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pro vyhledávání: '"García, Álvaro López"'
Open science is a fundamental pillar to promote scientific progress and collaboration, based on the principles of open data, open source and open access. However, the requirements for publishing and sharing open data are in many cases difficult to me
Externí odkaz:
http://arxiv.org/abs/2408.10766
Autor:
Díaz, Judith Sáinz-Pardo, Castrillo, María, Bartok, Juraj, Cachá, Ignacio Heredia, Ondík, Irina Malkin, Martynovskyi, Ivan, Alibabaei, Khadijeh, Berberi, Lisana, Kozlov, Valentin, García, Álvaro López
The increasing generation of data in different areas of life, such as the environment, highlights the need to explore new techniques for processing and exploiting data for useful purposes. In this context, artificial intelligence techniques, especial
Externí odkaz:
http://arxiv.org/abs/2408.05761
Anonymization techniques based on obfuscating the quasi-identifiers by means of value generalization hierarchies are widely used to achieve preset levels of privacy. To prevent different types of attacks against database privacy it is necessary to ap
Externí odkaz:
http://arxiv.org/abs/2305.07415
Openly sharing data with sensitive attributes and privacy restrictions is a challenging task. In this document we present the implementation of pyCANON, a Python library and command line interface (CLI) to check and assess the level of anonymity of a
Externí odkaz:
http://arxiv.org/abs/2208.07556
Deep learning has been postulated as a solution for numerous problems in different branches of science. Given the resource-intensive nature of these models, they often need to be executed on specialized hardware such graphical processing units (GPUs)
Externí odkaz:
http://arxiv.org/abs/2208.02498
Federated learning is a data decentralization privacy-preserving technique used to perform machine or deep learning in a secure way. In this paper we present theoretical aspects about federated learning, such as the presentation of an aggregation ope
Externí odkaz:
http://arxiv.org/abs/2207.08581
Autor:
Cacha, Ignacio Heredia, Díaz, Judith Sainz-Pardo, Melguizo, María Castrillo, García, Álvaro López
In this work we evaluate the applicability of an ensemble of population models and machine learning models to predict the near future evolution of the COVID-19 pandemic, with a particular use case in Spain. We rely solely in open and public datasets,
Externí odkaz:
http://arxiv.org/abs/2207.05753
Autor:
Castrillo, María, García, Álvaro López
Publikováno v:
Water Research. Volume 172, 1 April 2020, 115490
Continuous high frequency water quality monitoring is becoming a critical task to support water management. Despite the advancements in sensor technologies, certain variables cannot be easily and/or economically monitored in-situ and in real time. In
Externí odkaz:
http://arxiv.org/abs/2001.09695
Publikováno v:
Future Generation Computer Systems (2019)
Maximizing resource utilization by performing an efficient resource provisioning is a key factor for any cloud provider: commercial actors can maximize their revenues, whereas scientific and non-commercial providers can maximize their infrastructure
Externí odkaz:
http://arxiv.org/abs/1812.10668
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