A Fine-Grained Model to Assess Learner-Content and Methodology Satisfaction in Distance Education

Learning Management System (LMS) platforms have led to a transformation in Universities in the last decade, helping them to adapt and expand their services to new technological challenges. These platforms have made possible the expansion of distance education. A current trend in this area is focused on the evaluation and improvement of the students’ satisfaction. In this work a new tool to assess student satisfaction using emoticons (smileys) is proposed to evaluate the quality of the learning content and the methodology at unit level for any course and at any time. The results indicate that the assessment of student satisfaction is sensitive to the period when the survey is performed and to the student’s study level. Moreover, the results of this new proposal are compared to the satisfaction results using traditional surveys, showing different results due to a more accuracy and flexibility when using the tool proposed in this work

Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network

[EN] Wireless acoustic sensor networks are nowadays an essential tool for noise pollution monitoring and managing in cities. The increased computing capacity of the nodes that create the network is allowing the addition of processing algorithms and artificial intelligence that provide more information about the sound sources and environment, e.g., detect sound events or calculate loudness. Several models to predict sound pressure levels in cities are available, mainly road, railway and aerial traffic noise. However, these models are mostly based in auxiliary data, e.g., vehicles flow or street geometry, and predict equivalent levels for a temporal long-term. Therefore, forecasting of temporal short-term sound levels could be a helpful tool for urban planners and managers. In this work, a Long Short-Term Memory (LSTM) deep neural network technique is proposed to model temporal behavior of sound levels at a certain location, both sound pressure level and loudness level, in order to predict near-time future values. The proposed technique can be trained for and integrated in every node of a sensor network to provide novel functionalities, e.g., a method of early warning against noise pollution and of backup in case of node or network malfunction. To validate this approach, one-minute period equivalent sound levels, captured in a two-month measurement campaign by a node of a deployed network of acoustic sensors, have been used to train it and to obtain different forecasting models. Assessments of the developed LSTM models and Auto regressive integrated moving average models were performed to predict sound levels for several time periods, from 1 to 60 min. Comparison of the results show that the LSTM models outperform the statistics-based models. In general, the LSTM models achieve a prediction of values with a mean square error less than 4.3 dB for sound pressure level and less than 2 phons for loudness. Moreover, the goodness of fit of the LSTM models and the behavior pattern of the data in terms of prediction of sound levels are satisfactory.This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18.Navarro, JM.; Martínez-España, R.; Bueno-Crespo, A.; Cecilia-Canales, JM.; Martínez, R. (2020). Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network. Sensors. 20(3):1-16. https://doi.org/10.3390/s20030903S116203Hornikx, M. (2016). Ten questions concerning computational urban acoustics. Building and Environment, 106, 409-421. doi:10.1016/j.buildenv.2016.06.028Murphy, E., & King, E. A. (2010). Strategic environmental noise mapping: Methodological issues concerning the implementation of the EU Environmental Noise Directive and their policy implications. Environment International, 36(3), 290-298. doi:10.1016/j.envint.2009.11.006Arana, M., San Martin, R., San Martin, M. L., & Aramendía, E. (2009). Strategic noise map of a major road carried out with two environmental prediction software packages. Environmental Monitoring and Assessment, 163(1-4), 503-513. doi:10.1007/s10661-009-0853-5Garg, N., & Maji, S. (2014). A critical review of principal traffic noise models: Strategies and implications. Environmental Impact Assessment Review, 46, 68-81. doi:10.1016/j.eiar.2014.02.001Steele, C. (2001). A critical review of some traffic noise prediction models. Applied Acoustics, 62(3), 271-287. doi:10.1016/s0003-682x(00)00030-xLi, B., Tao, S., Dawson, R. W., Cao, J., & Lam, K. (2002). A GIS based road traffic noise prediction model. Applied Acoustics, 63(6), 679-691. doi:10.1016/s0003-682x(01)00066-4VAN LEEUWEN, H. J. A. (2000). RAILWAY NOISE PREDICTION MODELS: A COMPARISON. Journal of Sound and Vibration, 231(3), 975-987. doi:10.1006/jsvi.1999.2570Lui, W. K., Li, K. M., Ng, P. L., & Frommer, G. H. (2006). A comparative study of different numerical models for predicting train noise in high-rise cities. Applied Acoustics, 67(5), 432-449. doi:10.1016/j.apacoust.2005.08.005Van Leeuwen, J. J. A. (1996). NOISE PREDICTIONS MODELS TO DETERMINE THE EFFECT OF BARRIERS PLACED ALONGSIDE RAILWAY LINES. Journal of Sound and Vibration, 193(1), 269-276. doi:10.1006/jsvi.1996.0267Oerlemans, S., & Schepers, J. G. (2009). Prediction of Wind Turbine Noise and Validation against Experiment. International Journal of Aeroacoustics, 8(6), 555-584. doi:10.1260/147547209789141489Tadamasa, A., & Zangeneh, M. (2011). Numerical prediction of wind turbine noise. Renewable Energy, 36(7), 1902-1912. doi:10.1016/j.renene.2010.11.036Maisonneuve, N., Stevens, M., & Ochab, B. (2010). Participatory noise pollution monitoring using mobile phones. Information Polity, 15(1,2), 51-71. doi:10.3233/ip-2010-0200Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393-422. doi:10.1016/s1389-1286(01)00302-4Peckens, C., Porter, C., & Rink, T. (2018). Wireless Sensor Networks for Long-Term Monitoring of Urban Noise. Sensors, 18(9), 3161. doi:10.3390/s18093161Alías, F., & Alsina-Pagès, R. M. (2019). Review of Wireless Acoustic Sensor Networks for Environmental Noise Monitoring in Smart Cities. Journal of Sensors, 2019, 1-13. doi:10.1155/2019/7634860Mydlarz, C., Salamon, J., & Bello, J. P. (2017). The implementation of low-cost urban acoustic monitoring devices. Applied Acoustics, 117, 207-218. doi:10.1016/j.apacoust.2016.06.010Navarro, J. M., Tomas-Gabarron, J. B., & Escolano, J. (2017). A Big Data Framework for Urban Noise Analysis and Management in Smart Cities. Acta Acustica united with Acustica, 103(4), 552-560. doi:10.3813/aaa.919084Längkvist, M., Karlsson, L., & Loutfi, A. (2014). A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters, 42, 11-24. doi:10.1016/j.patrec.2014.01.008Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018). Recurrent Neural Networks for Multivariate Time Series with Missing Values. Scientific Reports, 8(1). doi:10.1038/s41598-018-24271-9Kim, H.-G., & Kim, J. Y. (2017). Environmental sound event detection in wireless acoustic sensor networks for home telemonitoring. China Communications, 14(9), 1-10. doi:10.1109/cc.2017.8068759Luque, A., Romero-Lemos, J., Carrasco, A., & Barbancho, J. (2018). Improving Classification Algorithms by Considering Score Series in Wireless Acoustic Sensor Networks. Sensors, 18(8), 2465. doi:10.3390/s18082465Zhang, Y., Fu, Y., & Wang, R. (2018). Collaborative representation based classification for vehicle recognition in acoustic sensor networks. Journal of Computational Methods in Sciences and Engineering, 18(2), 349-358. doi:10.3233/jcm-180794Cobos, M., Perez-Solano, J. J., Felici-Castell, S., Segura, J., & Navarro, J. M. (2014). Cumulative-Sum-Based Localization of Sound Events in Low-Cost Wireless Acoustic Sensor Networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12), 1792-1802. doi:10.1109/taslp.2014.2351132Sevillano, X., Socoró, J. C., Alías, F., Bellucci, P., Peruzzi, L., Radaelli, S., … Zambon, G. (2016). DYNAMAP – Development of low cost sensors networks for real time noise mapping. Noise Mapping, 3(1). doi:10.1515/noise-2016-0013Segura-Garcia, J., Navarro-Ruiz, J., Perez-Solano, J., Montoya-Belmonte, J., Felici-Castell, S., Cobos, M., & Torres-Aranda, A. (2018). Spatio-Temporal Analysis of Urban Acoustic Environments with Binaural Psycho-Acoustical Considerations for IoT-Based Applications. Sensors, 18(3), 690. doi:10.3390/s18030690Bello, J. P., Silva, C., Nov, O., Dubois, R. L., Arora, A., Salamon, J., … Doraiswamy, H. (2019). SONYC. Communications of the ACM, 62(2), 68-77. doi:10.1145/3224204Socoró, J., Alías, F., & Alsina-Pagès, R. (2017). An Anomalous Noise Events Detector for Dynamic Road Traffic Noise Mapping in Real-Life Urban and Suburban Environments. Sensors, 17(10), 2323. doi:10.3390/s17102323Yu, L., & Kang, J. (2009). Modeling subjective evaluation of soundscape quality in urban open spaces: An artificial neural network approach. The Journal of the Acoustical Society of America, 126(3), 1163-1174. doi:10.1121/1.3183377Lopez-Ballester, J., Pastor-Aparicio, A., Segura-Garcia, J., Felici-Castell, S., & Cobos, M. (2019). Computation of Psycho-Acoustic Annoyance Using Deep Neural Networks. Applied Sciences, 9(15), 3136. doi:10.3390/app9153136Mansourkhaki, A., Berangi, M., Haghiri, M., & Haghani, M. (2018). A NEURAL NETWORK NOISE PREDICTION MODEL FOR TEHRAN URBAN ROADS. Journal of Environmental Engineering and Landscape Management, 26(2), 88-97. doi:10.3846/16486897.2017.1356327Pedersen, K., Transtrum, M. K., Gee, K. L., Butler, B. A., James, M. M., & Salton, A. R. (2018). Machine learning-based ensemble model predictions of outdoor ambient sound levels. 2019 International Congress on Ultrasonics. doi:10.1121/2.0001056Torija, A. J., Ruiz, D. P., & Ramos-Ridao, A. F. (2012). Use of back-propagation neural networks to predict both level and temporal-spectral composition of sound pressure in urban sound environments. Building and Environment, 52, 45-56. doi:10.1016/j.buildenv.2011.12.024Garg, N., Soni, K., Saxena, T. K., & Maji, S. (2015). Applications of AutoRegressive Integrated Moving Average (ARIMA) approach in time-series prediction of traffic noise pollution. Noise Control Engineering Journal, 63(2), 182-194. doi:10.3397/1/376317Tong, W., Li, L., Zhou, X., Hamilton, A., & Zhang, K. (2019). Deep learning PM2.5 concentrations with bidirectional LSTM RNN. Air Quality, Atmosphere & Health, 12(4), 411-423. doi:10.1007/s11869-018-0647-4Krishan, M., Jha, S., Das, J., Singh, A., Goyal, M. K., & Sekar, C. (2019). Air quality modelling using long short-term memory (LSTM) over NCT-Delhi, India. Air Quality, Atmosphere & Health, 12(8), 899-908. doi:10.1007/s11869-019-00696-7Noriega-Linares, J. E., Rodriguez-Mayol, A., Cobos, M., Segura-Garcia, J., Felici-Castell, S., & Navarro, J. M. (2017). A Wireless Acoustic Array System for Binaural Loudness Evaluation in Cities. IEEE Sensors Journal, 17(21), 7043-7052. doi:10.1109/jsen.2017.2751665Raspberry PI https://www.raspberrypi.orgLegates, D. R., & McCabe, G. J. (1999). Evaluating the use of «goodness-of-fit» Measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1), 233-241. doi:10.1029/1998wr90001

A high-performance IoT solution to reduce frost damages in stone fruits

[EN] Agriculture is one of the key sectors where technology is opening new opportunities to break up the market. The Internet of Things (IoT) could reduce the production costs and increase the product quality by providing intelligence services via IoT analytics. However, the hard weather conditions and the lack of connectivity in this field limit the successful deployment of such services as they require both, ie, fully connected infrastructures and highly computational resources. Edge computing has emerged as a solution to bring computing power in close proximity to the sensors, providing energy savings, highly responsive web services, and the ability to mask transient cloud outages. In this paper, we propose an IoT monitoring system to activate anti-frost techniques to avoid crop loss, by defining two intelligent services to detect outliers caused by the sensor errors. The former is a nearest neighbor technique and the latter is the k-means algorithm, which provides better quality results but it increases the computational cost. Cloud versus edge computing approaches are analyzed by targeting two different low-power GPUs. Our experimental results show that cloud-based approaches provides highest performance in general but edge computing is a compelling alternative to mask transient cloud outages and provide highly responsive data analytic services in technologically hostile environments.This work was partially supported by the Fundación Séneca del Centro de Coordinación de la Investigación de la Región de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants TIN2016-78799-P (AEI/FEDER, UE) and RTC-2017-6389-5. Finally, we thank the farmers for the availability of their resources to be able to asses and improve the IoT monitoring system proposed.Guillén-Navarro, MA.; Martínez-España, R.; López, B.; Cecilia-Canales, JM. (2021). A high-performance IoT solution to reduce frost damages in stone fruits. Concurrency and Computation: Practice and Experience (Online). 33(2):1-14. https://doi.org/10.1002/cpe.529911433

COVIDSensing: Social Sensing strategy for the management of the COVID-19 crisis

[EN] The management of the COVID-19 pandemic has been shown to be critical for reducing its dramatic effects. Social sensing can analyse user-contributed data posted daily in social-media services, where participants are seen as Social Sensors. Individually, social sensors may provide noisy information. However, collectively, such opinion holders constitute a large critical mass dispersed everywhere and with an immediate capacity for information transfer. The main goal of this article is to present a novel methodological tool based on social sensing, called COVIDSensing. In particular, this application serves to provide actionable information in real time for the management of the socio-economic and health crisis caused by COVID-19. This tool dynamically identifies socio-economic problems of general interest through the analysis of people¿s opinions on social networks. Moreover, it tracks and predicts the evolution of the COVID-19 pandemic based on epidemiological figures together with the social perceptions towards the disease. This article presents the case study of Spain to illustrate the tool.This work is derived from R&D project RTI2018-096384-B-I00, as well as the Ramon y Cajal Grant RYC2018-025580-I, funded by MCIN/AEI/10.13039/501100011033 and ERDF A way of making Europe, by the Spanish Agencia Estatal de Investigación (grant number PID2020- 112827GB-I00/ AEI/10.13039/501100011033), and by the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Proyectos AICO/2020, Spain, under Grant AICO/2020/302.Sepúlveda, A.; Periñán-Pascual, C.; Muñoz, A.; Martínez-España, R.; Hernández-Orallo, E.; Cecilia-Canales, JM. (2021). COVIDSensing: Social Sensing strategy for the management of the COVID-19 crisis. Electronics. 10(24):1-17. https://doi.org/10.3390/electronics10243157S117102

A Decision Support System for Water Optimization in Anti-Frost Techniques by Sprinklers

[EN] Precision agriculture is a growing sector that improves traditional agricultural processes through the use of new technologies. In southeast Spain, farmers are continuously fighting against harsh conditions caused by the effects of climate change. Among these problems, the great variability of temperatures (up to 20 degrees C in the same day) stands out. This causes the stone fruit trees to flower prematurely and the low winter temperatures freeze the flower causing the loss of the crop. Farmers use anti-freeze techniques to prevent crop loss and the most widely used techniques are those that use water irrigation as they are cheaper than other techniques. However, these techniques waste too much water and it is a scarce resource, especially in this area. In this article, we propose a novel intelligent Internet of Things (IoT) monitoring system to optimize the use of water in these anti-frost techniques while minimizing crop loss. The intelligent component of the IoT system is designed using an approach based on a multivariate Long Short-Term Memory (LSTM) model, designed to predict low temperatures. We compare the proposed approach of multivariate model with the univariate counterpart version to figure out which model obtains better accuracy to predict low temperatures. An accurate prediction of low temperatures would translate into significant water savings, as anti-frost techniques would not be activated without being necessary. Our experimental results show that the proposed multivariate LSTM approach improves the univariate counterpart version, obtaining an average quadratic error no greater than 0.65 degrees C and a coefficient of determination R2 greater than 0.97. The proposed system has been deployed and is currently operating in a real environment obtained satisfactory performance.This work has been partially supported by the Spanish Ministry of Science and Innovation, under the Ramon y Cajal Program (Grant No. RYC2018-025580-I) and under grants RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5, by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by the "Conselleria de Educacion, Investigacion, Cultura y Deporte, Direccio General de Ciencia i Investigacio, Proyectos AICO/2020", Spain, under Grant AICO/2020/302.Guillén-Navarro, MA.; Martínez-España, R.; Bueno-Crespo, A.; Morales-García, J.; Ayuso, B.; Cecilia-Canales, JM. (2020). A Decision Support System for Water Optimization in Anti-Frost Techniques by Sprinklers. Sensors. 20(24):1-15. https://doi.org/10.3390/s20247129S1152024Melgarejo-Moreno, J., López-Ortiz, M.-I., & Fernández-Aracil, P. (2019). Water distribution management in South-East Spain: A guaranteed system in a context of scarce resources. Science of The Total Environment, 648, 1384-1393. doi:10.1016/j.scitotenv.2018.08.263Ferrández-Pastor, F., García-Chamizo, J., Nieto-Hidalgo, M., & Mora-Martínez, J. (2018). Precision Agriculture Design Method Using a Distributed Computing Architecture on Internet of Things Context. Sensors, 18(6), 1731. doi:10.3390/s18061731Liaghat. (2010). A Review: The Role of Remote Sensing in Precision Agriculture. American Journal of Agricultural and Biological Sciences, 5(1), 50-55. doi:10.3844/ajabssp.2010.50.55Nelson, G. C., van der Mensbrugghe, D., Ahammad, H., Blanc, E., Calvin, K., Hasegawa, T., … Willenbockel, D. (2013). Agriculture and climate change in global scenarios: why don’t the models agree. Agricultural Economics, 45(1), 85-101. doi:10.1111/agec.12091Crookston, R. K. (2006). A Top 10 List of Developments and Issues Impacting Crop Management and Ecology During the Past 50 Years. Crop Science, 46(5), 2253-2262. doi:10.2135/cropsci2005.11.0416gasDutta, R., Morshed, A., Aryal, J., D’Este, C., & Das, A. (2014). Development of an intelligent environmental knowledge system for sustainable agricultural decision support. Environmental Modelling & Software, 52, 264-272. doi:10.1016/j.envsoft.2013.10.004Zhang, J., Zhu, Y., Zhang, X., Ye, M., & Yang, J. (2018). Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas. Journal of Hydrology, 561, 918-929. doi:10.1016/j.jhydrol.2018.04.065Sahoo, S., Russo, T. A., Elliott, J., & Foster, I. (2017). Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S. Water Resources Research, 53(5), 3878-3895. doi:10.1002/2016wr019933Coopersmith, E. J., Minsker, B. S., Wenzel, C. E., & Gilmore, B. J. (2014). Machine learning assessments of soil drying for agricultural planning. Computers and Electronics in Agriculture, 104, 93-104. doi:10.1016/j.compag.2014.04.004Mohammadi, K., Shamshirband, S., Motamedi, S., Petković, D., Hashim, R., & Gocic, M. (2015). Extreme learning machine based prediction of daily dew point temperature. Computers and Electronics in Agriculture, 117, 214-225. doi:10.1016/j.compag.2015.08.008Feng, Y., Peng, Y., Cui, N., Gong, D., & Zhang, K. (2017). Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data. Computers and Electronics in Agriculture, 136, 71-78. doi:10.1016/j.compag.2017.01.027Jin, X.-B., Yu, X.-H., Wang, X.-Y., Bai, Y.-T., Su, T.-L., & Kong, J.-L. (2020). Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System. Sustainability, 12(4), 1433. doi:10.3390/su12041433Castañeda-Miranda, A., & Castaño-Meneses, V. M. (2020). Internet of things for smart farming and frost intelligent control in greenhouses. Computers and Electronics in Agriculture, 176, 105614. doi:10.1016/j.compag.2020.105614Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of Things in agriculture, recent advances and future challenges. Biosystems Engineering, 164, 31-48. doi:10.1016/j.biosystemseng.2017.09.007Shi, X., An, X., Zhao, Q., Liu, H., Xia, L., Sun, X., & Guo, Y. (2019). State-of-the-Art Internet of Things in Protected Agriculture. Sensors, 19(8), 1833. doi:10.3390/s19081833Jawad, H., Nordin, R., Gharghan, S., Jawad, A., & Ismail, M. (2017). Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review. Sensors, 17(8), 1781. doi:10.3390/s17081781Guillén‐Navarro, M. A., Martínez‐España, R., López, B., & Cecilia, J. M. (2019). A high‐performance IoT solution to reduce frost damages in stone fruits. Concurrency and Computation: Practice and Experience, 33(2). doi:10.1002/cpe.5299Guillén, M. A., Llanes, A., Imbernón, B., Martínez-España, R., Bueno-Crespo, A., Cano, J.-C., & Cecilia, J. M. (2020). Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. The Journal of Supercomputing, 77(1), 818-840. doi:10.1007/s11227-020-03288-

Inequality for wage earners and self-employed : evidence from panel data

In this paper we study the evolution of income inequality for employees and self-employed workers. We highlight the importance of separately analyzing these different sources of income to gain a broader understanding of inequality. Using Spanish panel data on income and consumption from the ECPF for the period 1987-96, we decompose the variance of income shocks into a permanent and a transitory component. We find that there are noticeable differences in the evolution of income inequality, as well as in the relative importance of the permanent and transitory components across these groups. Our results point that the evolution of inequality can be basically explained by movements in the variance of the transitory component of income for the self-employed, while for the employees it is mainly driven by the variance of the permanent component, specially at the end of the period. Given these disparities, it seems that these two sources of income should be studied separately and that different policies are suitable for each grou

Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning

[EN] The Internet of Things (IoT) is driving the digital revolution. AlSome palliative measures aremost all economic sectors are becoming "Smart" thanks to the analysis of data generated by IoT. This analysis is carried out by advance artificial intelligence (AI) techniques that provide insights never before imagined. The combination of both IoT and AI is giving rise to an emerging trend, called AIoT, which is opening up new paths to bring digitization into the new era. However, there is still a big gap between AI and IoT, which is basically in the computational power required by the former and the lack of computational resources offered by the latter. This is particularly true in rural IoT environments where the lack of connectivity (or low-bandwidth connections) and power supply forces the search for "efficient" alternatives to provide computational resources to IoT infrastructures without increasing power consumption. In this paper, we explore edge computing as a solution for bridging the gaps between AI and IoT in rural environment. We evaluate the training and inference stages of a deep-learning-based precision agriculture application for frost prediction in modern Nvidia Jetson AGX Xavier in terms of performance and power consumption. Our experimental results reveal that cloud approaches are still a long way off in terms of performance, but the inclusion of GPUs in edge devices offers new opportunities for those scenarios where connectivity is still a challenge.This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants RTI2018-096384-B-I00 (AEI/FEDER, UE) and RTC-2017-6389-5.Guillén-Navarro, MA.; Llanes, A.; Imbernón, B.; Martínez-España, R.; Bueno-Crespo, A.; Cano, J.; Cecilia-Canales, JM. (2021). Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. The Journal of Supercomputing. 77:818-840. https://doi.org/10.1007/s11227-020-03288-w8188407

Bioinspired Architecture Selection for Multitask Learning

Faced with a new concept to learn, our brain does not work in isolation. It uses all previously learned knowledge. In addition, the brain is able to isolate the knowledge that does not benefit us, and to use what is actually useful. In machine learning, we do not usually benefit from the knowledge of other learned tasks. However, there is a methodology called Multitask Learning (MTL), which is based on the idea that learning a task along with other related tasks produces a transfer of information between them, what can be advantageous for learning the first one. This paper presents a new method to completely design MTL architectures, by including the selection of the most helpful subtasks for the learning of the main task, and the optimal network connections. In this sense, the proposed method realizes a complete design of the MTL schemes. The method is simple and uses the advantages of the Extreme Learning Machine to automatically design a MTL machine, eliminating those factors that hinder, or do not benefit, the learning process of the main task. This architecture is unique and it is obtained without testing/error methodologies that increase the computational complexity. The results obtained over several real problems show the good performances of the designed networks with this method

Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data

[EN] We are witnessing the dramatic consequences of the COVID¿19 pandemic which, unfortunately, go beyond the impact on the health system. Until herd immunity is achieved with vaccines, the only available mechanisms for controlling the pandemic are quarantines, perimeter closures and social distancing with the aim of reducing mobility. Governments only apply these measures for a reduced period, since they involve the closure of economic activities such as tourism, cultural activities, or nightlife. The main criterion for establishing these measures and planning socioeconomic subsidies is the evolution of infections. However, the collapse of the health system and the unpredictability of human behavior, among others, make it difficult to predict this evolution in the short to medium term. This article evaluates different models for the early prediction of the evolution of the COVID¿19 pandemic to create a decision support system for policy¿makers. We consider a wide branch of models including artificial neural networks such as LSTM and GRU and statistically based models such as autoregressive (AR) or ARIMA. Moreover, several consensus strategies to ensemble all models into one system are proposed to obtain better results in this uncertain environment. Finally, a multivariate model that includes mobility data provided by Google is proposed to better forecast trend changes in the 14¿day CI. A real case study in Spain is evaluated, providing very accurate results for the prediction of 14¿day CI in scenarios with and without trend changes, reaching 0.93 R2, 4.16 RMSE and 1.08 MAE.This work has been partially supported by the Spanish Ministry of Science and Innovation, under Grants RYC2018-025580-I, RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5, by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, by the "Conselleria de Educacion, Investigacion, Cultura y Deporte, Direccio General de Ciencia i Investigacio, Proyectos AICO/2020", Spain, under Grant AICO/2020/302 and a predoctoral contract by the Generalitat Valenciana and the European Social Fund under Grant ACIF/2018/219.García-Cremades, S.; Morales-García, J.; Hernández-Sanjaime, R.; Martínez-España, R.; Bueno-Crespo, A.; Hernández-Orallo, E.; López-Espín, JJ.... (2021). Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data. Scientific Reports. 11(1):1-16. https://doi.org/10.1038/s41598-021-94696-2S11611