Artículos (Lenguajes y Sistemas Informáticos)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11392

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Mostrando 1 - 6 de 6

A Survey on Energy Expenditure Estimation Using Wearable Devices
(ACM, 2020-09) Álvarez García, Juan Antonio; Božidara Cvetković; Mitja Luštrek; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Educación, Cultura y Deporte (MECD). España; Ministerio de Economía y Competitividad (MINECO). España; European Commission (EC); European Union (UE); Universidad de Sevilla. TIC134: Sistemas Informáticos
Human Energy Expenditure (EE) is a valuable tool for measuring physical activity and its impact on our body in an objective way. To accurately measure the EE, there are methods such as doubly labeled water and direct and indirect calorimetry, but their cost and practical limitations make them suitable only for research and professional sports. This situation, combined with the proliferation of commercial activity monitors, has stimulated the research of EE estimation (EEE) using machine learning on multimodal data from wearable devices. The article provides an overview of existing work in this evolving field, categorizes it, and makes publicly available an EEE dataset. Such a dataset is one of the most valuable resources for the development of the field but is generally not provided by researchers due to the high cost of collection. Finally, the article highlights best practices and promising future direction for designing EEE methods.
Cross-domain polarity classification using a knowledge-enhanced meta-classifier
(Elsevier, 2015) Franco Salvador, Marc; Cruz Mata, Fermín; Troyano Jiménez, José Antonio; Rosso, Paolo; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; European Commission (EC); Ministerio de Economía y Competitividad (MINECO). España; Ministerio de Economía y Competitividad (MINECO). España; Junta de Andalucía
Current approaches to single and cross-domain polarity classification usually use bag of words, n-grams or lexical resource-based classifiers. In this paper, we propose the use of meta-learning to combine and enrich those approaches by adding also other knowledge-based features. In addition to the aforementioned classical approaches, our system uses the BabelNet multilingual semantic network to generate features derived from word sense disambiguation and vocabulary expansion. Experimental results show state-of-the-art performance on single and cross-domain polarity classification. Contrary to other approaches, ours is generic. These results were obtained without any domain adaptation technique. Moreover, the use of meta-learning allows our approach to obtain the most stable results across domains. Finally, our empirical analysis provides interesting insights on the use of semantic network-based features.
Data Processing Unit for Energy Saving in Computer Vision: Weapon Detection Use Case
(MDPI, 2022-12-28) Perea Trigo , Marina; López Ortiz, Enrique J.; Salazar González, Jose Luis; Álvarez García, Juan Antonio; Vegas Olmos, Juan José; Universidad de Sevilla. Departamento de Ciencia de la Computación e Inteligencia Artificial; Universidad de Sevilla. Departamento de Lenguaje y Sistemas Informáticos; Ministerio de Ciencia e Innovación (MICIN). España; European Commission (EC)
The growth of the Internet has led to the emergence of servers that perform increasingly heavy tasks. Some servers must remain active 24 h a day, but the evolution of network cards has facilitated the use of Data Processing Units (DPUs) to reduce network traffic and alleviate server workloads. This capability makes DPUs good candidates for load alleviation in systems that perform continuous data processing when the data can be pre-filtered. Computer vision systems that use some form of artificial intelligence, such as facial recognition or weapon detection, tend to have high workloads and high power consumption, which is becoming increasingly costly. Reducing the workload is therefore desirable and possible in some scenarios. The main contributions of this study are threefold: (1) to explore the potential benefits of using a DPU to alleviate the workload of a 24-h active server; (2) to present a study that measures the workload reduction of a CCTV weapon detection system and evaluate its performance under different conditions. We observed a 43,123% reduction in workload over the 24 h of video used in the experimentation, reaching more than 98% savings during night hours, which significantly reduces system stress and has a direct impact on electrical energy expenditure; and (3) to provide a framework that can be adapted to other computer vision-based detection systems.
Evaluation of localization and activity recognition systems for ambient assisted living: The experience of the 2012 EvAAL competition
(IOS Press, 2013) Álvarez García, Juan Antonio; Barsocchi, Paolo; Chessa, Stefano; Salvi, Dario; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; European Commission (EC); Universidad de Sevilla. TIC-134: Sistemas Informáticos
EvAAL is an annual international competition that addresses the “grand” challenge of evaluation and comparison of Ambient Assisted Living (AAL) systems and platforms, with the final goal to assess the autonomy, independent living and quality of life that AAL systems may grant to their end users. The 2012 Edition was focused on two pillars of AAL: Indoor localization and activity recognition. Results from both competitions suggest that there is still space for other editions not only to improve accuracy of such systems, but also their user acceptance and interoperability. This paper describes the organization and results of the 2012 edition.
Generating a seismogenic source zone model for the Pyrenees: A GIS-assisted triclustering approach
(Elsevier, 2021) Amaro Mellado, José Lázaro; Melgar García, Laura; Rubio Escudero, Cristina; Gutiérrez Avilés, David; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; European Commission (EC); Ministerio de Economía y Competitividad (MINECO). España; Junta de Andalucía
Seismogenic source zone models, including the delineation and the characterization, still have a role to play in seismic hazard calculations, particularly in regions with moderate or low to moderate seismicity. Seismic source zones establish areas with common tectonic and seismic characteristics, described by a unique magnitude–frequency distribution. Their definition can be addressed from different views. Traditionally, the source zones have been geographically outlined from seismotectonic, geological structures, and earthquake catalogs. Geographic information systems (GIS) can be of great help in their definition, as they deal rigorously and less ambiguously with the available geographical data. Moreover, novel computer science approaches are now being employed in their definition. The Pyrenees mountain range – in southwest Europe – is located in a region characterized by low to moderate seismicity. In this study, a method based purely on seismic catalogs, managed with a GIS and a triclustering algorithm, were used to delineate seismogenic zones in the Pyrenees. Based on an updated, reviewed, declustered, extensive, and homogeneous earthquake catalog (including detailed information about each event such as date and time, hypocentral location, and size), a triclustering algorithm has been applied to generate the seismogenic zones. The method seeks seismicity patterns in a quasi-objective manner following an initial assessment as to the best suited seismic parameters. The eight zones identified as part of this study are represented on maps to be analyzed, being the zone covered by the Arudy–Arette region to Bagnères de Bigorre as the one with the highest seismic hazard potential.
Impact of plot size and model selection on forest biomass estimation using airborne LiDAR: A case study of pine plantations in southern Spain
(Czech Academy of Agricultural Sciences (CAAS), 2017) Navarro Cerrillo, Rafael M.; González Ferreiro, Eduardo; García Gutiérrez, Jorge; Ceacero Ruiz, Carlos J.; Hernández Clemente, Rocío; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; European Commission (EC); Ministerio de Economia, Industria y Competitividad (MINECO). España; Universidad de Sevilla. TIC-134: Sistemas Informáticos
We explored the usefulness of LiDAR for modelling and mapping the stand biomass of two conifer species in southern Spain. We used three different plot sizes and two statistical approaches (i.e. stepwise selection and genetic algorithm selection) in combination with multiple linear regression models to estimate biomass. 43 predictor variables derived from discrete-return LiDAR data (4 pulses per m2 ) were used for estimating the forest biomass of Pinus sylvestris Linnaeus and Pinus nigra Arnold forests. Twelve circular plots – six for each species – and three different fixed-radius designs (i.e. 7, 15, and 30 m) were estab lished within the range of the airborne LiDAR. The Bayesian information criterion and R2 were used to select the best models. As expected, the models that included the largest plots (30 m) yielded the highest R2 value (0.91) for Pinus sp. using genetic algorithm models. Considering P. sylvestris and P. nigra models separately, the genetic algorithm approach also yielded the highest R2 values for the 30-m plots (P. nigra: R2 = 0.99, P. sylvestris: R2 = 0.97). The results we obtained with two species and different plot sizes revealed that increasing the size of plots from 15 to 30 m had a low effect on modelling attempts.

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Examinando Artículos (Lenguajes y Sistemas Informáticos) por Agencia financiadora "European Commission (EC)"