Persona: Pastor Vargas, Rafael
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Pastor Vargas
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Rafael
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Publicación 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, RobertoThe 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.Publicación Cerebral ischemia detection using Deep Learning techniques(Springer, 2025-05-20) Pastor Vargas, Rafael; Antón‑Munárriz, Cristina; Haut, Juan M.; Robles Gómez, Antonio; Paoletti, Mercedes E.; Benítez Andrades, José Alberto; https://orcid.org/0000-0002-4089-9538Cerebrovascular accident (CVA), commonly known as stroke, stands as a significant contributor to contemporary mortality and morbidity rates, often leading to lasting disabilities. Early identification is crucial in mitigating its impact and reducing mortality. Non-contrast computed tomography (NCCT) remains the primary diagnostic tool in stroke emergencies due to its speed, accessibility, and cost-effectiveness. NCCT enables the exclusion of hemorrhage and directs attention to ischemic causes resulting from arterial flow obstruction. Quantification of NCCT findings employs the Alberta Stroke Program Early Computed Tomography Score (ASPECTS), which evaluates affected brain structures. This study seeks to identify early alterations in NCCT density in patients with stroke symptoms using a binary classifier distinguishing NCCT scans with and without stroke. To achieve this, various well-known deep learning architectures, namely VGG3D, ResNet3D, and DenseNet3D, validated in the ImageNet challenges, are implemented with 3D images covering the entire brain volume. The training results of these networks are presented, wherein diverse parameters are examined for optimal performance. The DenseNet3D network emerges as the most effective model, attaining a training set accuracy of 98% and a test set accuracy of 95%. The aim is to alert medical professionals to potential stroke cases in their early stages based on NCCT findings displaying altered density patterns.Publicación Forensic Analysis Laboratory for Sport Devices: A Practical Case of Use(MDPI, 2023) Donaire Calleja, Pablo; Robles Gómez, Antonio; Tobarra Abad, María de los Llanos; Pastor Vargas, RafaelAt present, the mobile device sector is experiencing significant growth. In particular, wear- 1 able devices have become a common element in society. This fact implies that users unconsciously 2 accept the constant dynamic collection of private data about their habits and behaviours. Therefore, 3 this work focuses on highlighting and analyzing some of the main issues that forensic analysts face 4 in this sector, such as the lack of standard procedures for analysis and the common use of private 5 protocols for data communication. Thus, it is almost impossible for a digital forensic specialist to 6 fully specialize in the context of wearables, such as smartwatches for sports activities. With the aim 7 of highlighting these problems, a complete forensic analysis laboratory for such sports devices is 8 described in this paper. We selected a smartwatch belonging to the Garmin Forerunner Series, due to 9 its great popularity. Through an analysis, its strengths and weaknesses in terms of data protection 10 are described. We also analyze how companies are increasingly taking personal data privacy into 11 consideration, in order to minimize unwanted information leaks. Finally, a set of initial security 12 recommendations for the use of these kinds of devices are provided to the reader.Publicación Analyzing the Users’ Acceptance of an IoT Cloud Platform using the UTAUT/TAM Model(Institute of Electrical and Electronics Engineers, 2021) Haut, Juan M.; Robles Gómez, Antonio; Tobarra Abad, María de los Llanos; Pastor Vargas, Rafael; Hernández Berlinches, RobertoAntonio Robles-Gómez, Llanos Tobarra, Rafael Pastor-Vargas, Roberto Hernández, Juan M. Haut; Título:; Publicación: . ISSN (https://doi.org/10.1109/ACCESS.2021.3125497);Publicación A Data-Driven Approach to Engineering Instruction: Exploring Learning Styles, Study Habits, and Machine Learning(IEEE Xplore, 2025-01-10) Isaza Domínguez, Lauren Genith; Robles Gómez, Antonio; Pastor Vargas, RafaelThis study examined the impact of learning style and study habit alignment on the academic success of engineering students. Over a 16-week semester, 72 students from Process Engineering and Electronic Engineering programs at the Universidad de Los Llanos participated in this study. They completed the Learning Styles Index questionnaire on the first day of class, and each week, teaching methods and class activities were aligned with one of the four learning dimensions of the Felder-Silverman Learning Styles Model. Lesson 1 focused on one side of a learning dimension, lesson 2 on the opposite side, and the tutorial session incorporated both. Quizzes and engagement surveys assessed short-term academic performance, whereas midterm and final exam results measured long-term performance. Paired t-tests, Cohen’s effect size, and two-way ANOVA showed that aligning teaching methods with learning styles improved students’short-term exam scores and engagement. However, multiple regression analysis indicated that study habits (specifically time spent studying, frequency, and scores on a custom-developed study quality survey) were much stronger predictors of midterm and final exam performance. Several machine learning models, including Random Forest and Voting Ensemble, were tested to predict academic performance using study behavior data. Voting Ensemble was found to be the strongest model, explaining 83% of the variance in final exam scores, with a mean absolute error of 3.18. Our findings suggest that, while learning style alignment improves short-term engagement and comprehension, effective study habits and time management play a more important role in long-term academic success.Publicación A WoT Platform for Supporting Full-Cycle IoT Solutions from Edge to Cloud Infrastructures: A Practical Case(MDPI, 2020-07-05) Pastor Vargas, Rafael; Tobarra Abad, María de los Llanos; Robles Gómez, Antonio; Martín Gutiérrez, Sergio; Hernández Berlinches, Roberto; Cano, Jesús; MDPI; https://orcid.org/0000-0001-6926-1311Internet of Things (IoT) learning involves the acquisition of transversal skills ranging from the development based on IoT devices and sensors (edge computing) to the connection of the devices themselves to management environments that allow the storage and processing (cloud computing) of data generated by sensors. The usual development cycle for IoT applications consists of the following three stages: stage 1 corresponds to the description of the devices and basic interaction with sensors. In stage 2, data acquired by the devices/sensors are employed by communication models from the origin edge to the management middleware in the cloud. Finally, stage 3 focuses on processing and presentation models. These models present the most relevant indicators for IoT devices and sensors. Students must acquire all the necessary skills and abilities to understand and develop these types of applications, so lecturers need an infrastructure to enable the learning of development of full IoT applications. AWeb of Things (WoT) platform named Labs of Things at UNED (LoT@UNED) has been used for this goal. This paper shows the fundamentals and features of this infrastructure, and how the different phases of the full development cycle of solutions in IoT environments are implemented using LoT@UNED. The proposed system has been tested in several computer science subjects. Students can perform remote experimentation with a collaborativeWoT learning environment in the cloud, including the possibility to analyze the generated data by IoT sensors.Publicación SiCoDeF² Net: Siamese Convolution Deconvolution Feature Fusion Network for One-Shot Classification(IEEE, 2021) Kumar Roy, Swalpa; Kar, Purbayan; Paoletti, Mercedes E.; Haut, Juan M.; Pastor Vargas, Rafael; Robles Gómez, AntonioNowadays, deep convolutional neural networks (CNNs) for face recognition exhibit a performance comparable to human ability in the presence of the appropriate amount of labelled training data. However, training CNNs remains as an arduous task due to the lack of training samples. To overcome this drawback, applications demand one-shot learning to improve the obtained performances over traditional machine learning approaches by learning representative information about data categories from few training samples. In this context, Siamese convolutional network ( SiConvNet ) provides an interesting deep architecture to tackle the data limitation. In this regard, applying the convolution operation on real world images by using the trainable correlative Gaussian kernel adds correlations to the output images, which hinder the recognition process due to the blurring effects introduced by the convolution kernel application. As a result the pixel-wise and channel-wise correlations or redundancies could appear in both single and multiple feature maps obtained by a hidden layer. In this sense, convolution-based models fail to generalize the feature representation because of both the strong correlations presence in neighboring pixels and the channel-wise high redundancies between different channels of the feature maps, which hamper the effective training. Deconvolution operation helps to overcome the shortcomings that limit the conventional SiConvNet performance, learning successfully correlation-free features representation. In this paper, a simple but efficient Siamese convolution deconvolution feature fusion network ( SiCoDeF 2 Net ) is proposed to learn the invariant and discriminative complementary features generated from both the (i) sub-convolution (SCoNet) and (ii) sub deconvolutional (SDeNet) networks using a concatenation operation which significantly improves the one-shot unconstrained facial recognition task. Extensive experiments performed on several widely used benchmarks, provide promising results, where the proposed SiCoDeF 2 Net model significantly outperforms the current state-of-art in terms of classification accuracy, F1, precision and recall. The code will be available on: https://github.com/purbayankar/SiCoDeF2Net .Publicación Students’ Acceptance and Tracking of a New Container-Based Virtual Laboratory(MDPI, 2020) Cano, Jesús; Tobarra Abad, María de los Llanos; Robles Gómez, Antonio; Pastor Vargas, Rafael; Hernández Berlinches, Roberto; Duque Fernández, AndrésPresently, the ever-increasing use of new technologies helps people to acquire additional skills for developing an applied critical thinking in many contexts of our society. When it comes to education, and more particularly in any Engineering subject, practical learning scenarios are key to achieve a set of competencies and applied skills. In our particular case, the cybersecurity topic with a distance education methodology is considered and a new remote virtual laboratory based on containers will be presented and evaluated in this work. The laboratory is based on the Linux Docker virtualization technology, which allows us to create consistent realistic scenarios with lower configuration requirements for the students. The laboratory is comparatively evaluated with our previous environment, LoT@UNED, from both the points of view of the students’ acceptance with a set of UTAUT models, and their behavior regarding evaluation items, time distribution, and content resources. All data was obtained from students’ surveys and platform registers. The main conclusion of this work is that the proposed laboratory obtains a very high acceptance from the students, in terms of several different indicators (perceived usefulness, estimated effort, social influence, attitude, ease of access, and intention of use). Neither the use of the virtual platform nor the distance methodology employed affect the intention to use the technology proposed in this workPublicación Web of Things Platforms for Distance Learning Scenarios in Computer Science Disciplines: A Practical Approach(MDPI, 2019) Tobarra Abad, María de los Llanos; Robles Gómez, Antonio; Pastor Vargas, Rafael; Hernández Berlinches, Roberto; Cano, Jesús; López, Daniel; https://orcid.org/0000-0001-6926-1311Problem-based learning is a widely used learning methodology in the field of technological disciplines, especially in distance education environments. In these environments, the most used tools, which provide learning scenarios, are remote and virtual laboratories. Internet of Things (IoT) devices can be used as remote or virtual laboratories. In addition to this, they can be organized/orchestrated to build remote maker spaces through the web. These types of spaces are called the Web of Things (WoT). This paper proposes the use of these types of spaces and their integration as practical activities into the curricula of technological subjects. This approach will allow us to achieve two fundamental objectives: (1) To improve the academic results (grades) of students; and (2) to increase engagement and interest of students in the studied technologies, including IoT devices. These platforms are modeled using archetypes based on different typologies and usage scenarios. In particular, these usage scenarios will implement a learning strategy for each problem to be solved. The current work shows the evolution of these archetypes and their application in the teaching of disciplines/subjects defined in computer science, such as distributed computing and cybersecurity.Publicación Detection of Cerebral Ischaemia using Transfer Learning Techniques(IEEE) Antón Munárriz, Cristina; Haut, Juan M.; Paoletti, Mercedes E.; Benítez Andrades, José Alberto; Pastor Vargas, Rafael; Robles Gómez, AntonioCerebrovascular accident (CVA) or stroke is one of the main causes of mortality and morbidity today, causing permanent disabilities. Its early detection helps reduce its effects and its mortality: time is brain. Currently, non-contrast computed tomography (NCCT) continues to be the first-line diagnostic method in stroke emergencies because it is a fast, available, and cost-effective technique that makes it possible to rule out haemorrhage and focus attention on the ischemic origin, that is, due to obstruction to arterial flow. NCCT are quantified using a scoring system called ASPECTS (Alberta Stroke Program Early Computed Tomography Score) according to the affected brain structures. This paper aims to detect in an initial phase those CTs of patients with stroke symptoms that present early alterations in CT density using a binary classifier of CTs without and with stroke, to alert the doctor of their existence. For this, several well-known neural network architectures are implemented in the ImageNet challenges (VGG, NasNet, ResNet and DenseNet), with 3D images, covering the entire brain volume. The training results of these networks are exposed, in which different parameters are tested to obtain maximum performance, which is achieved with a DenseNet3D network that achieves an accuracy of 98% in the training set and 95% in the test set