ALLEGRO
Smart multi-modal crowdsensing-based system as a service oriented to the prediction of social problems
Project grant PID2020-112827GB-I00 funded by:
Smart multi-modal crowdsensing-based system as a service oriented to the prediction of social problems
Project grant PID2020-112827GB-I00 funded by:
ALLEGRO (Adaptive muLti-domain sociaL-media sEnsinG fRamewOrk) is a general-purpose multi-modal system for the development of real-time crowdsensing applications that can accurately reconstruct the state of the surrounding world as interpreted by the collective intelligence of social-media users. It is primarily focused on the data layer of crowdsensing systems, where raw data collected from social media along with other open-data sources (e.g. providers of meteorological, demographic, macroeconomic or geographical data) can be processed to extract relevant knowledge.
ALLEGRO consists of two modules (i.e. Data Analysis and Data Fusion), which make use of a multi-modal data repository and a knowledge base. In both modules, high performance computing (HPC) contributes to accomplish highly efficient real-time data processing. On the one hand, Data Analysis is comprised of a dedicated component for each type of data to be analysed in social-media posts, namely the Text Analysis module (DIAPASON, unifieD hybrId ApProach to microtext Analysis in Social-media crOwdseNsing), the Audio Analysis module (SOUND, Social-media sOUnd aNalysis moDule), and the Image Analysis module (ADAGIO, sociAl meDia imAGe analysIs mOdule). In this regard, microtext analysis is viewed as the initial process that provides the context of the problem described in each message, so that an event-based knowledge schema can be returned. In case that messages go with embedded audio and/or image content, this schema can be supplemented with context data from audio analysis and/or image analysis, which are concurrently executed, both to verify or rebut event-related information detected in the text or to complete missing information in the knowledge schema. The video content can also be taken into consideration for this context-augmentation process by analyzing its audio and image components separately in the corresponding modules. On the other hand, augmented knowledge schemas produced in this module are combined in the Data Fusion module (LAUD, sociaL mediA fUsion moDule), where the quality of aggregated data is enhanced by rejecting irrelevant information, minimizing redundancy, resolving inconsistencies, and completing missing information.
ALLEGRO takes an integral approach to smart cities, whose ultimate goal is to improve well-being and quality of life. To this end, social-media posts are analysed to reveal insights about problems that can disrupt social coexistence. Therefore, we focus not only on the quality of the urban infrastructure and services provided to citizens (e.g. environment, healthcare, lighting, traffic, and transportation, among others) but also on the understanding of the sociological dimension of the city, which is reflected through people's concerns (e.g. cultural and ethnic conflicts, economy and employment, poverty, and violence, just to name a few). As a result, in this proposal we use the term "smart city" to refer to both aspects, which can be viewed as two sides of the same coin. In this manner, our proposal offers a more complete alternative to the existing applications in smart cities, which are more focused on the first aspect.
Click on one of the following ALLEGRO modules:
Alameda Hernández, A. (in press) Social media detection of texts on the social problem of violence against women within the multimodal intelligent system ALLEGRO. En Jiménez-Briones, R. y Corral Esteban, A. (Eds.). Approaches to Knowledge Representation and Language [Colección Interlingua]. Granada: Editorial Comares.
Aragón-Jurado, J. M., Acuña-Vega, L. E., Ortiz, G., Boubeta-Puig, J., Muñoz, A. (2022) Detección inteligente de sucesos en Smart Cities con feedback de los ciudadanos. XVII Jornadas de Ingeniería de Ciencia e Ingeniería de Servicios (JCIS 2022). http://hdl.handle.net/11705/JCIS/2022/029
Balderas-Díaz, S., Muñoz, A., Guerrero-Contreras, G. (2023) Optimization of traffic light controllers using genetic algorithms: A case study in the city of Cádiz. Novais, P., et al. (eds.) Ambient Intelligence – Software and Applications – 14th International Symposium on Ambient Intelligence. ISAmI 2023. Lecture Notes in Networks and Systems, vol 770. Cham: Springer. https://doi.org/10.1007/978-3-031-43461-7_11
Bao, Y., Sun, Y., Feric, Z., Shen, M., Weston, M., Abellán, J. L., Baruah, T., Kim, J., Joshi, A., Kaeli, D. (2022) NaviSim: A highly accurate GPU simulator for AMD RDNA GPUs. Proceedings of the 31st International Conference on Parallel Architectures and Compilation Techniques (PACT 2022).
Cebrian, J. M., Imbernón, B., Soto, J., Cecilia, J. M. (2021). Evaluation of Clustering Algorithms on HPC Platforms. Mathematics, 9(17), 2156. https://doi.org/10.3390/math9172156
Cecilia, J. M., Morales-García, J., Imbernón, B., Prades, J., Cano, J. C., & Silla, F. (2023) Using remote GPU virtualization techniques to enhance edge computing devices. Future Generation Computer Systems 142, 14-24. https://doi.org/10.1016/j.future.2022.12.038
Curado, M., Rodriguez, R., Terroso-Saenz, F., Tortosa, L., Vicent, J. F. (2022) A centrality model for directed graphs based on the Two-Way-Random Path and associated indices for characterizing the nodes. Journal of Computational Science, 63, 101819. https://doi.org/10.1016/j.jocs.2022.101819
Curado, M., Tortosa, L., & Vicent, J. F. (2023) A novel measure to identify influential nodes: Return Random Walk Gravity Centrality. Information Sciences. https://doi.org/10.1016/j.ins.2023.01.097
Felices-Lago, Á. (in press) Social problems related to the ECONOMY dimension in the DIAPASON module: The case of the Income domain. En R. Jiménez-Briones y A. Corral Esteban (Eds.) Approaches to Knowledge Representation and Language [Colección Interlingua]. Granada: Comares.
Fernández-Martínez, N.J. (in press) Exploring the creation of synthetic corpora of negative communicative functions for the task of communicative function identification. En R. Jiménez-Briones & A. Corral Esteban (Eds.) Approaches to Knowledge Representation and Language [Colección Interlingua]. Granada: Editorial Comares.
Fernández-Martínez, N.J. (in press) Taxonomía de funciones comunicativas negativas para su identificación automática en el contexto de las ciudades inteligentes. En F. Olmo-Cazevieille (Ed.) Investigación lingüística en entornos digitales. Valencia: Tirant Lo Blanch.
Fernández Martínez, N. J. y Felices Lago, Á. (2021) Automatic lexical collocate extraction for corpus-based ontology building and refinement. Revista Española de Lingüística Aplicada 34:2, 435–463. https://doi.org/10.1075/resla.19030.fer
Garg, R., Qin, E., Muñoz-Martínez, F., Guirado, R., Jain, A., Abadal, S., Abellán, J. L., Acacio, M. E., Alarcón, E., Rajamanickam, S., Krishna, T. (2022) Understanding the design-space of sparse/dense multiphase GNN dataflows on spatial accelerators. Proceedings of the 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 571-582. https://doi.org/10.1109/IPDPS53621.2022.00062
Gonzalez-Vidal, A., Terroso-Saenz, F., & Skarmeta, A. (2022) Parking availability prediction with coarse-grained human mobility data. CMC-Computers, Materials & Continua 71(3), 4355-4375. https://techscience.com/cmc/v71n3/46462/pdf
Jiménez-Briones, R. y A. Felices Lago (in press). La formalización del conocimiento en DIAPASON a través de una muestra de problemas poblacionales y macroeconómicos. En F. Olmo-Cazevieille (Ed.) Investigación lingüística en entornos digitales. Valencia: Tirant Lo Blanch.
Jiménez-Briones, R. y A. Corral Esteban (Eds.) (in press). Approaches to Knowledge Representation and Language [Colección Interlingua]. Granada: Editorial Comares.
López, B., Arcas-Túnez, F., Cantabella, M., Terroso-Sáenz, F., Curado, M., Muñoz, A. (2022) EMO-Learning: Towards an intelligent tutoring system to assess online students' emotions. Proceedings of the 18th International Conference on Intelligent Environments, 1-4. https://doi.org/10.1109/IE54923.2022.9826770
Muñoz, A., Martínez-España, R., Guerrero-Contreras, G., Balderas-Díaz, S. and Bueno-Crespo, A. (2023) A real-time traffic alert system based on image recognition: A case of study in Spain. Proceedings of the 19th International Conference on Intelligent Environments (IE), Mauritius, pp. 1-7. https://doi.org/10.1109/IE57519.2023.10179106
Muñoz-Martínez, F., Abellán, J. L., Acacio, M. E., Krishna, T. (2022) STIFT: A spatio-temporal integrated folding tree for efficient reductions in flexible DNN accelerators. Journal of Emerging Technologies and Computing Systems. https://doi.org/10.1145/3531011
Navarro, J. M. & Pita, A. (2023). Machine learning prediction of the long-term environmental acoustic pattern of a city location using short-term sound pressure level measurements. Applied Sciences 13(3), 1613. https://doi.org/10.3390/app13031613
Navarro Ruiz, J.M., Terroso-Sáenz, F., Fernández, J.M. (2022) Ampliación de aplicación móvil de sonometría para la clasificación de fuentes sonoras. 53º Congreso Español de Acústica -Tecniacústica 2022.
Padilla-Quimbiulco, D., Morales-García, J., Cantabella, M., Ayuso, B., Muñoz, A. and Cecilia, J. M. (2023) Greenhouse intelligent warning system for precision agriculture. Proceedings of the 19th International Conference on Intelligent Environments (IE), Mauritius, pp. 1-6. https://doi.org/10.1109/IE57519.2023.10179105
Peña-Abreu, J., & Terroso-Saenz, F. (2023) Beach and weather: A mobile crowdsensing platform to report beach conditions. Software Impacts 15, 100472. https://doi.org/10.1016/j.simpa.2023.100472
Pérez-Sala, L., Curado, M., Tortosa, L., & Vicent, J. F. (2023) Deep learning model of convolutional neural networks powered by a genetic algorithm for prevention of traffic accidents severity. Chaos, Solitons & Fractals 169, 113245. https://doi.org/10.1016/j.chaos.2023.113245
Periñán-Pascual, C. (2022) Measuring associational thinking through word embeddings. Artificial Intelligence Review 55, 2065-2102. https://doi.org/10.1007/s10462-021-10056-6
Periñán-Pascual, C. (2023) From Smart City to Smart Society: A quality-of-life ontological model for problem detection from user-generated content. Applied Ontology 18 (3), pp. 263-306. https://jopepas3.webs.upv.es/cv/resources/papers/072.pdf
Periñán-Pascual, C. (2023) Exploring user-generated content to detect community problems: The ontological model of ALLEGRO, 15th International Conference on Knowledge Engineering and Ontology Development (KEOD 2023), Rome, 13-15 November 2023. David Aveiro, Jan Dietz, Antonella Poggi and Jorge Bernardino (eds.) Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2023), vol. 2: KEOD, SCITEPRESS, Setúbal, pp. 224-230. https://www.scitepress.org/Papers/2023/122033/122033.pdf
Periñán-Pascual, C. (2023) Un uso sostenible de WordNet en la inteligencia artificial. Quaderns de Filologia: Estudis Lingüístics 28: pp. 141-155. https://ojs.uv.es/index.php/qfilologia/article/download/26595/23572
Periñán-Pascual, C. (2024) Minería de textos para investigadores lingüistas. Valencia: Tirant lo Blanch.
Periñán-Pascual, C. (in press) Exploring problems through social media: the case of Beach Quality. En Jiménez-Briones, Rocío y Corral Esteban, Avelino (eds.) (in press). Approaches to Knowledge Representation and Language. Colección Interlingua. Granada: Comares.
Periñán-Pascual, C. (in press) Modelización de las quejas de los ciudadanos como artefactos digitales culturales: DIAPASON. En Françoise Olmo-Cazevieille (ed.) Investigación lingüística en entornos digitales. Valencia: Tirant Lo Blanch.
Periñán-Pascual, C. (in press) Modelling governance-related problems through social media. En Henry Tyne, Mireille Bilger, Laurie Buscail, Maï Leray, Niall Curry & Carmen Pérez-Sabater (eds.) Discovering language: Learning and affordance. Peter Lang.
Pita, A., Rodriguez, F.J., Navarro, J.M. (2022) Analysis and evaluation of clustering techniques applied to wireless acoustics sensor network data. Applied Sciences 12(17): 8550. https://doi.org/10.3390/app12178550
Pita, A., Rodriguez, F.J., Navarro, J.M. (2022) On the application of unsupervised clustering to sound pressure data from an acoustic sensors network. Ambient Intelligence and Smart Environments 31: Workshops at 18th International Conference on Intelligent Environments (IE2022). Amsterdam: IOS Press. https://ebooks.iospress.nl/volumearticle/60119
Rodríguez, R., Curado, M., Tortosa, L., & Vicent, J. F. (2023) Understanding the metallic glasses formation by applying a centrality measure based on betweenness. Computational Materials Science, 218, 111986. https://doi.org/10.1016/j.commatsci.2022.111986
Sáenz, F. T., Arcas-Tunez, F., & Muñoz, A. (2023) Nation-wide touristic flow prediction with graph neural networks and heterogeneous open data. Information Fusion 91, 582-597. https://doi.org/10.1016/j.inffus.2022.11.005
Segura-Garcia, J., Pérez-Solano, J. J., Felici-Castell, S., Montoya-Belmonte, J., Lopez-Ballester, J., & Navarro, J. M. (2023). Sustainable soundscape monitoring of modified psycho-acoustic annoyance model with edge computing for 5G IoT systems. Sustainability 15(13), 10016. https://doi.org/10.3390/su151310016
Sepúlveda, A., Periñán-Pascual, C., Muñoz, A., Martínez-España, R., Hernández-Orallo, E., Cecilia, J. M. (2021) COVIDSensing: Social sensing strategy for the management of the COVID-19 crisis. Electronics, 10(24), 3157. https://doi.org/10.3390/electronics10243157
Shivdikar, K., Jonatan, G., Mora, E., Livesay, N., Agrawal, R., Joshi, A., Abellan, J. L., Kim, J., Kaeli, D. (2022) Accelerating polynomial multiplication for homomorphic encryption on GPUs. Proceedings of the 2022 IEEE International Symposium on Secure and Private Execution Environment Design (SEED 2022).
Sidhu, N., Terroso-Sáenz, F., Ortiz, G., Muñoz, A. (2023). A sustainable framework to manage plastic waste in urban environments using open data. Da Silva Portela, C.F. (eds.) Sustainable, Innovative and Intelligent Societies and Cities. EAI/Springer Innovations in Communication and Computing. Cham: Springer. https://doi.org/10.1007/978-3-031-30514-6_12
Terroso-Saenz, F., Flores, R., Muñoz, A. (2022) Human mobility forecasting with region-based flows and geotagged Twitter data. Expert Systems with Applications, 117477. https://doi.org/10.1016/j.eswa.2022.117477
Terroso-Sáenz, F., Martínez-Guillén, D., & Llanes, A. (2022) An Open Souce System for People Counting based on 802.11 Packets Tracking. 2022 IEEE Symposium on Wireless Technology & Applications, 17-18 agosto 2022, Kuala Lumpur (Malaysia), IEEE, 41-46. https://doi.org/10.1109/ISWTA55313.2022.9942729
Terroso-Saenz, F., Morales-García, J., & Muñoz, A. (2023) Nationwide air pollution forecasting with heterogeneous graph neural networks. ACM Transactions on Intelligent Systems and Technology https://doi.org/10.1145/3637492
Terroso-Sáenz, F., & Muñoz, A. (2022) Nation-wide human mobility prediction based on graph neural networks. Applied Intelligence, 1-17. https://doi.org/10.1007/s10489-021-02645-3
Terroso-Saenz, F., & Muñoz, A. (2023) Human mobility prediction with region-based flows and road traffic data. JUCS: Journal of Universal Computer Science, 29(4). https://lib.jucs.org/article/94514/
Terroso-Saenz, F., Muñoz, A., Arcas, F., Curado, M. (2022) Can Twitter be a reliable proxy to characterize nation-wide human mobility? A case study of Spain. Social Science Computer Review, 08944393211071071. https://doi.org/10.1177/08944393211071071
Terroso-Saenz, F., Muñoz, A., Arcas, F., Curado, M. (2022) An analysis of Twitter as a relevant human mobility proxy. Geoinformatica 26, 677–706. https://doi.org/10.1007/s10707-021-00460-z
Terroso-Sáenz, F., Muñoz, A., & Roose, P. (2023). War & music: The impact of the Ukrainian war on the music listening behaviour in Eastern Europe. Proceedings of the 25th IEEE International Symposium on Multimedia, IEEE. https://univ-pau.hal.science/hal-04311398/file/War_and_music_ISM2023_Camera_Ready.pdf
Terroso-Saenz, F., Soto, J., & Muñoz, A. (2023) Evolution of global music trends: An exploratory and predictive approach based on Spotify data. Entertainment Computing 44, 100536. https://doi.org/10.1016/j.entcom.2022.100536
Terroso-Saenz, F., Soto, J., & Muñoz, A. (2023) Music mobility patterns: How songs propagate around the world through Spotify. Pattern Recognition 143, 109807. https://www.sciencedirect.com/science/article/pii/S0031320323005058
Terroso-Saenz, F., Soto, J., Muñoz, A. (2023) SINATRA: A music genre classifier based on clustering and graph analysis. Daimi, K., Alsadoon, A., Coelho, L. (eds.) Cutting Edge Applications of Computational Intelligence Tools and Techniques. Studies in Computational Intelligence, vol 1118. Cham: Springer. https://link.springer.com/chapter/10.1007/978-3-031-44127-1_9
Ureña Gómez-Moreno, P. (in press) Integrating corpus methodology into the construction of an intelligent crowdsensing system. En Jiménez-Briones, R. & Corral Esteban, A. (Eds.) Approaches to Knowledge Representation and Language [Colección Interlingua]. Granada: Editorial Comares.
Ureña Góméz-Moreno, P y Alameda Hernández, A. (in press) La detección de problemas sociales: una propuesta desde el procesamiento lingüístico de las redes sociales. En Olmo-Cazevieille, F. Investigación lingüística en entornos digitales. Valencia: Tirant Lo Blanch.