Modelling of a System for the Detection of Weak Signals Through Text Mining and NLP. Proposal of Improvement by a Quantum Variational Circuit

Tesis por compendio[ES] En esta tesis doctoral se propone y evalúa un sistema para detectar señales débiles (weak signals) relacionadas con cambios futuros trascendentales. Si bien la mayoría de las soluciones conocidas se basan en el uso de datos estructurados, el sistema propuesto detecta cuantitativamente estas señales utilizando información heterogénea y no estructurada de fuentes científicas, periodísticas y de redes sociales. La predicción de nuevas tendencias en un medio tiene muchas aplicaciones. Por ejemplo, empresas y startups se enfrentan a cambios constantes en sus mercados que son muy difíciles de predecir. Por esta razón, el desarrollo de sistemas para detectar automáticamente cambios futuros significativos en una etapa temprana es relevante para que cualquier organización tome decisiones acertadas a tiempo. Este trabajo ha sido diseñado para obtener señales débiles del futuro en cualquier campo dependiendo únicamente del conjunto de datos de entrada de documentos. Se aplican técnicas de minería de textos y procesamiento del lenguaje natural para procesar todos estos documentos. Como resultado, se obtiene un mapa con un ranking de términos, una lista de palabras clave clasificadas automáticamente y una lista de expresiones formadas por múltiples palabras. El sistema completo se ha probado en cuatro sectores diferentes: paneles solares, inteligencia artificial, sensores remotos e imágenes médicas. Este trabajo ha obtenido resultados prometedores, evaluados con dos metodologías diferentes. Como resultado, el sistema ha sido capaz de detectar de forma satisfactoria nuevas tendencias en etapas muy tempranas que se han vuelto cada vez más importantes en la actualidad. La computación cuántica es un nuevo paradigma para una multitud de aplicaciones informáticas. En esta tesis doctoral también se presenta un estudio de las tecnologías disponibles en la actualidad para la implementación física de qubits y puertas cuánticas, estableciendo sus principales ventajas y desventajas, y los marcos disponibles para la programación e implementación de circuitos cuánticos. Con el fin de mejorar la efectividad del sistema, se describe un diseño de un circuito cuántico basado en máquinas de vectores de soporte (SVM) para la resolución de problemas de clasificación. Este circuito está especialmente diseñado para los ruidosos procesadores cuánticos de escala intermedia (NISQ) que están disponibles actualmente. Como experimento, el circuito ha sido probado en un computador cuántico real basado en qubits superconductores por IBM como una mejora para el subsistema de minería de texto en la detección de señales débiles. Los resultados obtenidos con el experimento cuántico muestran también conclusiones interesantes y una mejora en el rendimiento de cerca del 20% sobre los sistemas convencionales, pero a su vez confirman que aún se requiere un desarrollo tecnológico continuo para aprovechar al máximo la computación cuántica.[CA] En aquesta tesi doctoral es proposa i avalua un sistema per detectar senyals febles (weak signals) relacionats amb canvis futurs transcendentals. Si bé la majoria de solucions conegudes es basen en l'ús de dades estructurades, el sistema proposat detecta quantitativament aquests senyals utilitzant informació heterogènia i no estructurada de fonts científiques, periodístiques i de xarxes socials. La predicció de noves tendències en un medi té moltes aplicacions. Per exemple, empreses i startups s'enfronten a canvis constants als seus mercats que són molt difícils de predir. Per això, el desenvolupament de sistemes per detectar automàticament canvis futurs significatius en una etapa primerenca és rellevant perquè les organitzacions prenguen decisions encertades a temps. Aquest treball ha estat dissenyat per obtenir senyals febles del futur a qualsevol camp depenent únicament del conjunt de dades d'entrada de documents. S'hi apliquen tècniques de mineria de textos i processament del llenguatge natural per processar tots aquests documents. Com a resultat, s'obté un mapa amb un rànquing de termes, un llistat de paraules clau classificades automàticament i un llistat d'expressions formades per múltiples paraules. El sistema complet s'ha provat en quatre sectors diferents: panells solars, intel·ligència artificial, sensors remots i imatges mèdiques. Aquest treball ha obtingut resultats prometedors, avaluats amb dues metodologies diferents. Com a resultat, el sistema ha estat capaç de detectar de manera satisfactòria noves tendències en etapes molt primerenques que s'han tornat cada cop més importants actualment. La computació quàntica és un paradigma nou per a una multitud d'aplicacions informàtiques. En aquesta tesi doctoral també es presenta un estudi de les tecnologies disponibles actualment per a la implementació física de qubits i portes quàntiques, establint-ne els principals avantatges i desavantatges, i els marcs disponibles per a la programació i implementació de circuits quàntics. Per tal de millorar l'efectivitat del sistema, es descriu un disseny d'un circuit quàntic basat en màquines de vectors de suport (SVM) per resoldre problemes de classificació. Aquest circuit està dissenyat especialment per als sorollosos processadors quàntics d'escala intermèdia (NISQ) que estan disponibles actualment. Com a experiment, el circuit ha estat provat en un ordinador quàntic real basat en qubits superconductors per IBM com una millora per al subsistema de mineria de text. Els resultats obtinguts amb l'experiment quàntic també mostren conclusions interessants i una millora en el rendiment de prop del 20% sobre els sistemes convencionals, però a la vegada confirmen que encara es requereix un desenvolupament tecnològic continu per aprofitar al màxim la computació quàntica.[EN] In this doctoral thesis, a system to detect weak signals related to future transcendental changes is proposed and tested. While most known solutions are based on the use of structured data, the proposed system quantitatively detects these signals using heterogeneous and unstructured information from scientific, journalistic, and social sources. Predicting new trends in an environment has many applications. For instance, companies and startups face constant changes in their markets that are very difficult to predict. For this reason, developing systems to automatically detect significant future changes at an early stage is relevant for any organization to make right decisions on time. This work has been designed to obtain weak signals of the future in any field depending only on the input dataset of documents. Text mining and natural language processing techniques are applied to process all these documents. As a result, a map of ranked terms, a list of automatically classified keywords and a list of multi-word expressions are obtained. The overall system has been tested in four different sectors: solar panels, artificial intelligence, remote sensing, and medical imaging. This work has obtained promising results that have been evaluated with two different methodologies. As a result, the system was able to successfully detect new trends at a very early stage that have become more and more important today. Quantum computing is a new paradigm for a multitude of computing applications. This doctoral thesis also presents a study of the technologies that are currently available for the physical implementation of qubits and quantum gates, establishing their main advantages and disadvantages and the available frameworks for programming and implementing quantum circuits. In order to improve the effectiveness of the system, a design of a quantum circuit based on support vector machines (SVMs) is described for the resolution of classification problems. This circuit is specially designed for the noisy intermediate-scale quantum (NISQ) computers that are currently available. As an experiment, the circuit has been tested on a real quantum computer based on superconducting qubits by IBM as an improvement for the text mining subsystem in the detection of weak signals. The results obtained with the quantum experiment show interesting outcomes with an improvement of close to 20% better performance than conventional systems, but also confirm that ongoing technological development is still required to take full advantage of quantum computing.Griol Barres, I. (2022). Modelling of a System for the Detection of Weak Signals Through Text Mining and NLP. Proposal of Improvement by a Quantum Variational Circuit [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/183029TESISCompendi

Implementación de un sistema de detección de señales débiles de futuro mediante técnicas de minería de textos

Nowadays, one of the biggest threats for companies is not being able to cope with the constant changes occurring in the market by not predicting them well in advance. For this reason, the development of new processes that facilitate the detection of future phenomena and significant changes is a key component for correct decision making that can mark a correct course in the company. A business intelligence based architecture system is proposed to allow discrete changes or weak signals detection in the present that are indicative of more significant phenomena and transcendental changes in the future. In contrast with current available works, which are focused on structured information sources or, at most, with only a single type of data source, in this paper the detection of these signals is done quantitatively from various kinds of heterogeneous and unstructured documents (scientific articles, journalistic articles and social networks) on which text mining techniques are applied. The system has been tested in the study of the future of solar panels sector, obtaining promising results that can help business experts in the recognition of new driving factors of their markets and the development of new opportunities.Actualmente, una de las mayores amenazas para las empresas es no ser capaces de hacer frente a los cambios constantes que se dan en el mercado, por no predecirlos con la suficiente antelación. Por ello, el desarrollo de nuevos procesos que faciliten la detección de fenómenos y cambios futuros significativos es una componente clave para una correcta toma de decisiones que marque un rumbo correcto para la empresa. Por esta razón, se propone un sistema basado en una arquitectura de inteligencia de negocio que permite detectar cambios discretos o señales débiles (weak signals) en el presente, pero que son indicativos de fenómenos más significativos y cambios trascendentales en el futuro. Frente a los trabajos actuales que se centran en fuentes de información estructuradas, o como mucho, con un único tipo de fuente de datos, en este trabajo la detección de estas señales se realiza de forma cuantitativa a partir de documentos heterogéneos y no estructurados de diversa índole (artículos científicos, periodísticos y redes sociales) sobre los que se aplican técnicas de minería de textos. El sistema ha sido testeado para estudiar el futuro del sector de los paneles solares, habiéndose obtenido resultados prometedores para ayudar a expertos en el reconocimiento de nuevos factores de peso en sus mercados y en el desarrollo de nuevas oportunidades

STARTUPV: Different approaches in mentoring and tutorship for entrepreneurs in the three stages of a university entrepreneurial ecosystem

Year after year, a crowd of students from the Universitat Politècnica de València (UPV), a polytechnic university in Valencia (Spain) with over 30,000 students, are encouraged to start their own business projects. Since 1992, IDEAS UPV, the Entrepreneurial service at UPV, has been mentoring entrepreneurs. Up till now, IDEAS UPV has helped in the generation of close to 1000 new businesses with a survival rate of over 60% in five years. In 2012, IDEAS UPV introduced new mentoring and tutorship activities for students by the creation of a business incubator within the university campus. StartUPV is currently a 5-year startup incubation programme and an entrepreneurial ecosystem with more than 300 startups and more than 50 million euros of overall private investment StartUPV programme is divided into three different stages: (i) STAND UP, in which startups define a business model and complete a validation process; (ii) START UP, in which startups achieve a targeted market share and build their company management team; and (iii) SCALE UP, in which startups achieve maturity and scale to other international markets. As university students and their startups face different needs in every step of the programme, different approaches for mentoring and tutorship are applied in every stage. For instance, a startup in the first stage is mentored in business modelling or market segmentation, while a scale up requires a more specific mentorship in dealing with corporates and venture capital. These different approaches are analysed in this work including the main findings of the 10 years of this programme

Variational quantum circuits for machine learning. An application for the detection of weak signals

[EN] Featured Application Quantum classifier to detect weak signals. Quantum computing is a new paradigm for a multitude of computing applications. This study presents the technologies that are currently available for the physical implementation of qubits and quantum gates, establishing their main advantages and disadvantages and the available frameworks for programming and implementing quantum circuits. One of the main applications for quantum computing is the development of new algorithms for machine learning. In this study, an implementation of a quantum circuit based on support vector machines (SVMs) is described for the resolution of classification problems. This circuit is specially designed for the noisy intermediate-scale quantum (NISQ) computers that are currently available. As an experiment, the circuit is tested on a real quantum computer based on superconducting qubits for an application to detect weak signals of the future. Weak signals are indicators of incipient changes that will have a future impact. Even for experts, the detection of these events is complicated since it is too early to predict this impact. The data obtained with the experiment shows promising results but also confirms that ongoing technological development is still required to take full advantage of quantum computing.Griol-Barres, I.; Milla, S.; Cebrián Ferriols, AJ.; Mansoori, Y.; Millet Roig, J. (2021). Variational quantum circuits for machine learning. An application for the detection of weak signals. Applied Sciences. 11(14):1-22. https://doi.org/10.3390/app11146427S122111

Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing

[EN] Organizations, companies and start-ups need to cope with constant changes on the market which are difficult to predict. Therefore, the development of new systems to detect significant future changes is vital to make correct decisions in an organization and to discover new opportunities. A system based on business intelligence techniques is proposed to detect weak signals, that are related to future transcendental changes. While most known solutions are based on the use of structured data, the proposed system quantitatively detects these signals using heterogeneous and unstructured information from scientific, journalistic and social sources, applying text mining to analyze the documents and natural language processing to extract accurate results. The main contributions are that the system has been designed for any field, using different input datasets of documents, and with an automatic classification of categories for the detected keywords. In this research paper, results from the future of remote sensors are presented. Remote sensing services are providing new applications in observation and analysis of information remotely. This market is projected to witness a significant growth due to the increasing demand for services in commercial and defense industries. The system has obtained promising results, evaluated with two different methodologies, to help experts in the decision-making process and to discover new trends and opportunities.This research is partially supported by EIT Climate-KIC of the European Institute of Technology (project EIT Climate-KIC Accelerator-TC_3.1.5_190607_P066-1A) and InnoCENS from Erasmus + (573965-EPP-1-2016-1-SE-EPPKA2-CBHE-JP).Griol-Barres, I.; Milla, S.; Cebrián Ferriols, AJ.; Fan, H.; Millet Roig, J. (2020). Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing. 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Variational Quantum Circuits for Machine Learning. An Application for the Detection of Weak Signals

Quantum computing is a new paradigm for a multitude of computing applications. This study presents the technologies that are currently available for the physical implementation of qubits and quantum gates, establishing their main advantages and disadvantages and the available frameworks for programming and implementing quantum circuits. One of the main applications for quantum computing is the development of new algorithms for machine learning. In this study, an implementation of a quantum circuit based on support vector machines (SVMs) is described for the resolution of classification problems. This circuit is specially designed for the noisy intermediate-scale quantum (NISQ) computers that are currently available. As an experiment, the circuit is tested on a real quantum computer based on superconducting qubits for an application to detect weak signals of the future. Weak signals are indicators of incipient changes that will have a future impact. Even for experts, the detection of these events is complicated since it is too early to predict this impact. The data obtained with the experiment shows promising results but also confirms that ongoing technological development is still required to take full advantage of quantum computing

TÉCNICAS DE REDUCCIÓN EN FRONT-ENDS INTEGRADOS PARA DETECTORES DE RADIACIÓN GAMMA

El objetivo de este trabajo es la evaluación de las distintas contribuciones de ruido electrónico en un front-end integrado para detectores de radiación gamma de tipo continuo. En primer lugar, se analizarán las contribuciones de ruido provenientes de los bloques analógicos destinados a la polarización, incluyendo los "bandgaps" empleados como referencias de corriente. Una vez realizado un análisis de una referencia de corriente real, se propondrán soluciones para reducir las distintas fuentes problemáticas de ruido y se realizará una propuesta de referencia de corriente de bajo ruido que mejore las prestaciones actuales y que, además, mejore sustancialmente su estabilidad con la temperatura. Para finalizar, se incluirá un estudio sobre el modelo de sustrato y el efecto de ruido proveniente de los bloques digitales sobre el comportamiento de los bloques analógicos y se incluirán alternativas y técnicas de aislamiento en sistemas mixtos.Griol Barres, I. (2010). TÉCNICAS DE REDUCCIÓN EN FRONT-ENDS INTEGRADOS PARA DETECTORES DE RADIACIÓN GAMMA. http://hdl.handle.net/10251/12810Archivo delegad

StartUPV 10 años de ecosistema emprendedor universitario / StartUPV 10 anys d'ecosistema emprenedor universitari

StartUPV cumple 10 años. Una década de éxitos, emprendimiento y fomento de grandes ideas. Desde el vicerrectorado de Estudiantes y Emprendimiento queremos no solo seguir apostando por este ecosistema emprendedor,sino dotarlo de más recursos y sinergias para que siga siendo un referente a nivel internacional entre los programas universitarios de formación, incubación y aceleración de startups.Estas páginas incluyen una amplia variedad de historias que muestran como StartUPV ha impactado en la vida de los estudiantes y egresados de nuestra UniversitatVicerrectorado de estudiantes y emprendimiento; Griol Barres, I. (2023). StartUPV 10 años de ecosistema emprendedor universitario / StartUPV 10 anys d'ecosistema emprenedor universitari. Editorial Universitat Politècnica de València. https://doi.org/10.4995/2023.64020

Improving strategic decision making by the detection of weak signals in heterogeneous documents by text mining techniques

[EN] At present, one of the greatest threats to companies is not being able to cope with the constant changes that occur in the market because they do not predict them well in advance. Therefore, the development of new processes that facilitate the detection of significant phenomena and future changes is a key component for correct decision making that sets a correct course in the company. For this reason, a business intelligence architecture system is hereby proposed to allow the detection of discrete changes or weak signals in the present, indicative of more significant phenomena and transcendental changes in the future. In contrast to work currently available focusing on structured information sources, or at most with a single type of data source, the detection of these signals is here quantitatively based on heterogeneous and unstructured documents of various kinds (scientific journals, newspaper articles and social networks), to which text mining and natural language processing techniques (a multi-word expression analysis) are applied. The system has been tested to study the future of the artificial intelligence sector, obtaining promising results to help business experts in the recognition of new driving factors of their markets and the development of new opportunities.This work is partially supported by EIT Climate KIC of the European Union (project Accelerator TC2018B-2.2.5-ACCUPV-P066-1A) and Erasmus+ InnoCENS (573965-EPP-1-2016-1-SE-EPPKA2-CBHE-JP).Griol Barres, I.; Milla, S.; Millet Roig, J. (2019). Improving strategic decision making by the detection of weak signals in heterogeneous documents by text mining techniques. AI Communications. 32(5-6):347-360. https://doi.org/10.3233/AIC-190625S347360325-6Ansoff, H. I. (1975). Managing Strategic Surprise by Response to Weak Signals. California Management Review, 18(2), 21-33. doi:10.2307/41164635S. Bird, E. Loper and E. Klein, Natural Language Processing with Python, O’Reilly Media Inc., 2009.A. Cooper, C. Voigt, E. Unterfrauner, M. Kravcik, J. Pawlowski and H. Pirkkalainen, Report on weak signals collection, in: TELMAP, European Commission Seventh Framework Project (IST-257822), Deliverable D4.1, 2011, pp. 6–7.J. Dator, Futures studies as applied knowledge, in: New Thinking for a New Millennium, R. Slaughter, ed., Routledge, London, 1996, www.futures.hawaii.edu/dator/futures/appliedknow.html.Dator, J. (2005). Universities without «quality» and quality without «universities». On the Horizon, 13(4), 199-215. doi:10.1108/10748120510627321Duan, J., Zhang, M., Jingzhong, W., & Xu, Y. (2011). A hybrid framework to extract bilingual multiword expression from free text. Expert Systems with Applications, 38(1), 314-320. doi:10.1016/j.eswa.2010.06.067Fink, L., Yogev, N., & Even, A. (2017). Business intelligence and organizational learning: An empirical investigation of value creation processes. Information & Management, 54(1), 38-56. doi:10.1016/j.im.2016.03.009Guralnik, V., & Srivastava, J. (1999). Event detection from time series data. Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’99. doi:10.1145/312129.312190Haegeman, K., Marinelli, E., Scapolo, F., Ricci, A., & Sokolov, A. (2013). Quantitative and qualitative approaches in Future-oriented Technology Analysis (FTA): From combination to integration? Technological Forecasting and Social Change, 80(3), 386-397. doi:10.1016/j.techfore.2012.10.002Hiltunen, E. (2008). The future sign and its three dimensions. Futures, 40(3), 247-260. doi:10.1016/j.futures.2007.08.021Ilmola, L., & Kuusi, O. (2006). Filters of weak signals hinder foresight: Monitoring weak signals efficiently in corporate decision-making. Futures, 38(8), 908-924. doi:10.1016/j.futures.2005.12.019Ishikiriyama, C. S., Miro, D., & Gomes, C. F. S. (2015). Text Mining Business Intelligence: A small sample of what words can say. Procedia Computer Science, 55, 261-267. doi:10.1016/j.procs.2015.07.044K. Jung, A Study of Foresight Method Based on Text Mining and Complexity Network Analysis, KISTEP, Seoul, 2010.Kawahara, Y., & Sugiyama, M. (2009). Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation. Proceedings of the 2009 SIAM International Conference on Data Mining. doi:10.1137/1.9781611972795.34Kim, J., Han, M., Lee, Y., & Park, Y. (2016). Futuristic data-driven scenario building: Incorporating text mining and fuzzy association rule mining into fuzzy cognitive map. Expert Systems with Applications, 57, 311-323. doi:10.1016/j.eswa.2016.03.043Koivisto, R., Kulmala, I., & Gotcheva, N. (2016). Weak signals and damage scenarios — Systematics to identify weak signals and their sources related to mass transport attacks. Technological Forecasting and Social Change, 104, 180-190. doi:10.1016/j.techfore.2015.12.010Liu, S., Yamada, M., Collier, N., & Sugiyama, M. (2013). Change-point detection in time-series data by relative density-ratio estimation. Neural Networks, 43, 72-83. doi:10.1016/j.neunet.2013.01.012MohamadiBaghmolaei, R., Mozafari, N., & Hamzeh, A. (2017). Continuous states latency aware influence maximization in social networks. AI Communications, 30(2), 99-116. doi:10.3233/aic-170720Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523. doi:10.1016/0306-4573(88)90021-0Schoemaker, P. J. H., Day, G. S., & Snyder, S. A. (2013). Integrating organizational networks, weak signals, strategic radars and scenario planning. Technological Forecasting and Social Change, 80(4), 815-824. doi:10.1016/j.techfore.2012.10.020Thorleuchter, D., Scheja, T., & Van den Poel, D. (2014). Semantic weak signal tracing. Expert Systems with Applications, 41(11), 5009-5016. doi:10.1016/j.eswa.2014.02.046Thorleuchter, D., & Van den Poel, D. (2015). Idea mining for web-based weak signal detection. Futures, 66, 25-34. doi:10.1016/j.futures.2014.12.007Tseng, Y.-H., Lin, C.-J., & Lin, Y.-I. (2007). Text mining techniques for patent analysis. Information Processing & Management, 43(5), 1216-1247. doi:10.1016/j.ipm.2006.11.011Willett, P. (2006). The Porter stemming algorithm: then and now. Program, 40(3), 219-223. doi:10.1108/00330330610681295Yangui, R., Nabli, A., & Gargouri, F. (2016). Automatic Transformation of Data Warehouse Schema to NoSQL Data Base: Comparative Study. Procedia Computer Science, 96, 255-264. doi:10.1016/j.procs.2016.08.138Yoon, J. (2012). Detecting weak signals for long-term business opportunities using text mining of Web news. Expert Systems with Applications, 39(16), 12543-12550. doi:10.1016/j.eswa.2012.04.059Zhang, W., Yoshida, T., & Tang, X. (2008). Text classification based on multi-word with support vector machine. Knowledge-Based Systems, 21(8), 879-886. doi:10.1016/j.knosys.2008.03.04

StartUPV: 10 años de ecosistema emprendedor universitario

StartUPV cumple 10 años. Una década de éxitos, emprendimiento y fomento de grandes ideas. Desde el vicerrectorado de Estudiantes y Emprendimiento queremos no solo seguir apostando por este ecosistema emprendedor,sino dotarlo de más recursos y sinergias para que siga siendo un referente a nivel internacional entre los programas universitarios de formación, incubación y aceleración de startups. Estas páginas incluyen una amplia variedad de historias que muestran como StartUPV ha impactado en la vida de los estudiantes y egresados de nuestra UniversitatVicerrectorado de estudiantes y emprendimiento; Griol Barres, I. (2022). StartUPV: 10 años de ecosistema emprendedor universitario. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/183917EDITORIA