Digitalisation is continuing to play an essential role in modernising Europe's industrial capabilities, allowing companies to be well positioned for global competitiveness and sustainability. Data is viewed as an ...
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ISBN:
(纸本)9783031197611;9783031197628
Digitalisation is continuing to play an essential role in modernising Europe's industrial capabilities, allowing companies to be well positioned for global competitiveness and sustainability. Data is viewed as an essential resource for economic growth, competitiveness, innovation, job creation and societal progress. As such, EU industry needs to develop highly integrated digital networks that can underpin the creation of innovative digital services. While the convergence of novel digital technologies are viewed as key enablers, their inherent complexity, heterogeneity and dynamicity create challenges for managing workflows and trust at scale. As a result valuable data assets are disparate sitting in silos across systems, roles and business functions and go unutilised. In addition, large volumes of data sit across organisations that can provide a rich cross-pollination of experience to identify common patterns, opportunities, and train robust models to support innovative data-driven services. The work presented here outlines an initial analysis of the system requirements, architectural considerations, and challenges that need to be overcome to realise distributed and trusted digitalworkflows with a focus on use cases in the domain of smart manufacturing.
Surveillance systems are very important to prevent situations where armed people appear. To minimize human supervision, there are algorithms based on artificial intelligence that perform a large part of the identifica...
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Surveillance systems are very important to prevent situations where armed people appear. To minimize human supervision, there are algorithms based on artificial intelligence that perform a large part of the identification and detection tasks. These systems usually require large data processing servers. However, a high number of cameras causes congestion in the networks due to a large amount of data being sent. This work introduces a novel system for identifying individuals with weapons by leveraging Edge, Fog, and Cloud computing. The key advantages include minimizing the data transmitted to the Cloud and optimizing the computations performed within it. The main benefits of our proposal are the high and simple scalability, the immediacy of the detection, as well as the optimization of processes through distributed processing of high performance in the Fog layer. Moreover, the structure of this proposal is suitable for 5G camera networks, which require low latency and quick responses.
Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Parallelism and distribution are now fundamentally integrated aspects of nea...
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ISBN:
(纸本)9781665410380
Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Parallelism and distribution are now fundamentally integrated aspects of nearly all areas of computation. Over the last eleven years, the CDER center has been working to raise awareness of the need to incorporate these concepts in computer science education, starting in the earliest courses, and to sponsor and train instructors in developing novel parallel and distributed computing (PDC) education approaches. CDER recently concluded a study that gathered input from PDC stakeholder communities to identify their needs. The results indicate that lack of early PDC education in undergraduate programs is actually just one sign of a broader failure of computer science education to adjust to the nature of the modern computing ecosystem. This talk will illustrate the problem, and identify directions for addressing it through adopting a more modern underlying model and changing the approach employed in teaching computational problem solving from the beginning.
The size of information gathered from real world applications today is staggering. To make matters worse, this information may also be incomplete, due to errors in measurement or lack of discipline. The two phenomena ...
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The size of information gathered from real world applications today is staggering. To make matters worse, this information may also be incomplete, due to errors in measurement or lack of discipline. The two phenomena give rise to a big incomplete information system (IIS). The processing of a big IIS is difficult because of its two problems, big size and incompleteness, and the present work introduces an approach that addresses both. Specifically, we develop an efficient rough set theoretic (RST) algorithm to compute the approximation space of the IIS, which addresses the incompleteness problem. Then we distribute the computational chores of the algorithm using the MapReduce framework, which addresses the size problem. The approach is explained fully, and a detailed illustrative example is provided. For validation and performance analysis, the approach has been implemented and tested on four publicly-accessible big IISs for many metrics including sizeup, scaleup, and speedup. The experimental results attest to its validity, accuracy and efficiency. A comparison test with similar approaches shows that it has superior performance. (C) 2020 Elsevier Inc. All rights reserved.
The coded distributed computing (CDC) proposed by Li et al. provides a high-efficiency method to decrease the communication load in most distributed computing frameworks. However, as there are more and more nodes, the...
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ISBN:
(纸本)9781665437585
The coded distributed computing (CDC) proposed by Li et al. provides a high-efficiency method to decrease the communication load in most distributed computing frameworks. However, as there are more and more nodes, the numbers of output functions and input files in these CDC scenarios grow too fast to be applied in practice. In the course of our work, we make an attempt to significantly reduce the minimum requirement of $N$ and $Q$ , where $N$ denote the number of input files and $Q$ denote the number output functions in our proposed CDC scheme. The results indicate that the minimum requirement of $Q$ in our new CDC scheme is only a factor of the total number of computing nodes $K$ , and the minimum requirement of $N$ is far less than that of the optimal scheme provided by Li et al., while $\frac{L_{new}}{L_{Li}}\leq 1.03125$ , where $L_{Li}$ and $L_{new}$ are the communication loads of the Li scheme provided by Li et al. and our derived CDC scheme, respectively.
We propose two coded schemes for the distributed computing problem of multiplying a matrix by a set of vectors. The first scheme is based on partitioning the matrix into submatrices and applying maximum distance separ...
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We propose two coded schemes for the distributed computing problem of multiplying a matrix by a set of vectors. The first scheme is based on partitioning the matrix into submatrices and applying maximum distance separable (MDS) codes to each submatrix. For this scheme, we prove that up to a given number of partitions the communication load and the computational delay (not including the encoding and decoding delay) are identical to those of the scheme recently proposed by Li et al., based on a single, long MDS code. However, due to the use of shorter MDS codes, our scheme yields a significantly lower overall computational delay when the delay incurred by encoding and decoding is also considered. We further propose a second coded scheme based on Luby transform (LT) codes under inactivation decoding. Interestingly, LT codes may reduce the delay over the partitioned scheme at the expense of an increased communication load. We also consider distributed computing under a deadline and show numerically that the proposed schemes outperform other schemes in the literature, with the LT code-based scheme yielding the best performance for the scenarios considered.
This paper introduces a renewed gateway to ENEAGRID distributed computing resources named Fast Access to Remote Objects 2.0 (FARO 2.0). FARO 2.0 is a tool for application and desktop virtualization with a focus toward...
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This paper introduces a renewed gateway to ENEAGRID distributed computing resources named Fast Access to Remote Objects 2.0 (FARO 2.0). FARO 2.0 is a tool for application and desktop virtualization with a focus towards user experience (UX), providing trained as well as untrained users with a collection of centralized services that can be seamlessly used on their client through a remote desktop protocol. FARO 2.0 is a javaFX application whose graphical user interface (GUI) and whose main logics have been implemented through the well-known Web technologies (HTML5, CSS3, Javascript) for easier maintainability and customizability, taking full advantage of the WebView component. The FARO 2.0 framework has been deployed both as a general purpose GUI for remote user access to ENEAGRID resources and as a specialized application or workflow-oriented GUI. They are applied in a set of applicative domains, ranging from materials science to technologies for energy and industry, environmental modeling, and nuclear fusion. Some examples and results are also presented. (C) 2017 Elsevier B.V. All rights reserved.
This paper presents a complete data processing pipeline for improved urban solar potential estimation by applying solar irradiation estimation directly to individual aerial laser scanning (ALS) points in a distributed...
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This paper presents a complete data processing pipeline for improved urban solar potential estimation by applying solar irradiation estimation directly to individual aerial laser scanning (ALS) points in a distributed computing environment. Solar potential is often measured by solar irradiation the amount of the Sun's radiant energy received at the Earth's surface over a period of time. To overcome previous limits of solar radiation estimations based on either two-and-a-half-dimensional raster models or overly simplistic, manually-generated, geometric models, an alternative approach is proposed using dense, urban aerial laser scanning data to enable the incorporation of the true, complex, and heterogeneous elements common in most urban areas. The approach introduces a direct, per-point analysis to fully exploit all details provided by the input point cloud data. To address the resulting computational demands required by the thousands of calculations needed per point for a full-year analysis, a distributed data processing strategy is employed that introduces an atypical data partition strategy. The scalability and performance of the approach are demonstrated on a 1.4-billion-point dataset covering more than 2 km(2) of Dublin, Ireland. The reliability and realism of the simulation results are rigorously confirmed with (1) an aerial image collected concurrently with the laser scanning, (2) a terrestrial image acquired from an online source, and (3) a four-day, direct solar radiation collection experiment.
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