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A high-performance computing framework for Monte Carlo ocean color simulations
Publication . Kajiyama, Tamito; D'Alimonte, Davide; Cunha, Jose C.
This paper presents a high-performance computing (HPC) framework for Monte Carlo (MC) simulations in the ocean color (OC) application domain. The objective is to optimize a parallel MC radiative transfer code named MOX, developed by the authors to create a virtual marine environment for investigating the quality of OC data products derived from in situ measurements of in-water radiometric quantities. A consolidated set of solutions for performance modeling, prediction, and optimization is implemented to enhance the efficiency of MC OC simulations on HPC run-time infrastructures. HPC, machine learning, and adaptive computing techniques are applied taking into account a clear separation and systematic treatment of accuracy and precision requirements for large-scale MC OC simulations. The added value of the work is the integration of computational methods and tools for MC OC simulations in the form of an HPC-oriented problem-solving environment specifically tailored to investigate data acquisition and reduction methods for OC field measurements. Study results highlight the benefit of close collaboration between HPC and application domain researchers to improve the efficiency and flexibility of computer simulations in the marine optics application domain. (C) 2016 The Authors. Concurrency and Computation: Practice and Experience Published by John Wiley & Sons Ltd.
Affective computing emotional body gesture recognition: evolution and the cream of the crop
Publication . Migueis Vaz Martins, Pedro Jorge; Rodrigues, Joao; Cardoso, Pedro
The field of affective computing (AffC) has experienced significant growth, making it challenging to stay up to date with the latest advancements. This surge in interest has likely contributed to a significant rise in the number of systematic reviews or surveys (SRoS) being published across various journals, covering topics like databases, methods, and general perspectives. This paper provides three key contributions: 1) A comprehensive analysis of the evolution of emotion recognition methods from 2002 to 2024, with particular emphasis on emotional body gesture recognition, documenting a clear transition from traditional machine learning to sophisticated deep learning architectures; 2) Identification and detailed analysis of the most impactful papers (the ‘‘cream of the crop’’) that have shaped body-based AffC methods, revealing that modern approaches increasingly use attention mechanisms, graph-based representations for skeletal data, and advanced spatial-temporal modeling techniques; and 3) A systematic categorization and analysis of emotion recognition methods across architectural types (machine learning, deep learning, and hybrid) and modalities (emotional body gesture recognition, facial emotion recognition, multimodal emotion recognition, and speech emotion recognition), demonstrating the field’s progression from unimodal to more robust multimodal approaches. Through an analysis of 10 selected SRoS papers published between 2021-2024, referencing 292 papers collectively, this study reveals critical challenges including limited availability of large-scale body-based emotional databases, computational demands of modern architectures, and cross-database generalization issues.
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Fundação para a Ciência e a Tecnologia
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5876
Número da atribuição
UID/CEC/04516/2013