82,125 research outputs found

    Cloud based testing of business applications and web services

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    This paper deals with testing of applications based on the principles of cloud computing. It is aimed to describe options of testing business software in clouds (cloud testing). It identifies the needs for cloud testing tools including multi-layer testing; service level agreement (SLA) based testing, large scale simulation, and on-demand test environment. In a cloud-based model, ICT services are distributed and accessed over networks such as intranet or internet, which offer large data centers deliver on demand, resources as a service, eliminating the need for investments in specific hardware, software, or on data center infrastructure. Businesses can apply those new technologies in the contest of intellectual capital management to lower the cost and increase competitiveness and also earnings. Based on comparison of the testing tools and techniques, the paper further investigates future trend of cloud based testing tools research and development. It is also important to say that this comparison and classification of testing tools describes a new area and it has not yet been done

    Special Session on Industry 4.0

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    Beta-Testing the “Particular Machine”: The Machine-or-Transformation Test in Peril and Its Impact on Cloud Computing

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    This Issue Brief examines recent cases addressing the patent eligibility of computer-implemented method claims and their implications for the development of cloud computing technologies. Despite the Supreme Court’s refusal to endorse the machine-or-transformation test as the exclusive patent eligibility inquiry, lower courts have continued to invalidate method claims using a stringent “particular machine” requirement alongside the requisite abstract ideas analysis. This Issue Brief argues that 1) post-Bilski v. Kappos cases have failed to elucidate what constitutes a particular machine for computer-implemented methods; 2) in light of substantial variance among Federal Circuit judges’ Section 101 jurisprudence, the application of the particular machine requirement has become subject to a high degree of panel-dependency, such that its relevance for analyzing software method claims has come under question; 3) notwithstanding the unease expressed by practitioners and scholars for the future of cloud computing patents, the courts’ hardening stance toward computer-implemented method claims will do little to deter patenting in the cloud computing context. Instead, clouds delivering platform and software services will remain capable of satisfying the particular machine requirement and supporting patent eligibility, especially given the possible dilution of the particular machine requirement itself

    BonFIRE: A multi-cloud test facility for internet of services experimentation

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    BonFIRE offers a Future Internet, multi-site, cloud testbed, targeted at the Internet of Services community, that supports large scale testing of applications, services and systems over multiple, geographically distributed, heterogeneous cloud testbeds. The aim of BonFIRE is to provide an infrastructure that gives experimenters the ability to control and monitor the execution of their experiments to a degree that is not found in traditional cloud facilities. The BonFIRE architecture has been designed to support key functionalities such as: resource management; monitoring of virtual and physical infrastructure metrics; elasticity; single document experiment descriptions; and scheduling. As for January 2012 BonFIRE release 2 is operational, supporting seven pilot experiments. Future releases will enhance the offering, including the interconnecting with networking facilities to provide access to routers, switches and bandwidth-on-demand systems. BonFIRE will be open for general use late 2012

    TPU Cloud-Based Generalized U-Net for Eye Fundus Image Segmentation

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    Medical images from different clinics are acquired with different instruments and settings. To perform segmentation on these images as a cloud-based service we need to train with multiple datasets to increase the segmentation independency from the source. We also require an ef cient and fast segmentation network. In this work these two problems, which are essential for many practical medical imaging applications, are studied. As a segmentation network, U-Net has been selected. U-Net is a class of deep neural networks which have been shown to be effective for medical image segmentation. Many different U-Net implementations have been proposed.With the recent development of tensor processing units (TPU), the execution times of these algorithms can be drastically reduced. This makes them attractive for cloud services. In this paper, we study, using Google's publicly available colab environment, a generalized fully con gurable Keras U-Net implementation which uses Google TPU processors for training and prediction. As our application problem, we use the segmentation of Optic Disc and Cup, which can be applied to glaucoma detection. To obtain networks with a good performance, independently of the image acquisition source, we combine multiple publicly available datasets (RIM-One V3, DRISHTI and DRIONS). As a result of this study, we have developed a set of functions that allow the implementation of generalized U-Nets adapted to TPU execution and are suitable for cloud-based service implementation.Ministerio de Economía y Competitividad TEC2016-77785-
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