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Ayer 27 de mayo se celebró en la ETSI Telecomunicación de la UPM un acto de homenaje "in memoriam" al profesor Fernando Sáez Vacas. Al inicio del acto se inauguró una placa para dar nombre a la ciberteca de la Escuela, ciberteca Fernando Sáez Vacas.

El acto fue presidido por el Rector Guillermo Cisneros y organizado por la 47ª Promoción, que ha estado ligada a Fernando, dado que esta promoción inició la andadura de Satelec y Fernando les acompañó en el viaje fin de carrera, manteniendo reuniones desde entonces.

En el acto se repasó la trayectoria académica de Fernando. Dos doctorandos y colaboradores de Fernando, los profesores Gregorio Fernández y Gonzalo León comentaron diversos aspectos de su personalidad, como su capacidad y empeño en redactar textos docentes empleando un lenguaje preciso y claro, su visión, y su capacidad de transferencia tecnológica con la empresa, proponiendo tesis empresariales. También participaron dos alumnos,  Mariano  González, director general de Bull, y  Mateo Valero, director del Barcelona Supercomputing Center y reciente premio Charles Babbage 2017, que comentaron su labor como profesor y mentor. Finalmente, Ángel Mayoral, colaborador de Fernando en Bull, y Paco Román, expresidente de Vodafone España y presidente de fundación SERES, comentaron su labor profesional, ligada a la formación empresarial en Bull, y su capacidad de reflexionar sobre las implicaciones sociales de la tecnología. Durante las intervenciones, también se destacó cómo Fernando impulsó diversas asociaciones de estudiantes, como el IEEE, IAESTE o Satelec.

El artículo  A semantic data lake framework for autonomous fault management in SDN environments, por Fernando Benayas, Álvaro Carrera, Carlos A. Iglesias y Manuel García Amado has sido aceptado y publicado en la revista Transactions on Emerging Telecommunications Technologies. Esta revista se encuentra indexada en JCR: (Q3, 1.61).

Fault Management is a vital issue for any network operator since the beginning of the telecommunications era. As networks have become more and more complex, their management systems are crucial for any operator company. In this ecosystem, the Software‐Defined Networking (SDN) approach has appeared as a possible solution for different networking issues. The flexibility provided by SDN to the network management enables a great dynamism in the configuration of network devices. However, this feature introduces the cost of a potential increase in failures because every modification introduced on the control plane is a new possibility for failures to appear and cause a decrement of the quality for offered services. Because of the growing pace of the networks, the classical approach is not feasible to cope that dynamism. Increasing the number of human operators in charge of the fault management process would increase the operation cost dramatically. Thus, this paper presents an approach to apply machine learning over a big data framework for an autonomous fault management process in SDN networks. In this paper, we present a Semantic Data Lake framework for a self‐diagnosis service, which is deployed on top of an SDN management platform. Moreover, we have developed a prototype of the proposed service with different diagnosis models for SDN networks. Models and algorithms have been evaluated showing good results.

Referencia:

Benayas F, Carrera Á, García-Amado M, Iglesias CA. A semantic data lake framework for autonomous fault management in SDN environments. Trans Emerging Tel Tech. 2019; e3629. https: //doi.org/10.1002/ett.3629

Green Open Access:

http://gsi.upm.es/administrator/components/com_jresearch/files/publications/prerprint%20with%20acknow-SDS2018_SI.pdf

El pasado 29 de abril se celebró la entrega de la primera edición de las becas GSI, presidida por el Rector Guillermo Cisneros. Durante el acto, se inauguró la placa del Laboratorio de Sistemas Inteligentes Profesores Gregorio Fernández, Mercedes Garijo y Fernando Sáez Vacas.

Más información del acto en fotografías: https://www.flickr.com/photos/etsit-upm/sets/72157691193114953/with/40774042453/

 El grupo de Sistemas Inteligentes organiza un acto de entrega de la Primera Edición de las Becas  de Iniciación a la investigación en Sistemas Inteligentes:

El artículo A framework for fake review detection in online consumer electronics retailers, por Rodrigo Barbado, Oscar Araque y Carlos A. Iglesias has sido aceptado y publicado en la revista Information Processing & Management. Esta revista se encuentra indexada en JCR: (Q1, 3.444).

Abstract:The impact of online reviews on businesses has grown significantly during last years, being crucial to determine business success in a wide array of sectors, ranging from restaurants, hotels to e-commerce. Unfortunately, some users use unethical means to improve their online reputation by writing fake reviews of their businesses or competitors. Previous research has addressed fake review detection in a number of domains, such as product or business reviews in restaurants and hotels. However, in spite of its economical interest, the domain of consumer electronics businesses has not yet been thoroughly studied. This article proposes a feature framework for detecting fake reviews that has been evaluated in the consumer electronics domain. The contributions are fourfold: (i) Construction of a dataset for classifying fake reviews in the consumer electronics domain in four different cities based on scraping techniques; (ii) definition of a feature framework for fake review detection; (iii) development of a fake review classification method based on the proposed framework and (iv) evaluation and analysis of the results for each of the cities under study. We have reached an 82% F-Score on the classification task and the Ada Boost classifier has been proven to be the best one by statistical means according to the Friedman test.

Referencia:

Oscar Araque, Ganggao Zhu, Carlos A. Iglesias, A semantic similarity-based perspective of affect lexicons for sentiment analysis, Knowledge-Based Systems, Volume 165, 2019, Pages 346-359, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2018.12.005. (http://www.sciencedirect.com/science/article/pii/S0950705118305926).