This work presents a fully automatic system for the screening of chest X-ray images from portable devices under the analysis of three different clinical categories: normal, pathological cases of pulmonary diseases with findings similar to those of COVID-19, and COVID-19 cases. Our methodology was validated using a dataset retrieved specifically for this study, which was provided by the Radiology Service of the Complexo Hospitalario Universitario A Coruña (CHUAC). Despite the poor quality conditions of chest X-ray images acquired by portable devices, satisfactory results were obtained, demonstrating the robustness and great potential of the proposed system to help front-line clinicians in the diagnosis and treatment of patients with COVID-19. Full article

The Epiretinal Membrane (ERM) is an ocular disease that appears as a fibro-cellular layer of tissue over the retina, specifically, over the Inner Limiting Membrane (ILM). It causes vision blurring and distortion, and its presence can be indicative of other ocular pathologies, such as diabetic macular edema. The ERM diagnosis is usually performed by visually inspecting Optical Coherence Tomography (OCT) images, a manual process which is tiresome and prone to subjectivity. In this work, we present a methodology for the automatic segmentation and visualisation of the ERM in OCT volumes using deep learning. By employing a Densely Connected Convolutional Network, every pixel in the ILM can be classified into either healthy or pathological. Thus, a segmentation of the region susceptible to ERM appearance can be produced. This methodology also produces an intuitive colour map representation of the ERM presence over a visualisation of the eye fundus created from the OCT volume. In a series of representative experiments conducted to evaluate this methodology, it achieved a Dice score of $0.826\pm 0.112$ and a Jaccard index of $0.714\pm 0.155$. The results that were obtained demonstrate the competitive performance of the proposed methodology when compared to other works in the state of the art. Full article

Physical activity (PA) performed in group can slow down the decline in motor functions in people with disabilities. With this objective, Interactive Rehab Orchestra (IRO) was developed. IRO is an interactive multiplayer musical game that looks for reducing sedentary lifestyles by promoting PA. The individuals are responsible for playing the melody correctly. To do that, they must perform a movement when the on-screen avatar reaches a certain area. If the action is not performed, the melody will stop playing for a certain time interval. IRO is highly configurable, allowing the controller to be adapted to player skills. The customization of melodies and images is also possible according to the players’ preferences, which helps to enhance player engagement. In addition, a configurable color code allows identifying when to perform an action. IRO incorporates a statistical summary to assess the evolution of the user. In this way, IRO aims at encouraging PA through music to maintain/improve muscle tone and the subjects’ mobility, quantifying PA intensity, in relation to motor skills, and promoting PA so that participants can adhere to a specific program with long-term follow-up. Full article

This paper describes several computer access methods tested by Eva, a woman with choreoathetosic cerebral palsy. This disease prevents her from controlling the peripherals and configurations that normally give access to information and communication technologies, further limiting her independence. To make Eva access a computer, we focused our efforts on the methodologies that Eva could control by just moving her neck and head. These sensors were: Kinect, inertial measurement units (IMU), and video. Kinect, composed of a system of cameras and sensors, gives the option to interact and control the devices contactlessly. The IMU is a device consisting of an accelerometer and a gyroscope that measure velocity, orientation, and gravitational forces. For live image processing, a common webcam was used. During the development of the experiment, Eva must follow a sequence shown on the computer screen that alternates movement of the head with rest. These movements involved moving the head up, down, right, or left. Our results showed that the Kinect system could not be used effectively, while the image-processing algorithm obtained the best performance. Full article

COVID-19 is characterized by its impact on the respiratory system and, during the global outbreak of 2020, specific protocols had to be designed to contain its spread within hospitals. This required the use of portable X-ray devices that allow for a greater flexibility in terms of their arrangement in rooms not specifically designed for such purpose. However, their poor image quality, together with the subjectivity of the expert, can hinder the diagnosis process. Therefore, the use of automatic methodologies is advised. Even so, their development is challenging due to the scarcity of available samples. For this reason, we present a COVID-19-specific methodology able to segment these portable chest radiographs with a reduced number of samples via multiple transfer learning phases. This allows us to extract knowledge from two related fields and obtain a robust methodology with limited data from the target domain. Our proposal aims to help both experts and other computer-aided diagnosis systems to focus their attention on the region of interest, ignoring unrelated information. Full article

The global pandemic of COVID-19 raises the importance of having fast and reliable methods to perform an early detection and to visualize the evolution of the disease in every patient, which can be assessed with chest X-ray imaging. Moreover, in order to reduce the risk of cross contamination, radiologists are asked to prioritize the use of portable chest X-ray devices that provide a lower quality and lower level of detail in comparison with the fixed machinery. In this context, computer-aided diagnosis systems are very useful. During the last years, for the case of medical imaging, they are widely developed using deep learning strategies. However, there is a lack of sufficient representative datasets of the COVID-19 affectation, which are critical for supervised learning when training deep models. In this work, we propose a fully automatic method to artificially increase the size of an original portable chest X-ray imaging dataset that was specifically designed for the COVID-19 diagnosis, which can be developed in a non-supervised manner and without requiring paired data. The results demonstrate that the method is able to perform a reliable screening despite all the problems associated with images provided by portable devices, providing an overall accuracy of 92.50%. Full article

People with autism spectrum disorder (ASD) have great difficulties in social interaction and in the management of personal and other people’s emotions. This work aimed at developing an intelligent bracelet, capable of inferring the children’s emotional state, transmitting it to others, and, above all, informing the patients themselves so that they can learn to recognise, control, and work with, as well as to improve their self-knowledge and their relationship with their environment. Electrodermal activity (EDA) and photoplethysmography (PPG) are useful in combined psychophysiological and medical studies to determine the mood of patients. Due to COVID-19, no experiments with subjects could be carried out, although the modules were validated, and a public database was used to test the system’s application. The results concluded that, in general, when an individual is altered or becomes nervous, either positively or negatively (also known as valence) to a stimulus, their heart rate and sweating increase. This is the kind of relationship between physiological signals and external stimuli that the design of these circuits was intended to confirm. Finally, with the indicators of nervous system activity and knowing the behaviour of skin conductance in response to each basic emotion, it can be determined whether the subject is in a situation of pleasure or frustration in response to each reaction. Full article

The correct diagnosis of high blood pressure is important to avoid cardiovascular diseases. In this work, we propose a low-cost noninvasive blood-pressure measurement unit composed of a photoplethysmograph and an electrocardiograph. It is based on pulse transit time measurement, thus performing nonocclusive measurement. To test the effectiveness of this parameter, a total of five subjects were measured, verifying their effectiveness at all times. Full article

Phytoplankton blooming can compromise the quality of the water and its safety due to the negative effects of the toxins that some species produce. Therefore, the continuous monitoring of water sources is typically required. This task is commonly and routinely performed by specialists manually, which represents a major limitation in the quality and quantity of these studies. We present an accurate methodology to automate this task using multi-specimen images of phytoplankton which are acquired by regular microscopes. The presented fully automatic pipeline is capable of detecting and segmenting individual specimens using classic computer vision algorithms. Furthermore, the method can fuse sparse specimens and colonies when needed. Moreover, the system can differentiate genuine phytoplankton from other similar non-phytoplanktonic objects like zooplankton and detritus. These genuine phytoplankton specimens can also be classified in a target set of species, with special focus on the toxin-producing ones. The experiments demonstrate satisfactory and accurate results in each one of the different steps that compose this pipeline. Thus, this fully automatic system can aid the specialists in the routine analysis of water sources. Full article

Large companies use a lot of resources on workshop operator training and industrial machinery maintenance since the lack of this practice or its poor implementation increases the cost and risks of operating and handling sensitive and/or hazardous machinery. Industrial Augmented Reality (IAR), a major technology in the Industry 4.0 paradigm that may enhance worker performance, minimize hazards and improve manufacturing processes, could be beneficial in this situation. This paper presents an IAR solution that allows for visualizing and interacting with the digital twin of a critical system. Specifically, the augmented digital twin of an industrial cooler was developed. The proposed IAR system provides a dynamic way to perform operator training with a full-size model of the actual equipment and to provide step-by-step guidance so that maintenance processes can be performed more safely and efficiently. The proposed system also allows several users to use devices at the same time, creating a new type of collaborative interaction by viewing the model in the same place and state. Performance tests with many simultaneous users have been conducted, with response latency being measured as the number of connected users grows. Furthermore, the suggested IAR system has been thoroughly tested in a real-world industrial environment. Full article

The emergence of the current pandemic has led to a new reality in which bureaucratic formalities have been affected in terms of health security, procedures, resource management, among others. Specifically, in the electoral processes, where the difficulty of fulfilling the social distance and the mobility restrictions reopen the debate on the implementation of other more advanced and modern alternatives, such as electronic voting (e-voting). This article presents the design and implementation of a decentralized e-voting system that has the potential to provide a higher level of transparency, security, and cost-efficiency. Hyperledger Fabric blockchain and smart contracts are used to cast votes, which are then recorded in an immutable way, giving voters anonymity and trust in the fairness of the election process. In addition, promising results of the performance of the e-voting system in terms of latency and transaction load are presented. Full article

This paper describes how we developed a novel low-cost assistive robotic platform, with AI-based perception capabilities, able to navigate autonomously using Robot Operating System (ROS). The platform is a differential wheeled robot, equipped with two motors and encoders, which are controlled with an Arduino board. It also includes a Jetson Xavier processing board on which we deploy all AI processes, and the ROS architecture. As a result of the work, we have a fully functional platform, able to recognize actions online, and navigate autonomously through environments whose map has been preloaded. Full article

This paper presents RGen, a parallel data generator for benchmarking Big Data workloads, which integrates existing features and new functionalities in a standalone tool. The main functionalities developed in this work were the generation of text and graphs that meet the characteristics defined by the 4 Vs of Big Data. On the one hand, the LDA model has been used for text generation, which extracts topics or themes covered in a series of documents. On the other hand, graph generation is based on the Kronecker model. The experimental evaluation carried out on a 16-node cluster has shown that RGen provides very good weak and strong scalability results. RGen is publicly available to download at https://github.com/rubenperez98/RGen, accessed on 30 September 2021. Full article

In the era of big data, a vast amount of data are being produced. This results in two main issues when trying to discover knowledge from these data. There is a lot of information that is not relevant to the problem we want to solve, and there are many imperfections and errors in the data. Therefore, preprocessing these data is a key step before applying any kind of learning algorithm. Reducing the number of features to a relevant subset (feature selection) and reducing the possible values of continuous variables (discretisation) are two of the main preprocessing techniques. This paper will review different methods for completing these two steps, focusing on the big data context and giving examples of projects where they have been applied. Full article

This work focuses on the development of a software link interface tool between the Looxid Link Device coupled to the HTC Vive Pro VR HeadSets and the Unity platform, to generate real-time interactivity in virtual reality applications. The software incorporates a dynamic and parameterizable algorithm to be used as a core-engine in the real-time Biofeedback process, recognizing the values of the biological signals registered in each of the EEG channels of the Looxid Link device. The values of EEG frequencies detected in real time can be used to generate elements of interactivity, with different frequencies and intensities. Full article

The present work deals with the problem of detecting Denial of Service attacks in an IoT environment. To achieve this goal, a dataset registered in an MQTT protocol network is used, applying dimension reduction techniques combined with classification algorithms. The final classifiers presents successful results. Full article

We introduce nonparametric estimators to estimate the conditional survival function, cure probability and latency function in the setting of a mixture cure model when the cure status is partially known. For the sake of illustration, we present an application concerning patients hospitalized with COVID-19 in Galicia (Spain) during the first outbreak of the epidemic. Full article

The progress of LPWAN technologies in recent years has increased their use in various types of environments as well as increased the applications in which they are used. However, due to the duty cycle limitations of license-free based technologies, they have a considerable limitation for applications with frequent data transmission or real-time data. In this regard, technologies working in the 2.4 GHz band are a compelling option to consider but their main problem concerns their limited range. Fortunately, the new Bluetooth 5 standard has a new feature (Long Range mode) that is especially useful in long distance or large indoor environments. This paper describes a practical study on this new technology for indoor environments. The performed experiments evaluate reception range, communications quality, channel occupancy, response times, and power consumption. The obtained results indicate that a three-floor building of more than 4200 m${}^2$ may be covered with a stable signal with only two Bluetooth 5 nodes. Full article

This study explores the potential of music as a therapy element in digital therapy programs to improve mental health and well-being. Music induces an emotional component in the individual that translates into changes in their brain activity, which can be monitored through electroencephalography. A scoping review was conducted to identify the most recent relevant publications related to the effect of music on brain activity and emotional state in digital therapy programs. From 585 identified publications, six relevant publications were selected that meet all the requirements defined in the study. Full article

Information extracted from electronic health records (EHRs) is used for predictive tasks and clinical pattern recognition. Machine learning techniques also allow the extraction of knowledge from EHR. This study is a continuation of previous work in which EHRs were exploited to make predictions about patients with respiratory diseases. In this study, we will try to predict the recurrence of patients with respiratory diseases using four different machine learning algorithms. Full article

Several studies have shown that video games help to motivate users in different kinds of therapies. Therefore, in this work we developed a tool that includes dual activities for therapy, as well as a data system for the specialist to follow the evolution of the user. The aim of dual activities is to train cognitive and aerobic capacities at the same time. The interaction between the user and the game is made through two Micro:Bits. Once the user finishes the game, the therapist can follow the evolution of the user through some parameters included in the activities. Full article

The analysis of the retinal vasculature represents a crucial stage in the diagnosis of several diseases. An exhaustive analysis involves segmenting the retinal vessels and classifying them into veins and arteries. In this work, we present an accurate approach, based on deep neural networks, for the joint segmentation and classification of the retinal veins and arteries from color fundus images. The presented approach decomposes this joint task into three related subtasks: the segmentation of arteries, veins and the whole vascular tree. The experiments performed show that our method achieves competitive results in the discrimination of arteries and veins, while clearly enhancing the segmentation of the different structures. Moreover, unlike other approaches, our method allows for the straightforward detection of vessel crossings, and preserves the continuity of the arterial and venous vascular trees at these locations. Full article

Depression is one of the most prevalent mental health diseases. Although there are effective treatments, the main problem relies on providing early and effective risk detection. Medical experts use self-reporting questionnaires to elaborate their diagnosis, but these questionnaires have some limitations. Social stigmas and the lack of awareness often negatively affect the success of these self-report questionnaires. This article aims to describe techniques to automatically estimate the depression severity from users on social media. We explored the use of pre-trained language models over the subject’s writings. We addressed the task “Measuring the Severity of the Signs of Depression” of eRisk 2020, an initiative in the CLEF Conference. In this task, participants have to fill the Beck Depression Questionnaire (BDI-II). Our proposal explores the application of pre-trained Multiple-Choice Question Answering (MCQA) models to predict user’s answers to the BDI-II questionnaire using their posts on social media. These MCQA models are built over the BERT (Bidirectional Encoder Representations from Transformers) architecture. Our results showed that multiple-choice question answering models could be a suitable alternative for estimating the depression degree, even when small amounts of training data are available (20 users). Full article

Advances achieved in recent decades regarding cardiac surgery have revealed a new risk that goes beyond surgeons’ dexterity; post-operative hours are crucial in these patients and are usually spent at intensive care units (ICUs), where they need to be continuously monitored to adjust the treatments. Clinical decision support systems (CDSS) have been developed to take this real-time information and provide clinical suggestions to physicians, so as to reduce medical errors and increase patient recovery ratio. In this review, an initial total of 666 papers were considered, finishing with 23 of them after the researchers’ filter, which included the deletion of duplications and exclusion if the title and abstract were not of real interest. The review of these papers concludes the applicability and extends the CDSS offer to both doctors and patients. Better prognosis and recovery rate are achieved by using this technology, which also has high acceptance among most physicians. However, despite the evidence that well-designed CDSS are effective, they still need to be refined to offer the best assistance as possible, which may still take time, despite the promising models that have already been applied in real ICUs. Full article

(1) Background: Stress is a major public health problem due to its relevant health, social and economic repercussions. Moreover, stress can be associated with work; when stress increases over time, burnout can occur, an occupational phenomenon recognized by the WHO in 2019. There is interest in the use of wearable devices to monitor and control stressors and their influence on the condition of workers. This study aims to identify the level of job stress and its influence on the quality of life of workers. (2) Methods:This longitudinal study was carried out between the end of May and mid-July 2021. Three assessment tools along with a daily and a weekly questionnaire were computerized through the RedCap platform. The participants had to fill out the diary and weekly questionnaires and wear a Xiaomi Mi Band 5 during the project. (3) Results and discussion: Thirty-six workers from the University of Coruña and from the University of Porto participated in the project. This study promotes the awareness of workers regarding their work stress and the influence of this factor on their quality of life using physiological (e.g., activity, sleep, and heart rate) and psychological indicators (self-report questionnaires in different moments). Full article

Memory management is one of the main tasks of an Operating System, where the data of each process running in the system is kept. In this context, there exist several types of attacks that exploit memory-related vulnerabilities, forcing Operating Systems to feature memory protection techniques that make difficult to exploit them. One of these techniques is ASLR, whose function is to introduce randomness into the virtual address space of a process. The goal of this work was to measure, analyze and compare the behavior of ASLR on the 64-bit versions of Windows 10 and Ubuntu 18.04 LTS. The results have shown that the implementation of ASLR has improved significantly on these two Operating Systems compared to previous versions. However, there are aspects, such as partial correlations or a frequency distribution that is not always uniform, so it can still be improved. Full article

Requirements prioritization (RP), part of Requirements engineering (RE), is an essential activity of Software Product-Lines (SPL) paradigm. Similar to standard systems, the identification and prioritization of the user needs are relevant to the software quality and challenging in SPL due to common requirements, increasing dependencies, and diversity of stakeholders involved. As prioritization process might become impractical when the number of derived products grows, recently there has been an exponential growth in the use of Artificial Intelligence (AI) techniques in different areas of RE. The present research aims to propose a semi-automatic multiple-criteria prioritization process for functional and non-functional requirements (FR/NFR) of software projects developed within the SPL paradigm for reducing stakeholder participation. Full article

This work proposes a resampling technique to approximate the smoothing parameter of Beran’s estimator. It is based on resampling by the smoothed bootstrap and minimising the bootstrap approximation of the mean integrated squared error to find the bootstrap bandwidth. The behaviour of this method has been tested by simulation on several models. Bootstrap confidence intervals are also addressed in this research and their performance is analysed in the simulation study. Full article

Geographic Information Systems (GIS) have spread all over our technological environment in the last decade. The inclusion of GPS technologies in everyday portable devices along with the creation of massive shareable geographical data banks has boosted the rise of geoinformatics. Despite the technological maturity of this field, there are still relevant research challenges concerning efficient information storage and representation. One of the most powerful techniques to tackle these issues is designing new Succinct Data Structures (SDS). These structures are defined by three main characteristics: they use a compact representation of the data, they have self-index properties and, as a consequence, they do not need decompression to process the enclosed information. Thus, SDS are not only capable of storing geographical data using as little space as possible, but they can also solve queries efficiently without any previous decompression. This work introduces how SDS can be successfully applied in the GIS context through several novel approaches and practical use cases. Full article

This work investigates the hydrodynamics of jet wiping, a coating process in which a thin slot gas jet impinges on a coating film dragged by a moving strip; thus, reducing the coating thickness and developing a run-back flow. The interaction between the liquid film and the gas jet is highly unsteady, producing long-wavelength defects on the final product known as undulations. We perform Computational Fluid Dynamics (CFD) simulations of the process using High-Performance Computing (HPC) resources. A multi-scale modal analysis is then applied to decrypt the mechanism of wave formation. The main undulation pattern features two-dimensional waves and is correlated with a large-scale motion of the gas jet. Full article

The main objective of this work is the development of a Computational Fluid Dynamics model coupled with a structural code for the simulation and optimization of fishing gears. As fishing nets are highly deformable structures under the influence of incident water, the use of merely empirical correlations for hydrodynamic forces, such as those used in many structural codes, does not provide precise predictions for their behaviour. The coupling between the structural problem and the hydrodynamic effects makes it necessary to tackle the problem through a new “fluid–structure interaction” approach, which is described here. Preliminary results obtained with the CFD model are also presented. Full article

The number of applications using unmanned aerial vehicles (UAVs) is increasing. The use of UAVs in swarms makes many operators see more advantages than the individual use of UAVs, thus reducing operational time and costs. The main objective of this work is to design a system that, using Reinforcement Learning (RL) and Artificial Neural Networks (ANNs) techniques, can obtain a good path for each UAV in the swarm and distribute the flight environment in such a way that the combination of the captured images is as simple as possible. To determine whether it is better to use a global ANN or multiple local ANNs, experiments have been done over the same map and with different numbers of UAVs at different altitudes. The results are measured based on the time taken to find a solution. The results show that the system works with any number of UAVs if the map is correctly partitioned. On the other hand, using local ANNs seems to be the option that can find solutions faster, ensuring better trajectories than using a single global network. There is no need to use additional map information other than the current state of the environment, like targets or distance maps. Full article

We present an architecture for the efficient storing and querying of large RDF datasets. Our approach seeks to store RDF datasets in very little space while offering complete SPARQL functionality. To achieve this, our proposal was built over HDT, an RDF serialization framework, and its interaction with the Jena query engine. We propose a set of modifications to this framework in order to incorporate a range of space-efficient compact data structures for data storage and access, while using high-level capabilities to answer more complicated SPARQL queries. As a result, our approach provides a standard mechanism for using low-level data structures in complicated query situations requiring SPARQL searches, which are typically not supported by current solutions. Full article

Due to the continuous development in the field of Next Generation Sequencing (NGS) technologies that have allowed researchers to take advantage of greater genetic samples in less time, it is a matter of relevance to improve the existing algorithms aimed at the enhancement of the quality of those generated reads. In this work, we present a Big Data tool implemented upon the open-source Apache Spark framework that is able to execute validated error-correction algorithms at an improved performance. The experimental evaluation conducted on a multi-core cluster has shown significant improvements in execution times, providing a maximum speedup of 9.5 over existing error correction tools when processing an NGS dataset with 25 million reads. Full article

Due to the recent outbreak of the COVID-19 pandemic, everybody’s lives have changed dramatically and society went through new stages accompanied by new needs. This article details a solution for one of these needs, since it provides a system of digital certificates for the secure storage and sharing of medical data related to COVID-19, with the goal of demonstrating immunity or lack of viral infection in a unequivocal, unbreakable and secure way. The proposed system is based on Blockchain and its inherent benefits, developing also a decentralized, mobile and multiplatform app for its use and an incentive system with a customized cryptocurrency in order to reward the users who use it. Full article

We considered a primal-mixed method for the Darcy–Forchheimer boundary value problem. This model arises in fluid mechanics through porous media at high velocities. We developed an a posteriori error analysis of residual type and derived a simple a posteriori error indicator. We proved that this indicator is reliable and locally efficient. We show a numerical experiment that confirms the theoretical results. Full article

Knowing the chemical composition of a substance provides valuable information about it. That is why numerous techniques have been developed to try to obtain it. One of them is the Near Infrared Spectrometry technique, a non-destructive technique that analyzes the electromagnetic spectrum in search of waves of a certain length. The aim of this project is to combine this technology with machine learning techniques to try to detect the presence of milk, as well as the level of cocoa present in an ounce of chocolate. This has given satisfactory results in both cases, so it is considered that the combination of these techniques offers great possibilities. Full article

Data processing and the use of machine learning techniques make it possible to solve a wide variety of problems. The great disadvantage of using this type of technology is the enormous amount of computation involved. This is why we have tried to develop an architecture that makes the best possible use of the resources available on each machine. The growth of cloud computing and the rise of virtualization techniques have led to a development that allows these tasks to be carried out in a more optimized way. Full article

At present, the great majority of Artificial Intelligence (AI) systems require the participation of humans in their development, tuning, and maintenance. Particularly, Machine Learning (ML) systems could greatly benefit from their expertise or knowledge. Thus, there is an increasing interest around how humans interact with those systems to obtain the best performance for both the AI system and the humans involved. Several approaches have been studied and proposed in the literature that can be gathered under the umbrella term of Human-in-the-Loop Machine Learning. The application of those techniques to the health informatics environment could provide a great value on prognosis and diagnosis tasks contributing to develop a better health service for Cancer related diseases. Full article

The aim of this work is to search for evidence of close binary stars associated with planetary nebulae (ionized stellar envelopes in expansion) by mining the astronomical archive of Gaia EDR3. For this task, using big data techniques, we selected a sample of central stars of planetary nebulae from almost 2000 million sources in an EDR3 database. Then, we analysed some of their parameters, which could provide clues about the presence of close binary systems, and we ran a statistical test to verify the results. Using this method, we concluded that red stars tend to show more affinity with close binarity than blue ones. Full article

Following the guidelines of the Basel III agreement (2013), large financial institutions are forced to incorporate additional collateral, known as Initial Margin, in their transactions in OTC markets. Currently, the computation of such collateral is performed following the Standard Initial Margin Model (SIMM) methodology. Focusing on a portfolio consisting of an interest rate swap, we propose the use of Artificial Neural Networks (ANN) to approximate the Initial Margin value of the portfolio over its lifetime. The goal is to find an optimal configuration of structural hyperparameters, as well as to analyze the robustness of the network to variations in the model parameters and swap features. Full article

In this work we study a routing and scheduling problem for a home care business. The problem is composed of two conflicting objectives, therefore we study it as a bi-objective one. We obtain the Pareto frontier for small size instances using the AUGMECON2 method and, for bigger cases, we developed an heuristic algorithm. We also obtained some preliminary results that show the algorithm has good behaviour. Full article

Virtual reality allows to generate an environment of great realism, while achieving the immersion of the user in it. The purpose of this project is to use this technology as a complementary tool in the rehabilitation of people with functional diversity. To do this, an application is being developed that will offer different environments that simulate situations in everyday life. Through its initial menu, the professional will be able to select the virtual work environment, with different configuration options to adapt each scenario to the user’s needs. This customization of the scenarios will allow such things as configuring the degree of difficulty of the activity to eventually adapting the elements of the scenario to the functional capacity of the user. Full article

Methylation is a chemical process that modifies DNA through the addition of a methyl group to one or several nucleotides. Discovering differentially methylated regions is an important research field in genomics, as it can help to anticipate the risk of suffering from certain diseases. RADMeth is one of the most accurate tools in this field, but it has high computational complexity. In this work, we present a hybrid MPI-OpenMP parallel implementation of RADMeth to accelerate its execution on distributed-memory systems, reaching speedups of up to 189 when running on 256 cores and allowing for its application to large-scale datasets. Full article

We describe a general-purpose framework to implement quantum algorithms relying upon an efficient handling of arrays. The cornerstone of the framework is the direct embedding of information into quantum amplitudes, thus avoiding hampering square roots. We discuss the entire pipeline, from data loading to information extraction. Particular attention is devoted to the definition of an efficient toolkit of basic quantum operations on arrays. We comment on strong and weak points of the proposed quantum manipulations, especially in relation to an effective exploitation of quantum parallelism. We describe in detail some general-purpose routines as well as their embedding in full algorithms. Their efficiency is critically discussed both locally, at the level of the routine, and globally, at the level of the full algorithm. Finally, we comment on some applications in the quantitative finance domain. Full article

The goal of this work was to develop a mobile application for Android devices, with the objective of stimulating the cognitive skills of children from 0 to 6 years old who are suffering from learning disabilities, while focusing on the most common learning impediments such as reading and writing disorders. This application is based on games specifically designed to meet the needs of this group. For this purpose, we collaborated with professionals from an organization in the area of A Coruña who established the functional requirements of the application and carried out the validation tests. The application monitored the progress of its users, thus allowing the therapists to track them and adapt the training program to each of their individual needs. Full article

The objective of this work is the proposal of a new navigation algorithm and its integration in a platform that is already designed and built, improving the functionality of the robot in order to patrol complex indoor environments. This patrol contains various features related to the navigation and the localization of the platform using a particle filter that allows the robot to move autonomously through the environment with the data obtained from an RGB-D camera and LIDAR. The navigation algorithm is adapted dynamically in real-time using the well-known CNN real-time object detector You Only Look Once (YOLOv3), which we have retrained with our own database. The platform detects standing and fallen people. Additionally, it registers people using a specific face recognition convolutional neural network. All these functionalities are controlled and centralized in a friendly user interface that appears on the robot’s touch screen and a voice service model is also used. Full article

The past and current situation of the SARS-CoV-2 pandemic has put the entire society, and especially all hospital systems, worldwide to the test. It is essential that health system managers and decision makers optimize the management of resources, even being forced to improvise new units, divert resources usually destined to other functions and/or change the usual care modality by considerably enhancing aspects of telemedicine. Artificial Intelligence (AI) techniques and procedures are of great help in decision making in emergency environments due to severe pandemics because of their predictive capacity. This paper presents the PRACTICUM DIRECT project, which proposes the design and implementation of a tool to assist health system managers in making decisions on the early management of hospital resources. It makes use of AI techniques to identify the most critical variables in each case and build models capable of showing the possibilities and consequences of the decisions taken on resources at each moment of the emergency. It includes a simulator that shows how they would affect management. The current status is that of the selection of the most appropriate variables, taking into account those affected during the SARS-CoV-2 pandemic: infectious diseases, cardio-neuro-circulatory diseases, metabolic diseases and rehabilitative medicine. Full article

This paper focuses on long-term education in Artificial Intelligence (AI) applied to robotics. Specifically, it presents the Robobo SmartCity educational framework. It is based on two main elements: the smartphone-based robot Robobo and a real model of a smart city. We describe the development of a simulation model of Robobo SmartCity in the CoppeliaSim 3D simulator, implementing both the real mock-up and the model of Robobo. In addition, a set of Python libraries that allow teachers and students to use state-of-the-art algorithms in their education projects is described too. Full article

Nowadays, the use of wearable devices is still emerging. Monitoring with wearable sensors is an easy and non-intrusive approach to encourage preventive care for older adults. Wearable devices are becoming an assessment tool for evaluating physical activity and sleep, among other biomedical parameters. The objective of the present study is to explore the daily activity and sleep of older adults from three nursing homes, as measured by Xiaomi Mi Band 2. The results showed that people with a greater number of steps (representing daily activity) could be related to a lower probability of risk of falling, dependency on basic activities of daily living, and mobility problems. Regarding sleep, the results suggest that people at risk of falling tend to be awake longer at night. Independent people get more deep sleep, while people who identify problems in their usual activities have a lower total sleep time. Finally, people who identify pain or discomfort have less light sleep and sleep in total. Full article

In the field of computer security, the possibility of knowing which specific version of an operating system is running behind a machine can be useful, to assist in a penetration test or monitor the devices connected to a specific network. One of the most widespread tools that better provides this functionality is Nmap, which follows a rule-based approach for this process. In this context, applying machine learning techniques seems to be a good option for addressing this task. The present work explores the strengths of different machine learning algorithms to perform operating system fingerprinting, using for that, the Nmap reference database. Moreover, some optimizations were applied to the method which brought the best results, random forest, obtaining an accuracy higher than 96%. Full article

In this work, we develop a mobile application which allows to create digital schedules for children with autism spectrum disorder. These schedules comprise a sorted sequence of tasks or activities which facilitates children to understand and anticipate the upcoming events, thus reducing their stress and frustration. For that, the activities are identified and described with the help of visual supports (pictograms) which can be visualized on the screen of any mobile device. The developed application also allows to gather valuable information about the performance and interests of the children from their interactions with it, helping to refine and define more appropriate routines or support therapies for the children. In this way, the aim of this work is to contribute to improve the lives of people with functional and cognitive diversity, especially children with these disorders, and also their families. Full article

Indoor positioning systems usually rely on RF-based devices that should be carried by the targets, which is non-viable in certain use cases. Recent advances in AI have increased the reliability of person detection in images, thus, enabling the use of surveillance cameras to perform person localization and tracking. This paper evaluates the performance of indoor person location using cameras and edge devices with AI accelerators. We describe the video processing performed in each edge device, including the selected AI models and the post-processing of their outputs to obtain the positions of the detected persons and allow their tracking. The person location is based on pose estimation models as they provide better results than do object detection networks in occlusion situations. Experimental results are obtained with public datasets to show the feasibility of the solution. Full article

The aim of this work is that the participants, using HoloLens 2 and Dynamics 365 Remote Assistance, can receive all the training and information necessary for the correct application of prosthesis and medical devices remotely, from a support center of the manufacturers, avoiding the displacement and presence of these technicians during surgeries. After implementing this method, an analysis will be made on its impact, avoiding displacement and the presence of technicians during surgery, in terms of increasing satisfaction and improving the experience of the participants, reduction of various risks (including the risk of infection) and on reduction of some economic and environmental costs. Full article

Life expectancy in Western countries is increasing. The fact that humans are living longer lives presents new challenges to people’s quality of life. Some of the problems that most affect older people are the problems associated with cognitive impairment. The development of a tool that helps psychologists to carry out different types of tests is the main objective of this work. To this end, an interdisciplinary group of psychologists and engineers have joined forces to create a tool that generates a series of standardised metrics to guide clinicians and help them make decisions about a patient’s cognitive impairment. Full article

During the last few years, some of the most relevant IT companies have started to develop new authentication solutions which are not vulnerable to attacks like phishing. WebAuthn and FIDO authentication standards were designed to replace or complement the de facto and ubiquitous authentication method: username and password. This paper performs an analysis of the current implementations of these standards while testing and comparing these solutions in a high-level analysis, drawing the context of the adoption of these new standards and their integration with the existing systems, from web applications and services to different use cases on desktop and server operating systems. Full article

A deep learning-based strategy for the analysis of the self-interference in single frequency networks (SFNs) for digital terrestrial television (DTT) broadcasting is considered. Several laboratory measurements were performed to create a dataset that relates the self-interference parameters and some quality metrics of the resulting received signal. The laboratory setup emulates an SFN scenario with two DTT transmitters. The strongest received signal and the relative values of attenuation and delay between the signals stand for the input parameters. The modulation error ratio (MER) of the strongest received signal, the MER of the resulting signal, and the SFN gain (SFNG) are the output parameters. This dataset is used to train four different multi-layer perceptron (MLP) models to predict accurate maps of interference and signal quality metrics. The considered models are suitable as complements for any multiple frequency network (MFN) coverage software with the capability to return the signal strength and the position data. This way, the SFN self-interference behavior can be predicted by considering only a proper description of the MFN coverage. Full article

In recent years, a quick transition towards digitization has been observed in most organizations. Along with it, certain inherent problems have appeared, such as the increase in cyber threats. Large organizations are able to adapt easily, but this does not happen with small and medium-sized companies. Currently, there are very few solutions aimed at fulfilling the needs of these small enterprises, so we have worked on a tool for them. Our tool is capable of displaying key, easy-to-interpret information related to each organization’s network assets. To achieve this, we used passive and active analysis techniques and successfully evaluated the viability of using machine learning techniques to get more meaningful information. All of the information obtained is displayed in a simple web application, which is designed to be used by managers in organizations without them needing to handle complex concepts and vocabulary. Full article

Transcription Factors (TFs) are proteins that regulate the expression of genes by binding to their promoter regions. There is great interest in understanding in which regions TFs will bind to the DNA sequence of an organism and the possible genetic implications that this entails. Occasionally, the sequence patterns (motifs) that a TF binds are not well defined. In this work, machine learning (ML) models were applied to TF binding data from ChIP-seq experiments. The objective was to detect patterns in TF binding regions that involved structural (DNAShapeR) and compositional (kmers) characteristics of the DNA sequence. After the application of random forest and Glmnet ML techniques with both internal and external validation, it was observed that two types of generated descriptors (HelT and tetramers) were significantly better than the others in terms of prediction, achieving values of more than 90%. Full article

Three robust algorithms for clustering multidimensional time series from the perspective of underlying processes are proposed. The methods are robust extensions of a fuzzy C-means model based on estimates of the quantile cross-spectral density. Robustness to the presence of anomalous elements is achieved by using the so-called metric, noise and trimmed approaches. Analyses from a wide simulation study indicate that the algorithms are substantially effective in coping with the presence of outlying series, clearly outperforming alternative procedures. The usefulness of the suggested methods is also highlighted by means of a specific application. Full article