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Pastor Vargas, Rafael

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0000-0002-4089-9538
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Pastor Vargas
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Rafael
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2016 - 201932020 - 202521
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Autor: Robles Gómez, Antonio ×

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Mostrando 1 - 10 de 25
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    Dataset Generation and Study of Deepfake Techniques
    (Springer, 2023) Falcón López, Sergio Adrián; Robles Gómez, Antonio; Tobarra Abad, María de los Llanos; Pastor Vargas, Rafael
    The consumption of multimedia content on the Internet has nowadays been expanded exponentially. These trends have contributed to fake news can become a very high influence in the current society. The latest techniques to influence the spread of digital false information are based on methods of generating images and videos, known as Deepfakes. This way, our research work analyzes the most widely used Deepfake content generation methods, as well as explore different conventional and advanced tools for Deepfake detection. A specific dataset has also been built that includes both fake and real multimedia contents. This dataset will allow us to verify whether the used image and video forgery detection techniques can detect manipulated multimedia content.
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    Forensic Technologies to Automate the Acquisition of Digital Evidences
    (IEEE, 2022) García Guerrero, David; Tobarra Abad, María de los Llanos; Robles Gómez, Antonio; Pastor Vargas, Rafael
    The main goal of this work is to propose the automatic acquisition of evidences in a remote way. This automated capacity becomes interesting for companies with extensive networks and/or several locations, as it allows them to delegate and centralize the acquisition task at a single point in their structure, while saving time and travel costs. This research has been carried out through the initial implementation of a virtual laboratory made up of a network and different scenarios, by including an experimentation process. The virtual network includes both the machine from which automatic acquisitions are performed and the devices from retrieving the evidence. The group of devices will be made up of various experiments. The aim is to analyze the viability of the acquisition in different scenarios, since distributed networks are not homogeneous in the real world
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    Anomaly Detection in Smart Rural IoT Systems
    (Springer, 2025-06) Fernández Morales, Enrique; Tobarra Abad, María de los Llanos; Robles Gómez, Antonio; Pastor Vargas, Rafael; Vidal Balboa, Pedro; Sarraipa, Joao
    The main contribution of this work is based on the presentation of new AI models for the detection of attacks, within IoT systems using an extensive and complete dataset. In this context, we evaluate not only the performance of the models in terms of detection of attacks, but also their resource consumption, such as the time needed to analyze a sample, the consumption of computing cycles to analyze a sample, as well as the hard disk usage to store the AI models. Its application is oriented to the context of IoT systems in rural environments, where devices deployed in these environments usually have strong restrictions on these resources. Our results indicate that the OPTIMIST-LSTM model offers the best balance between accuracy and generalization, whereas XAI-IoT stands out for its computational efficiency, making them the most suitable for implementation in IoT infrastructures with limited resources.
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    Assessing Feature Selection Techniques for AI-based IoT Network Intrusion Detection
    (Springer, 2025-06) García Merino, José Carlos; Tobarra Abad, María de los Llanos; Robles Gómez, Antonio; Pastor Vargas, Rafael; Vidal Balboa, Pedro; Dionisio Rocha, André; Jardim Gonçalves, Ricardo
    The widespread adoption of Internet of Things (IoT) technology in rural areas has led to qualitative leaps in fields such as agriculture, livestock farming, and transportation, giving rise to the concept of Smart Rural. However, Smart Rural IoT ecosystems are often vulnerable to cyberattacks. Although Artificial Intelligence (AI) based intrusion detection systems offer an effective solution to protect these environments, IoT devices are typically constrained in terms of memory and computation capabilities, making it essential to optimise the computational burden of AI models. This work explores different feature selection techniques to develop compact and fast Random Forest models for anomaly detection in IoT environments. The obtained results demonstrate that appropriate feature selection can reduce model size and inference time by at least 45% and 8%, respectively, without compromising predictive performance.
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    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, Antonio
    Cerebrovascular 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
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    Researchers’ perceptions of DH trends and topics in the English and Spanish-speaking community. DayofDH data as a case study
    (Jagiellonian University & Pedagogical University (Cracovia), 2016-07-22) González-Blanco García, Elena; Rio Riande, Gimena del; Robles Gómez, Antonio; Ros Muñoz, Salvador; Hernández Berlinches, Roberto; Tobarra Abad, María de los Llanos; Caminero Herráez, Agustín Carlos; Pastor Vargas, Rafael
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    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/0000-0001-6926-1311
    Problem-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.
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    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, Andrés
    Presently, 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 work
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    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, Antonio
    Nowadays, 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 .
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    EVI-LINHD, a virtual research environment for the Spanish speaking community
    (Oxford University Press, 2017-12) González-Blanco García, Elena; Rio Riande, Gimena del; Díez Platas, María Luisa; Olmo, Álvaro del; Urízar, Miguel; Martínez Cantón, Clara Isabel; Ros Muñoz, Salvador; Pastor Vargas, Rafael; Robles Gómez, Antonio; Caminero Herráez, Agustín Carlos
    Laboratorio de Innovación en Humanidades Digitales (UNED) has developed Entorno Virtual de Investigación del Laboratorio de Innovación en Humanidades Digitales (EVI-LINHD), the first virtual research environment devoted mainly to Spanish speakers interested in digital scholarly edition. EVI-LINHD combines different open-source software for developing a complete digital project: (1) a Webbased application markup tool—TEIscribe—combined with an eXistdb solution and a TEIPublisher platform, (2) Omeka for digital libraries, and (3) WordPress for simple Web pages. All these instances are linked to a local installation of the LINDAT/Common Language Resources and Technology Infrastructure (CLARIN) digital repository. LINDAT/CLARIN allows EVI-LINHD users to have their projects deposited and stored safely. Thanks to this solution, EVI-LINHD projects also improve their visibility. The specific metadata profile used in the repository is based on Dublin Core, and it is enriched with the Spanish translation of DARIAH’s Taxonomy of Digital Research Activities in the Humanities.