High Performance Computing: Modern Systems and Practices is a fully comprehensive and easily accessible treatment of high performance computing, covering fundamental concepts and essential knowledge while also providing key skills training. With this book, domain scientists will learn how to use supercomputers as a key tool in their quest for new knowledge. In addition, practicing engineers will discover how supercomputers can employ HPC systems and methods to the design and simulation of innovative products, and students will begin their careers with an understanding of possible directions for future research and development in HPC. Those who maintain and administer commodity clusters will find this textbook provides essential coverage of not only what HPC systems do, but how they are used. Covers enabling technologies, system architectures and operating systems, parallel programming languages and algorithms, scientific visualization, correctness and performance debugging tools and methods, GPU accelerators and big data problems Provides numerous examples that explore the basics of supercomputing, while also providing practical training in the real use of high-end computers Helps users with informative and practical examples that build knowledge and skills through incremental steps Features sidebars of background and context to present a live history and culture of this unique field Includes online resources, such as recorded lectures from the authors´ HPC courses
This book presents the first comprehensive overview of various verifiable computing techniques, which allow the computation of a function on outsourced data to be delegated to a server. It provides a brief description of all the approaches and highlights the properties each solution achieves. Further, it analyzes the level of security provided, how efficient the verification process is, who can act as a verifier and check the correctness of the result, which function class the verifiable computing scheme supports, and whether privacy with respect to t he input and/or output data is provided. On the basis of this analysis the authors then compare the different approaches and outline possible directions for future work. The book is of interest to anyone wanting to understand the state of the art of this research field.
This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.
Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, FiPy, matplotlib and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business management and more. After reading and using this book, you´ll get some takeaway case study examples of applications that can be found in areas like business management, big data/cloud computing, financial engineering (i.e., options trading investment alternatives), and even games. Up until very recently, Python was mostly regarded as just a web scripting language. Well, computational scientists and engineers have recently discovered the flexibility and power of Python to do more. Big data analytics and cloud computing programmers are seeing Python´s immense use. Financial engineers are also now employing Python in their work. Python seems to be evolving as a language that can even rival C++, Fortran, and Pascal/Delphi for numerical and mathematical computations.
Cloud Computing provides many types of services mainly SaaS, PaaS, IaaS. Each of them has its own security challenges, but IaaS undertakes all types of challenges viz., network attack and request based attacks ie handling the requests from untrusted users, XSS (cross-site scripting attack), DDOS and many more. These attacks are independent of each other and consequently the QoS provided by cloud is compromised. This paper proposes a Behaviour based IDS (Intrusion Detection System) BIDS. BIDS provides detection of untrusted users, false requests that may lead to spoofing, XSS or DOS attack and many such possible attacks. In addition, there are certain cases where user login or password is compromised. BIDs can be helpful in detecting such attacks and maintaining the QoS of cloud.
Service-oriented computing has become one of the predominant factors in IT research and development efforts over the last few years. In spite of several standardization efforts that advanced from research labs into industrial-strength technologies and tools, there is still much human effort required in the process of finding and executing Web services. Here, Dieter Fensel and his team lay the foundation for understanding the Semantic Web Services infrastructure, aimed at eliminating human intervention and thus allowing for seamless integration of information systems. They focus on the currently most advanced SWS infrastructure, namely SESA and related work such as the Web Services Execution Environment (WSMX) activities and the Semantic Execution Environment (OASIS SEE TC) standardization effort. Their book is divided into four parts: Part I provides an introduction to the field and its history, covering basic Web technologies and the state of research and standardization in the Semantic Web field. Part II presents the SESA architecture. The authors detail its building blocks and show how they are consolidated into a coherent software architecture that can be used as a blueprint for implementation. Part III gives more insight into middleware services, describing the necessary conceptual functionality that is imposed on the architecture through the basic principles. Each such functionality is realized using a number of so-called middleware services. Finally, Part IV shows how the SESA architecture can be applied to real-world scenarios, and provides an overview of compatible and related systems. The book targets professionals as well as academic and industrial researchers working on various aspects of semantic integration of distributed information systems. They will learn how to apply the Semantic Web Services infrastructure to automate and semi-automate tasks, by using existing integration technologies. In addition, the book is also suitable for advanced graduate students enrolled in courses covering knowledge management, the Semantic Web, or integration of information systems, as it will educate them about basic technologies for Semantic Web Services and general issues related to integration of information systems.
Now in its third edition, this best-selling book continues to bring you some of the best thinking on how to apply Oracle Database to produce scalable applications that perform well and deliver correct results. Tom Kyte and Darl Kuhn share a simple philosophy: ´´you can treat Oracle as a black box and just stick data into it, or you can understand how it works and exploit it as a powerful computing environment.´´ If you choose the latter, then you´ll find that there are few information management problems that you cannot solve quickly and elegantly. This fully revised third edition covers the developments up to Oracle Database 12 c . Significant new content is included surrounding Oracle´s new cloud feature set, and especially the use of pluggable databases. Each feature is taught in a proof-by-example manner, not only discussing what it is, but also how it works, how to implement software using it, and the common pitfalls associated with it. Don´t treat Oracle Database as a black-box. Get this book. Get under the hood. Turbo-charge your career. Revised to cover Oracle Database 12 c Proof-by-example approach: Let the evidence be your guide Dives deeply into Oracle Database´s most powerful features
With significant growth of bio-molecular sequence data in the last decade the need for algorithms to extract patterns and meaningful information from such data has been felt strongly. Alignment of sequences, in order to determine regions of common descent, has also been an important area of research as it helps scientist discover the evolution of species. Another problem that researchers are putting in a lot of effort into, is document summary. As the lower bound for computation is being met for various algorithms, to further expedite the computing on large data sets, parallelization has become imperative. New multiprocessor architectures like the Cell Broadband Engine have the potential to do extensive calculations and act as mini-supercomputers. Other applications for these include onboard aircraft fault diagnosis and prognosis. We take a peek into some existing algorithms for these problems as well as propose novel algorithms along with their implementations to address these problems in the field of bioinformatics.
In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence. This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from images. Outlining and analyzing various research frontiers of NLP in the deep learning era, it features self-contained, comprehensive chapters written by leading researchers in the field. A glossary of technical terms and commonly used acronyms in the intersection of deep learning and NLP is also provided. The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing.