Actas y comunicaciones de congresos

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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 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.
A simple measure to assess non-response
(2011-06-19) Peñas Padilla, Anselmo; Rodrigo Yuste, Álvaro
There are several tasks where is preferable not responding than responding incorrectly. This idea is not new, but despite several previous attempts there isn’t a commonly accepted measure to assess non-response. We study here an extension of accuracy measure with this feature and a very easy to understand interpretation. The measure proposed (c@1) has a good balance of discrimination power, stability and sensitivity properties. We show also how this measure is able to reward systems that maintain the same number of correct answers and at the same time decrease the number of incorrect ones, by leaving some questions unanswered. This measure is well suited for tasks such as Reading Comprehension tests, where multiple choices per question are given, but only one is correct.
Analyzing information retrieval methods to recover broken web links
(2011-06-19) Martínez Romo, Juan; Araujo Serna, M. Lourdes
In this work we compare different techniques to automatically find candidate web pages to substitute broken links. We extract information from the anchor text, the content of the page containing the link, and the cache page in some digital library.The selected information is processed and submitted to a search engine. We have compared different information retrievalmethods for both, the selection of terms used to construct the queries submitted to the search engine, and the ranking of the candidate pages that it provides, in order to help the user to find the best replacement. In particular, we have used term frequencies, and a language model approach for the selection of terms; and cooccurrence measures and a language model approach for ranking the final results. To test the different methods, we have also defined a methodology which does not require the user judgments, what increases the objectivity of the results.
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.
Aplicaciónde técnica de inteligencia artificial y tratamiento de señales en fusión
(CEA-IFAC, 2005, 2005-01-01) Santos, Matilde; Farias, Gonzalo
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.
Automated IoT vulnerability classification using Deep Learning
(2025-07-01) Sernández Iglesias, Daniel; Enrique Fernández Morales,; García Merino, José Carlos; Tobarra Abad, María de los Llanos; Pastor Vargas, Rafael; Robles Gómez, Antonio; Sarraipa, Joao
Technological advancements in the development of low-power chips have enabled everyday objects to connect to the Internet, giving rise to the concept known as the Internet of Things (IoT). It is currently estimated that there are approximately 16 billion IoT connections worldwide, a figure expected to double by 2030. However, this rapid growth of the IoT ecosystem has introduced new vulnerabilities that could be exploited by malicious actors. Since many IoT devices handle personal and sensitive information, threats to these devices can have severe consequences. Moreover, a series of cybersecurity incidents could undermine public trust in IoT technology, potentially delaying its widespread adoption across various sectors.Common Vulnerabilities and Exposures records (also known by their acronym as CVEs) is a public cataloging system designed to identify and list known security vulnerabilities in software and hardware products. This system is developed and maintained by MITRE with the support of the cybersecurity community and sponsored by the U.S. Department of Homeland Security (DHS) through the Cybersecurity and Infrastructure Security Agency (CISA). CVE provides a reference database that enables security researchers, manufacturers, and organizational security managers to more effectively identify and address security issues.In our study, we have focused on CVEs exclusively oriented towards IoT systems, with the aim of analyzing the main vulnerabilities detected from 2010 to nowadays as a basis for detecting the main attack vectors in IoT systems. As part of this effort we have created the following dataset. CVEs records include various metrics such as: - Common Weakness Enumeration (CWE), mainly focused on technical classification of vulnerabilities. - Common Vulnerability Scoring System (CVSS), which reports about different metrics such as the attack vector, the severity of the vulnerability or the impact level of the exploitation of the vulnerability. This is one of the most informative metric. - Stakeholder-Specific Vulnerability Categorization (SSVC), oriented towards help cybersecurity team to handle properly the vulnerability. These metrics allow security teams on the one hand to prioritize, such vulnerabilities within their security program, evaluating efforts to mitigate them. But according to our analysis of our dataset, around the 14% of CVEs records do not contain any metric. Around the 83% of CVEs registries contain CWE metric (an ID or its textual description). This metric, as it is explained before, only reports about the type of vulnerability from a technic point of view. Only the 10% of CVEs registries contain SSVC metrics. And CVSS, in its different versions, appears only in the 40% of the studied CVEs registries. Additionally, most of studied records includes metrics a retrospectively, several weeks or months later the vulnerability is disclosed. Thus, cybersecurity teams must trust their previous knowledge in order to distinguish which vulnerabilities are relevant and which not.To tackled this situation, our proposal is focused in the application of Deep Learning techniques in order to classify the severity of CVE records from its textual description. Textual description is a mandatory field that is present in all CVEs records. To achieve this objective, we trained the BiLSTM algorithm using the CVE records with CVSS metrics and its description field; and performed a comparative study of different hyperparameter configurations to find the optimal configuration. The metrics for model evaluation that have been studied are accuracy, loss and F1-score.
BIG-AFF: Exploring low cost and low intrusive infrastructures for affective computing in secondary schools
(ACM, 2017-07-09) González Boticario, Jesús; Santos, Olga C.; Cabestrero Alonso, Raúl; Quirós Expósito, Pilar; Salmeron Majadas, Sergio; Uría Rivas, Raúl; Arevalillo Herráez, Miguel; Ferri, Francesc J.
Recent research has provided solid evidence that emotions strongly affect motivation and engagement, and hence play an important role in learning. In BIG-AFF project, we build on the hypothesis that ``it is possible to provide learners with a personalised support that enriches their learning process and experience by using low intrusive (and low cost) devices to capture affective multimodal data that include cognitive, behavioural and physiological information''. In order to deal with the affect management complete cycle, thus covering affect detection, modelling and feedback, there is lack of standards and consolidated methodologies. Being our goal to develop realistic affect-aware learning environments, we are exploring different approaches on how these can be supported by either by traditional non-intrusive interaction sources or low intrusive and inexpensive sensing devices. In this work we describe the main issues involved in two user studies carried out with high school learners, highlight some open problems that arose when designing the corresponding experimental settings. In particular, the studies involved varied nature of information sources and each focused on one of the approaches. Our experience reflects the need to develop an extensive knowledge about the organization of this type of experiences that consider user-centric development and evaluation methodologies.
Challenges for Inclusive Affective Detection in Educational Scenarios
(Springer Nature, 2013) Santos, Olga C.; Rodríguez Ascaso, Alejandro; González Boticario, Jesús; Salmeron Majadas, Sergio; Quirós Expósito, Pilar; Cabestrero Alonso, Raúl
There exist diverse challenges for inclusive emotions detection in educational scenarios. In order to gain some insight about the difficulties and limitations of them, we have analyzed requirements, accommodations and tasks that need to be adapted for an experiment where people with different functional profiles have taken part. Adaptations took into consideration logistics, tasks involved and user interaction techniques. The main aim was to verify to what extent the same approach, measurements and technological infrastructure already used in previous experiments were adequate for inducing emotions elicited from the execution of the experiment tasks. In the paper, we discuss the experiment arrangements needed to cope with people with different functional profiles, which include adaptations on the analysis and results. Such analysis was validated in a pilot experiment with 3 visually impaired participants.
Circuit Testing Based on Fuzzy Sampling with BDD Bases
(University of Hawaiʻi at Mānoa, 2023) Pinilla, Elena; Fernández Amoros, David José; Heradio Gil, Rubén
Fuzzy testing of integrated circuits is an established technique. Current approaches generate an approximately uniform random sample from a translation of the circuit to Boolean logic. These approaches have serious scalability issues, which become more pressing with the ever-increasing size of circuits. We propose using a base of binary decision diagrams to sample the translations as a soft computing approach. Uniformity is guaranteed by design and scalability is greatly improved. We test our approach against five other state-of-the-art tools and find our tool to outperform all of them, both in terms of performance and scalability.
Comparison of physiological data acquisition for modeling of drivers in autonomous vehicles
(Springer Nature, 2025-04-24) Fernandez Matellan, Raul; Puertas Ramírez, David; Martín Gómez, David; González Boticario, Jesús
Humans can undergo rapid emotional changes and these changes can significantly affect their ability to perform tasks. Consequently, when we develop Human-Centred Symbiotic Artificial Intelligence (HCSAI) systems to support the interaction between autonomous systems and drivers, the intelligent system controlling the vehicle must adapt to the state of the user. This symbiotic relationship highlights the importance of collaboration and cooperation between humans and agents of artificial intelligence (AI). In the field of Autonomous Vehicles (AV), measurements must be made using non-invasive devices that do not interfere with the driving task. We have therefore used wristbands to measure physiological signals. This comparison is used to select the right equipment for setting up user modelling in different levels of autonomous vehicles. We compared the accuracy, precision and ease of use of three different wristbands: Fitbit Sense2, Empatica E4 and Emotibit. We tested the performance of the bands in two different driving scenarios: SAE Level 4 environment using autonomous golf carts (iCab), and a real-world SAE Level 2 driving environment in Scotland using a Toyota Prius equipped with Comma OpenPilot technology. The Fitbit Sense2 does not allow researchers to access raw data. The Emotibit and the Empatica E4 are designed for research, so they provide access to raw data, while the Empatica E4 is easier to use than the Emotibit. The comparison calls for the development of open source codes that will facilitate integration with different operating systems and other devices, as well as an easy way to use the devices in real time.
Composición fotográfica mediante el uso de un dron
(Comité Español de Automática, 2024-07-15) Sánchez García, Juan Miguel; Sánchez Moreno, José; Moreno Salinas, David
La composición fotográfica, conocida como mosaicos, es crucial en aplicaciones donde no es posible capturar toda la extensión de grandes superficies en una sola toma. Por ende, se requiere fotografiar secciones más pequeñas para luego componerlas y lograr una reproducción lo más precisa posible de la realidad. En este trabajo se presenta el resultado de aplicar los principios de las distintas etapas necesarias para crear un mosaico, complementado con el uso de un dron para la captura de las imágenes. La creación del mosaico implica técnicas avanzadas de procesamiento de imágenes que facilitan la detección de características, la transformación geométrica y la alineación de píxeles. Sin embargo, la experimentación con diferentes algoritmos ha revelado que no siempre es viable encontrar una transformación geométrica que produzca un mosaico de calidad, especialmente cuando las características de la fotografía no son óptimas, lo cual puede ser atribuible, en parte, a la resolución de los dispositivos fotográficos utilizados.
Correlation-Aware Averaging for Federated Learning in Remote Sensing Data Classification
(IEEE, 2024) Moreno Álvarez, Sergio; han, lirong; Paoletti, Mercedes Eugenia; Haut, Juan Mario; https://orcid.org/0000-0002-8613-7037; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0001-6701-961X
The increasing volume of remote sensing (RS) data offers substantial benefits for the extraction and interpretation of features from these scenes. Indeed, the detection of distinguishing features among captured materials and objects is crucial for classification purposes, such as in environmental monitoring applications. In these algorithms, the classes characterized by lower correlation often exhibit more distinct and discernible features, facilitating their differentiation in a straightforward manner. Nevertheless, the rise of Big Data provides a wide range of data acquired through multiple decentralized devices, where its susceptibility to be shared among various users or clients presents challenges in safeguarding privacy. Meanwhile, global features for similar classes are required to be learned for generalization purposes in the classification process. To address this, federated learning (FL) emerges as a privacy efficient decentralized solution. Firstly, in such scenarios, proprietary data is held by individual clients participating in the training of a global model. Secondly, clients may encounter challenges in identifying features that are more distinguishable within the data distributions of other clients. In this study, in order to handle these challenges, a novel methodology is proposed that considers the least correlated classes (LCCs) included in each client data distribution. This strategy exploits the distinctive features between classes, thereby enhancing performance and generalization ability in a secure and private environment.
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.
Deep Attention-Driven HSI Scene Classification Based on Inverted Dot-Product
(Institute of Electrical and Electronics Engineers Inc., 2022) Paoletti, Mercedes Eugenia; Tao, Xuanwen; han, lirong; Wu, Zhaoyue; Moreno Álvarez, Sergio; Haut, Juan M.; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0003-1093-0079; https://orcid.org/0000-0002-8613-7037; https://orcid.org/0000-0002-6797-2440; https://orcid.org/0000-0001-6701-961X
Capsule networks have been a breakthrough in the field of automatic image analysis, opening a new frontier in the art for image classification. Nevertheless, these models were initially designed for RGB images and naively applying these techniques to remote sensing hyperspectral images (HSI) may lead to sub-optimal behaviour, blowing up the number of parameters needed to train the model or not correctly modeling the spectral relations between the different layers of the scene. To overcome this drawback, this work implements a new capsule-based architecture with attention mechanism to improve the HSI data processing. The attention mechanism is applied during the concurrent iterative routing procedure through an inverted dot-product attention
Deep Robust Hashing Using Self-Distillation for Remote Sensing Image Retrieval
(IEEE, 2024) han,lirong; Paoletti, Mercedes Eugenia; Moreno Álvarez, Sergio; Haut, Juan Mario; Plaza, Antonio; https://orcid.org/0000-0002-8613-7037; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0001-6701-961X; https://orcid.org/0000-0002-9613-1659
This paper presents a novel self-distillation based deep robust hash for fast remote sensing (RS) image retrieval. Specifically, there are two primary processes in our proposed model: teacher learning (TL) and student learning (SL). Two transformed samples are produced from one sample image through nuanced and signalized transformations, respectively. Transformed samples are fed into both the TL and the SL flows. To reduce discrepancies in the processed samples and guarantee a consistent hash code, the parameters are shared by the two modules during the training stage. Then, a resilient module is employed to enhance the image features in order to ensure more dependable hash code production. Lastly, a three-component loss function is developed to train the entire model. Comprehensive experiments are conducted on two common RS datasets: UCMerced and AID. The experimental results validate that the proposed method has competitive performance against other RS image hashing methods.
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
Determinación de parámetros de la transfomada Wavelet para la clasificación de señales del diagnóstico scattering Thomson
(Jornadas de Automática 2004, 2004-01-01) Santos, Matilde; Fernández Marrón, José Luis; Dormido Canto, Sebastián; Farias, Gonzalo
Distributed reconfiguration of distance-based formations with virtual surface constraints
(IEEE, 2024) Guinaldo Losada, María; Sánchez Moreno, José; S. Zaragoza; Mañas Álvarez, Francisco José
This paper proposes a method to recover from the failure or loss of a subset of agents in a distance-based formation problem, where the system is initially deployed forming a virtual shield embedded in the 3D space. First, a distributed algorithm is proposed to restore the topology, which is a Delaunay triangulation. After that, the nodes execute a distance-based distributed control law that considers adaptive target distances. These values are computed in parallel by the nodes, which try to reach an agreement with some constraints, given by the desired shield shape. The updating policy is based on events. The results are illustrated through simulation examples.

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