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Enhancing iteration performance on distributed task-based workflows
(Elsevier, 2023-12)
Article
Restricted access - publisher's policyTask-based programming models have proven to be a robust and versatile way to approach development of applications for distributed environments. They provide natural programming patterns with high performance. However, ... -
An energy-efficient GeMM-based convolution accelerator with on-the-fly im2col
(2023-06-27)
Article
Open AccessSystolic array architectures have recently emerged as successful accelerators for deep convolutional neural network (CNN) inference. Such architectures can be used to efficiently execute general matrix–matrix multiplications ... -
High performance computing PP-distance algorithms to generate X-ray spectra from 3D models
(Multidisciplinary Digital Publishing Institute (MDPI), 2022-09-27)
Article
Open AccessX-ray crystallography is a powerful method that has significantly contributed to our understanding of the biological function of proteins and other molecules. This method relies on the production of crystals that, however, ... -
RouteNet-Fermi: network modeling with graph neural networks
(Institute of Electrical and Electronics Engineers (IEEE), 2023-05-08)
Article
Open AccessNetwork models are an essential block of modern networks. For example, they are widely used in network planning and optimization. However, as networks increase in scale and complexity, some models present limitations, such ... -
Optimizing iterative data-flow scientific applications using directed cyclic graphs
(Institute of Electrical and Electronics Engineers (IEEE), 2023-04-24)
Article
Open AccessData-flow programming models have become a popular choice for writing parallel applications as an alternative to traditional work-sharing parallelism. They are better suited to write applications with irregular parallelism ... -
Mitigating the NUMA effect on task-based runtime systems
(Springer Nature, 2023-09)
Article
Restricted access - publisher's policyProcessors with multiple sockets or chiplets are becoming more conventional. These kinds of processors usually expose a single shared address space. However, due to hardware restrictions, they adopt a NUMA approach, where ... -
Temporal pattern-based denoising and calibration for low-cost sensors in IoT monitoring platforms
(Institute of Electrical and Electronics Engineers (IEEE), 2023-01-25)
Article
Open AccessThe introduction of low-cost sensors (LCSs) in air quality Internet of Things (IoT) monitoring platforms presents the challenge of improving the quality of the data that these sensors provide. In this article, we propose ... -
OCATA: A deep-learning-based digital twin for the optical time domain
(Optical Society of American (OSA), 2023-02-01)
Article
Restricted access - publisher's policyThe development of digital twins to represent the optical transport network might enable multiple applications for network operation, including automation and fault management. In this work, we propose a deep-learning-based ... -
TrackSign-labeled web tracking dataset
(2023-05)
Article
Open AccessRecent studies show that more than 95% of the websites available on the Internet contain at least one of the so-called web tracking systems. These systems are specialized in identifying their users by means of a plethora ... -
Anomaly detection for fault detection in wireless community networks using machine learning
(Elsevier, 2023-03-15)
Article
Open AccessMachine learning has received increasing attention in computer science in recent years and many types of methods have been proposed. In computer networks, little attention has been paid to the use of ML for fault detection, ... -
ePrivo.eu: An online service for automatic web tracking discovery
(Institute of Electrical and Electronics Engineers (IEEE), 2023)
Article
Open AccessGiven the pervasiveness of web tracking practices on the Internet, many countries are developing and enforcing new privacy regulations to ensure the rights of their citizens. However, discovering websites that do not comply ... -
An automotive case study on the limits of approximation for object detection
(2023-05)
Article
Restricted access - publisher's policyThe accuracy of camera-based object detection (CBOD) built upon deep learning is often evaluated against the real objects in frames only. However, such simplistic evaluation ignores the fact that many unimportant objects ...
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