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Author Viñán-Ludeña M.S., De Campos L.M., Roberto Jacome Galarza, & Sinche Freire, J.
Title Social media influence: a comprehensive review in general and in tourism domain Type Journal Article
Year 2020 Publication Smart Innovation, Systems and Technologies. Abbreviated Journal
Volume 171, 2020 Issue Pages 25-35
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Call Number cidis @ cidis @ Serial 190
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Author Jacome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L.
Title Time series in sensor data using state of the art deep learning approaches: A systematic literature review. Type Conference Article
Year 2022 Publication VII International Conference on Science, Technology and Innovation for Society (CITIS 2021), mayo 26-28.  Smart Innovation, Systems and Technologies. Abbreviated Journal
Volume Vol. 252 Issue Pages 503-514
Keywords time series, deep learning, recurrent networks, sensor data, IoT.
Abstract IoT (Internet of Things) and AI (Artificial Intelligence) are becoming

support tools for several current technological solutions due to significant advancements of these areas. The development of the IoT in various technological fields has contributed to predicting the behavior of various systems such as mechanical, electronic, and control using sensor networks. On the other hand, deep learning architectures have achieved excellent results in complex tasks, where patterns have been extracted in time series. This study has reviewed the most efficient deep learning architectures for forecasting and obtaining trends over time, together with data produced by IoT sensors. In this way, it is proposed to contribute to applications in fields in which IoT is contributing a technological advance such as smart cities, industry 4.0, sustainable agriculture, or robotics. Among the architectures studied in this article related to the process of time series data we have: LSTM (Long Short-Term Memory) for its high precision in prediction and the ability to automatically process input sequences; CNN (Convolutional Neural Networks) mainly in human activity

recognition; hybrid architectures in which there is a convolutional layer for data pre-processing and RNN (Recurrent Neural Networks) for data fusion from different sensors and their subsequent classification; and stacked LSTM Autoencoders that extract the variables from time series in an unsupervised way without the need of manual data pre-processing.Finally, well-known technologies in natural language processing are also used in time series data prediction, such as the attention mechanism and embeddings obtaining promising results.
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Call Number cidis @ cidis @ Serial 152
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Author Nayeth I. Solorzano, L. C. H., Leslie del R. Lima, Dennys F. Paillacho & Jonathan S. Paillacho
Title Visual Metrics for Educational Videogames Linked to Socially Assistive Robots in an Inclusive Education Framework Type Conference Article
Year 2022 Publication Smart Innovation, Systems and Technologies. International Conference in Information Technology & Education (ICITED 21), julio 15-17 Abbreviated Journal
Volume 256 Issue Pages 119-132
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Abstract In gamification, the development of “visual metrics for educational

video games linked to social assistance robots in the framework of inclusive education” seeks to provide support, not only to regular children but also to children with specific psychosocial disabilities, such as those diagnosed with autism spectrum disorder (ASD). However, personalizing each child's experiences represents a limitation, especially for those with atypical behaviors. 'LOLY,' a social assistance robot, works together with mobile applications associated with the family of educational video game series called 'MIDI-AM,' forming a social robotic platform. This platform offers the user curricular digital content to reinforce the teaching-learning processes and motivate regular children and those with ASD. In the present study, technical, programmatic experiments and focus groups were carried out, using open-source facial recognition algorithms to monitor and evaluate the degree of user attention throughout the interaction. The objective is to evaluate the management of a social robot linked to educational video games

through established metrics, which allow monitoring the user's facial expressions

during its use and define a scenario that ensures consistency in the results for its applicability in therapies and reinforcement in the teaching process, mainly

adaptable for inclusive early childhood education.
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Call Number cidis @ cidis @ Serial 180
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