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A Cloud Game-based Educative Platform Architecture: the CyberScratch Project
(MDPI, 2021) Utrilla, Alejandro; Tobarra Abad, María de los Llanos; Robles Gómez, Antonio; Pastor Vargas, Rafael; Hernández Berlinches, Roberto
The employment of modern technologies is widespread in our society, so the inclusion of practical activities for education has become essential and useful at the same time. These activities are more noticeable in Engineering, in areas such as cybersecurity, data science, artificial intelligence, etc. Additionally, these activities acquire even more relevance with a distance education methodology, as our case is. The inclusion of these practical activities has clear advantages , such as (1) promoting critical thinking and (2) improving students’ abilities and skills for their professional careers. There are several options, such as the use of remote and virtual laboratories, virtual reality and gamebased platforms, among others. This work addresses the development of a new cloud game-based educational platform, which defines a modular and flexible architecture (using light containers). This architecture provides interactive and monitoring services and data storage in a transparent way. The platform uses gamification to integrate the game as part of the instructional process. The CyberScratch project is a particular implementation of this architecture focused on cybersecurity game-based activities. The data privacy management is a critical issue for these kinds of platforms, so the architecture is designed with this feature integrated in the platform components. To achieve this goal, we first focus on all the privacy aspects for the data generated by our cloud game-based platform, by considering the European legal context for data privacy following GDPR and ISO/IEC TR 20748-1:2016 recommendations for Learning Analytics (LA). Our second objective is to provide implementation guidelines for efficient data privacy management for our cloud game-based educative platform. All these contributions are not found in current related works. The CyberScratch project, which was approved by UNED for the year 2020, considers using the xAPI standard for data handling and services for the game editor, game engine and game monitor modules of CyberScratch. Therefore, apart from considering GDPR privacy and LA recommendations, our cloud game-based architecture covers all phases from game creation to the final users’ interactions with the game.
A comparison of extrinsic clustering evaluation metrics based on formal constraints
(Springer, 2009-05-11) Artiles, Javier; Verdejo, Felisa; Amigo Cabrera, Enrique; Gonzalo Arroyo, Julio Antonio
There is a wide set of evaluation metrics available to compare the quality of text clustering algorithms. In this article, we define a few intuitive formal constraints on such metrics which shed light on which aspects of the quality of a clustering are captured by different metric families. These formal constraints are validated in an experiment involving human assessments, and compared with other constraints proposed in the literature. Our analysis of a wide range of metrics shows that only BCubed satisfies all formal constraints. We also extend the analysis to the problem of overlapping clustering, where items can simultaneously belong to more than one cluster. As Bcubed cannot be directly applied to this task, we propose a modified version of Bcubed that avoids the problems found with other metrics.
A Controller design by QFT methodology for dynamic positioning of a moored platform
(2006-01-01) Muñoz Mansilla, María del Rocío; Aranda Almansa, Joaquín; Díaz Martínez, José Manuel; Dormido Canto, Sebastián; Chaos García, Dictino
A data driven approach for person name disambiguation in web search results
(2014-08-23) Víctor Fresno, Víctor; Montalvo, Soto; Delgado Muñoz, Agustín Daniel; Martínez Unanue, Raquel
This paper presents an unsupervised approach for the task of clustering the results of a search engine when the query is a person name shared by different individuals. We propose an algorithm that calculates the number of clusters and establishes the groups of web pages according to the different individuals without the need to any training data or predefined thresholds, as the successful state of the art systems do. In addition, most of those systems do not deal with social media web pages and their performance could fail in a real scenario. In this paper we also propose a heuristic method for the treatment of social networking profiles. Our approach is compared with four gold standard collections for this task obtaining really competitive results, comparable to those obtained by some approaches with supervision.
A historical perspective of algorithmic lateral inhibition and accumulative computation in computer vision
(Elsevier, 2011-03) Fernández Caballero, Antonio; Carmona, Enrique J.; Delgado, Ana Esperanza; López López, María Dolores
Certainly, one of the prominent ideas of Professor José Mira was that it is absolutely mandatory to specify the mechanisms and/or processes underlying each task and inference mentioned in an architecture in order to make operational that architecture. The conjecture of the last fifteen years of joint research has been that any bottom-up organization may be made operational using two biologically inspired methods called “algorithmic lateral inhibition”, a generalization of lateral inhibition anatomical circuits, and “accumulative computation”, a working memory related to the temporal evolution of the membrane potential. This paper is dedicated to the computational formulation of both methods. Finally, all of the works of our group related to this methodological approximation are mentioned and summarized, showing that all of them support the validity of this approximation.
A Knowledge Graph Framework for Dementia Research Data
(MDPI, 2023-09-20) Timón Reina, Santiago; Kirsebom, Bjørn-Eivind; Fladby, Tormod; Rincón Zamorano, Mariano; Martínez Tomás, Rafael
Dementia disease research encompasses diverse data modalities, including advanced imaging, deep phenotyping, and multi-omics analysis. However, integrating these disparate data sources has historically posed a significant challenge, obstructing the unification and comprehensive analysis of collected information. In recent years, knowledge graphs have emerged as a powerful tool to address such integration issues by enabling the consolidation of heterogeneous data sources into a structured, interconnected network of knowledge. In this context, we introduce DemKG, an open-source framework designed to facilitate the construction of a knowledge graph integrating dementia research data, comprising three core components: a KG-builder that integrates diverse domain ontologies and data annotations, an extensions ontology providing necessary terms tailored for dementia research, and a versatile transformation module for incorporating study data. In contrast with other current solutions, our framework provides a stable foundation by leveraging established ontologies and community standards and simplifies study data integration while delivering solid ontology design patterns, broadening its usability. Furthermore, the modular approach of its components enhances flexibility and scalability. We showcase how DemKG might aid and improve multi-modal data investigations through a series of proof-of-concept scenarios focused on relevant Alzheimer’s disease biomarkers.
A linear equation based on signal increments to predict disruptive behaviours and the time to disruption on JET
(IOP Publishing, 2019-12-13) Vega, J.; Murari, A.; Hernández, F.; Cruz, T.; Gadariya, Dhaval; Rattá, Giuseppe A.; Contributors, JET.; Dormido Canto, Sebastián
This article describes the development of a generic disruption predictor that is also used as basic system to provide an estimation of the time to disruption at the alarm times. The mode lock signal normalised to the plasma current is used as input feature. The recognition of disruptive/non-disruptive behaviours is not based on a simple threshold of this quantity but on the evolution of the amplitudes between consecutive samples taken periodically. The separation frontier between plasma behaviours (disruptive/non-disruptive) is linear in such parameter space. The percentages of recognised and false alarms are 98% and 4%, respectively. The recognised alarms can be split into valid alarms (90%) and late detections (8%). The experimental distribution of warning times follows an exponential model with average warning time of 443 ms. On the other hand, the prediction of the time to the disruption has been fitted to a Weibull model that relates this predicted time to the distance of the points to the diagonal in the parameter space of consecutive samples. The model shows a very good agreement between predicted times and warning times in narrow time intervals (between 0.01 s and 0.06 s) before the disruption.
A literature review on feature diagram product counting and its usage in software product line economic models
(World Scientific Publishing, 2013-10-01) Heradio Gil, Rubén; Fernández Amoros, David José; Cerrada Somolinos, José Antonio; Abad Cardiel, Ismael
In software product line engineering, feature diagrams are a popular means to represent the similarities and differences within a family of related systems. In addition, feature diagrams implicitly model valuable information that can be used in economic models to estimate the cost savings of a product line. In particular, this paper reviews existing proposals on computing the total number of products modeled with a feature diagram and, given a feature, the number of products that implement it. The paper also reviews the economic information that can be estimated when such numbers are known. Thus, this paper contributes by bringing together previously-disparate streams of work: the automated analysis of feature diagrams and economic models for product lines.
A methology to model and simuate binary distillation columns with inventory control
(2004-01-01) Duro Carralero, Natividad; Morilla García, Fernando
A new control laboratory using parallel programming
(2007-11-28) Dormido Bencomo, Sebastián; Dormido Canto, Sebastián; Sánchez Moreno, José
A new method of self-tuning digital PID controllers
(Albertos P. and de la Fuente J. A., 1986-01-01) Dormido Bencomo, Sebastián; Guillén, J. M.; Cruz García, Jesús Manuel de la; Aranda Almansa, Joaquín
A novel deep learning approach using blurring image techniques for Bluetooth-based indoor localisation
(Elsevier, 2022-10-17) Talla Chumpitaz, Reewos; Orozco Barbosa, Luis; García Castro, Raúl; Castillo Cara, José Manuel
The growing interest in the use of IoT technologies has generated the development of numerous and diverse applications. Many of the services provided by the applications are based on knowledge of the localisation and profile of the end user. Thus, the present work aims to develop a system for indoor localisation prediction using Bluetooth-based fingerprinting using Convolutional Neural Networks (CNN). For this purpose, a novel technique was developed that simulates the diffusion behaviour of the wireless signal by transforming tidy data into images. For this transformation, we implemented the technique used in painting known as blurring, simulating the diffusion of the signal spectrum. Our proposal also includes the use and a comparative analysis of two dimensional reduction algorithms, PCA and t -SNE. Finally, an evolutionary algorithm was implemented to configure and optimise our solution with the combination of different transmission power levels. The results reported in this work present an accuracy of close to 94%, which clearly shows the great potential of this novel technique in the development of more accurate indoor localisation systems .
A novel feature engineering approach for high-frequency financial data
(Elsevier, 2023-10) Mantilla, Pablo; Dormido Canto, Sebastián
Feature engineering for high-frequency financial data based on constructing dynamic data subsets, defined by time intervals in which high-frequency trends occur, is proposed. These intervals are obtained through time series segmentation. This methodology allows us to extract and analyze variables by intraday trends as well as to feed artificial intelligence models to forecast response variables in future trends. Furthermore, to show how to use this feature engineering, this methodology is applied to estimate high-frequency volatility, duration and direction linked to future intraday trends, developing multiclass classification models based on the machine learning method extreme gradient boosting. Experimentation was conducted using high-frequency financial data from the Brazil Stock Exchange, corresponding to 206 trading days related to 20 listed assets from this financial market.
A real application example of a control structure selection by means of a multiobjective genetic algorithm
(Springer-Verlag, 2003-01-01) Parrilla Sánchez, Manuel; Aranda Almansa, Joaquín::virtual::4100::600; Aranda Almansa, Joaquín; Aranda Almansa, Joaquín; Aranda Almansa, Joaquín
A refinement of the well-founded Information Content models with a very detailed experimental survey on WordNet.
(Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Lenguajes y Sistemas Informáticos) Lastra-Díaz, Juan J.; García Serrano, Ana Mª
In a recent paper, we introduce a new family of Information Content (IC) models based on the estimation of the conditional probability between child and parent concepts. This work is encouraged by the nding of two drawbacks in the computational method of our aforementioned family of IC models, as well as other two gaps in the literature. First gap is that two of our cognitive IC models do not satisfy the axiom that constrains the sum of probabilities on the leaf nodes to be 1, whilst some ontologies with multiple inheritance could prevent the IC model satisfying the growing monotonicity axiom in concepts with multiple parents. Second gap is the lack of a complete and updated experimental survey including a pairwise statistical signi cance analysis between most IC models and ontology-based similarity measures. Finally a third gap is the lack of replication and con rmation of previous methods and results in most works. The latest two gaps are especially signi cant in the current state of the problem, in which there is no convincing winner within the family of intrinsic IC-based similarity measures and the performance margin is very narrow. In order to bridge the aforementioned gaps, this paper introduces the following contributions: (1) a re nement of our recent family of well-founded Information Content (IC) models; (2) eight new intrinsic IC models and one new corpus-based IC model; and (3) a very detailed experimental survey of ontology-based similarity measures and Information Content (IC) models on WordNet, including the evaluation and statistical signi cance analysis on the ve most signi cant datasets of most ontology-based similarity measures and all WordNet-based IC models reported in the literature, with the only exception of the IC models recently introduced by Harispe et al. (2015a) and Ben Aouicha et al. (2016b). The evaluation is entirely based on a Java software library called HESML which has been developed by the authors in order to replicate all methods evaluated herein. The new IC models obtain rivaling results as regard the state-of-the-art methods and improve our previous mod- els, whilst the experimental survey allows a detailed and conclusive image of the state of the problem to be drawn by setting the new state of the art and quantifying the main achievements of the last three decades.
A Robust Constrained Reference Governor Approach using Linear Matrix Inequalities
(Journal of Process Control, 2007-01-01) Guzmán Sánchez, José Luis; Álamo, Teodoro; Berenguel, Manuel; Dormido Bencomo, Sebastián; Camacho, Eduardo F.
A robust sampled PI regulator for stable systems with monotone step responses
(Hamza M. H:, 1989-02-07) Cruz García, Jesús Manuel de la; Morilla García, Fernando; Aranda Almansa, Joaquín
A SCADA oriented middleware for RFID technology
(Elsevier, 2012-09-01) Abad Cardiel, Ismael; Heradio Gil, Rubén; Cerrada Somolinos, Carlos; Cerrada Somolinos, José Antonio
Radio Frequency IDentification (RFID) has emerged as the new technology paradigm for acquisition and information management. RFID can be used to improve significantly the efficiency of business processes by providing the capability of automatic identification and data capture. This technology introduces new challenges on data and process information management in current systems. RFID data are timedependent and dynamically changing. In addition, data carry implicit semantics. The homogeneous data processing of such implicit semantics allows us to propose RFID middleware as a WHO–WHEN–WHERE data problem. This paper presents DEPCAS, a new middleware for RFID information based on the SCADA architecture for control systems. An application of DEPCAS is the resolution of heterogeneous situations, which solves the WHAT or context–aware to apply the auto identification data received from RFID systems in business applications.
A scalable approach to exact model and commonality counting for extended feature models.
(Institute of Electrical and Electronics Engineers (IEEE), 2014-05-29) Fernández Amoros, David José; Heradio Gil, Rubén; Cerrada Somolinos, José Antonio; Cerrada Somolinos, Carlos
A software product line is an engineering approach to efficient development of software product portfolios. Key to the success of the approach is to identify the common and variable features of the products and the interdependencies between them, which are usually modeled using feature models. Implicitly, such models also include valuable information that can be used by economic models to estimate the payoffs of a product line. Unfortunately, as product lines grow, analyzing large feature models manually becomes impracticable. This paper proposes an algorithm to compute the total number of products that a feature model represents and, for each feature, the number of products that implement it. The inference of both parameters is helpful to describe the standarization/parameterization balance of a product line, detect scope flaws, assess the product line incremental development, and improve the accuracy of economic models. The paper reports experimental evidence that our algorithm has better runtime performance than existing alternative approaches.
A Scorewriter Application using Electrooculography-based Human-Computer Interface
(IEEE, 2022) Pérez–Roa, Enrique M.; Mañoso Hierro, María Carolina; Pérez de Madrid y Pablo, Ángel; Romero Hortelano, Miguel
At present, many projects are being developed with human-computer interfaces in different areas but few are related to music. In this work we present a scorewriter application that uses electrooculography as input interface. For one side, the hardware used to record the electrooculogram consists mainly of a low-cost Arduino based microcontroller board that will receive the signal from the electrodes, collect it and send it via USB to the computer. On the other hand, we use free software to implement the application running on the computer. This application is in charge of processing, classifying (using a neural network) and translating the signal into commands to finally build the song and play it. The modularity of the application allows it to be easily modified for other tasks using the same interface. Due to the nature of the application it is very suitable for entertainment. Furthermore, due to the characteristics of its interface it is also suitable for people with reduced mobility who want to easily perform simple music composition tasks.

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