.
Big data-enabled internet of things
Covers analytical techniques for handling the huge amount of data generated by the Internet of Things, from architectures and platforms to security and privacy issues, applications, and challenges as well as future directions. The fields of Big Data and the Internet of Things (IoT) have seen tremendous advances, developments, and growth in recent years. The IoT is the inter-networking of connected smart devices, buildings, vehicles and other items which are embedded with electronics, software, sensors and actuators, and network connectivity that enable these objects to collect and exchange data. The IoT produces a lot of data. Big data describes very large and complex data sets that traditional data processing application software is inadequate to deal with, and the use of analytical methods to extract value from data. This edited book covers analytical techniques for handling the huge amount of data generated by the Internet of Things, from architectures and platforms to security and privacy issues, applications, and challenges as well as future directions.
Big Data Recommender Systems ; Vol.1 : Algorithms, Architectures, Big Data, Security and Trust
Combines experimental and theoretical research on big data recommender systems to help computer scientists develop new concepts and methodologies for complex applications. It includes original scientific contributions in the form of theoretical foundations, comparative analysis, surveys, case studies, techniques and tools.
Autonomous intelligent systems : Multi-agents and data mining ; 2nd International workshop, AIS-ADM 2007, St. Petersburg, Russia, June 3-5, 2007, Proceedings
MAS offiers powerful metaphors for information system conceptualization, a range of new techniques, and technologies specifically focused on the design and implementation of lar- scale open distributed intelligent systems. KDD also provides intelligent inf- mation technology with powerful ideas, algorithms, and software means to help cope with the main problem of artificial intelligence, formulated in the we- known question “Where does the knowledge come from?”, thus actually making modern applications intelligent and adaptive. The evident recent trend in both science and industry is to integrate and take advantage of both technologies. The existing experience with combined application of multi-agent technology to design architectures of distributed (- erarchical and peer-to-peer) data mining and KDD systems, as well as the u- lization of data mining and KDD achievements to provide enhanced intelligence of MAS, confirms the fact that both technologies are capable of mutual enri- ment and their integrateduse may result in intelligent information systems with new emergent properties.
Autonomous driving : Technical, legal and social aspects
This book takes a look at fully automated, autonomous vehicles and discusses many open questions: How can autonomous vehicles be integrated into the current transportation system with diverse users and human drivers? Where do automated vehicles fall under current legal frameworks? What risks are associated with automation and how will society respond to these risks? How will the marketplace react to automated vehicles and what changes may be necessary for companies?
Automated machine learning : Methods, systems, challenges
This book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself.
Artificial neural networks in Pattern Recognition ; 9th IAPR TC3 Workshop, ANNPR 2020, Winterthur, Switzerland, September 2–4, 2020, Proceedings
This book constitutes the refereed proceedings of the 9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020, held in Winterthur, Switzerland, in September 2020. The conference was held virtually due to the COVID-19 pandemic. The 22 revised full papers presented were carefully reviewed and selected from 34 submissions. The papers present and discuss the latest research in all areas of neural network-and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications.
Artificial neural networks – ICANN 2007 ; 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I
This book contains learning theory, advances in neural network learning methods, ensemble learning, spiking neural networks, advances in neural network architectures neural network technologies, neural dynamics and complex systems, data analysis, estimation, spatial and spatio-temporal learning, evolutionary computing, meta learning, agents learning, complex-valued neural networks, as well as temporal synchronization and nonlinear dynamics in neural networks.
Artificial neural networks - ICANN 2006 ; Vol.4132 : 16th International Conference, Athens, Greece, September 10-14, 2006, Proceedings, Part II
This book includes the proceedings of the International Conference on Artificial Neural Networks (ICANN 2006) held on September 10-14, 2006 in Athens, Greece, with tutorials being presented on September 10, the main conference taking place during September 11-13 and accompanying workshops on perception, cognition and interaction held on September 14, 2006. The ICANN conference is organized annually by the European Neural Network Society in cooperation with the International Neural Network Society, the Japanese Neural Network Society and the IEEE Computational Intelligence Society. It is the premier European event covering all topics concerned with neural networks and related areas.
Artificial intelligence hardware design : Challenges and solutions
Learn foundational and advanced topics in Neural Processing Unit design with real-world examples from leading voices in the field. A thorough introduction to neural networks and neural network development history, as well as Convolutional Neural Network (CNN) models Explorations of various parallel architectures, including the Intel CPU, Nvidia GPU, Google TPU, and Microsoft NPU, emphasizing hardware and software integration for performance improvement Discussions of streaming graph for massive parallel computation with the Blaize GSP and Graphcore IPU An examination of how to optimize convolution with UCLA Deep Convolutional Neural Network accelerator filter decomposition
Artificial intelligence for multisource geospatial information
Collects 10 original research contributions published in the Special Issue entitled “Artificial Intelligence for Multisource Geospatial Information” of the ISPRS International Journal of Geo-Information. The focus is on different methods of Geospatial Artificial Intelligence (GeoAI) based on deep learning using different network architectures, clustering, soft computing, and semantic approaches. They are proposed to deal with a variety of Geospatial Big Data (GBD), such as georeferenced texts and photos in social networks, remote sensing images, cartographic maps, multidimensional geo databases, metadata in spatial data infrastructures, and for different tasks, such as for multisource georeferenced text integration and geodata flexible querying, for social sensing by applying sentiment analysis, clustering and geo analysis, for segmentation of roads, clouds and snow, and for detection of small targets and people on the streets.
Arithmetic of finite fields ; 1st International Workshop, WAIFI 2007, Madrid, Spain, June 21-22, 2007, Proceedings
This book presented structures in finite fields, efficient implementation and architectures, efficient finite field arithmetic, classification and construction of mappings over finite fields, curve algebra, cryptography, codes, and discrete structures.
Architecture of computing systems - ARCS 2006 ; 19th International Conference, Frankfurt / Main, Germany, March 13-16, 2006, Proceedings
Technological progress is one of the driving forces behind the dramatic devel- mentofcomputersystemarchitecturesoverthe pastthreedecades.Eventhough it is quite clear that this development cannot only be measured by the ma- mum number of components on a chip, Moore’s Law may be and is often taken as a simple measure for the non-braked growth of computational power over the years. The more components are realizable on a chip, the more innovative and unconventional ideas can be realized by system architects. As a result, research in computer system architectures is more exciting than ever before. This book coversthe trends that shape the ?eld of computer system archit- tures.
Architecture description languages ; IFIP TC-2 workshop on architecture description languages (WADL), World Computer Congress, Aug. 22-27, 2004, Toulouse, France
These proceedings record the papers presented at the Workshop onArchitecture Description Languages held in the city of Toulouse in thesouth of France.The aim of an ADL (Architecture Description Language) is to formallydescribe software and hardware architectures. Usually, an ADL describescomponents, their interfaces, their structures, their interactions (structureof data flow and control flow) and the mappings to hardware systems. Amajor goal of such descriptions is to allow analysis with respect to severalaspects like timing, safety, reliability, ...
Architecting dependable systems V
As software systems become increasingly ubiquitous, the issues of dependability become more and more crucial. Given that solutions to these issues must be considered from the very beginning of the design process, it is reasonable that dependability is addressed at the architectural level. This book was born of an effort to bring together the research communities of software architectures and dependability.
Architecting dependable systems III
As software systems become ubiquitous, the issues of dependability become more and more crucial. Given that solutions to these issues must be considered from the very beginning of the design process, it is reasonable that dependability is addressed at the architectural level. This book comes as a result of an effort to bring together the research communities of software architectures and dependability. The papers are organised in topical sections on architectures for dependable services, monitoring and reconfiguration in software architectures, dependability support for software architectures, architectural evaluation, and architectural abstractions for dependability
Applications of Membrane Computing
Membrane computing is a branch of natural computing which investigates computing models abstracted from the structure and functioning of living cells and from their interactions in tissues or higher-order biological structures. The models considered, called membrane systems (P systems), are parallel, distributed computing models, processing multisets of symbols in cell-like compartmental architectures. In many applications membrane systems have considerable advantages – among these are their inherently discrete nature, parallelism, transparency, scalability and nondeterminism.
Applications of Fuzzy Sets Theory ; 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Camogli, Italy, July 7-10, 2007, Proceedings
The book is organized in topical sections on fuzzy set theory, fuzzy information access and retrieval, fuzzy machine learning, fuzzy architectures and systems; and special sessions on intuitionistic fuzzy sets and soft computing in image processing.
Applications and Theory of Petri Nets ; 29th International Conference, PETRI NETS 2008, Xi’an, China, June 23-27, 2008. Proceedings
This book constitutes the refereed proceedings of the 29th International Conference on Applications and Theory of Petri Nets and Other Models of Concurrency, PETRI NETS 2008, held in Xi'an, China, in June 2008.
Analyzing computer system performance with Perl::PDQ
Analyzing computer system performance is often regarded by most system administrators, IT professionals and software engineers as a black art that is too time consuming to learn and apply. Finally, this book by acclaimed performance analyst Dr. Neil Gunther makes this subject understandable and applicable through programmatic examples. The means to this end is the open-source performance analyzer Pretty Damn Quick (PDQ) written in Perl As the epigraph in this book points out, Common sense is the pitfall of performance analysis. The performance analysis framework that replaces common sense is revealed in the first few chapters of Part I. The important queueing concepts embedded in PDQ are explained in a very simple style that does not require any knowledge of formal probability theory. Part II begins with a full specification of how to set up and use PDQ replete with examples written in Perl. Subsequent chapters present applications of PDQ to the performance analysis of multicomputer architectures, benchmark results, client/server scalability, and Web-based applications.
