Titlebook: Cloud Computing, Big Data & Emerging Topics; 8th Conference, JCC- Enzo Rucci,Marcelo Naiouf,Laura De Giusti Conference proceedings 2020 Spr

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https://doi.org/10.1007/978-3-322-89731-2hat adapting the windowing coefficients produces a moderate accuracy improvement. It is concluded that the gradient of the error function can be propagated through the neural calculation of the power spectrum. It is also concluded that the training of the windowing layer improves the model’s ability to generalize.

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Conference proceedings 2020held in La Plata, Argentina*, in September 2020..The 11 full papers presented were carefully reviewed and selected from a total of 36 submissions. The papers are organized in topical sections of cloud computing and HPC; Big Data and machine and deep learning..*The conference was held virtually due t

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Towards a Malleable Tensorflow Implementationopose the necessary modifications within TensorFlow to support dynamic selection of threads, in order to provide transparent malleability to the infrastructure. Experimental results show that this approach is effective in the variation of parallelism, and paves the road towards future co-scheduling techniques for multi-TensorFlow scenarios.

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Viral Diseases Propagation Analysis in Short Timeme the viral disease behaviour when it is known or not, as well as its associated uncertainty. Besides, it is suitable to test effects of different measures that tending towards stop the spread. We describe the solution and evaluate it for two viral diseases: Seasonal Influenza and COVID-19.

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Classification of Summer Crops Using Active Learning Techniques on Landsat Images in the Northwest othm was also compared with the supervised technique Support Vector Machine (SVM). The experiments were tested on three Landsat 8 images from different dates using 6 bands per image and various vegetation indices. The results obtained using AL in combination with the different heuristics do not differ substantially from SVM.