Javier Del Ser

Javier Del Ser
Tecnalia · ICT - European Software Institute

PhD, TECNALIA Research Professor, UPV/EHU Lecturer

About

406
Publications
95,281
Reads
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7,300
Citations
Introduction
Javier Del Ser currently works at the ICT - European Software Institute, Tecnalia. Javier does research in Algorithms, Artificial Intelligence and Computer Security and Reliability. Their current project is 'UPGRID'.
Additional affiliations
December 2017 - present
Tecnalia
Position
  • Professor
September 2015 - present
Universidad del País Vasco / Euskal Herriko Unibertsitatea
Position
  • Professor (Associate)
January 2010 - present
Tecnalia
Position
  • Leading researcher

Publications

Publications (406)
Article
Full-text available
In streaming time series classification problems, the goal is to predict the label associated to the most recently received observations over the stream according to a set of categorized reference patterns. In on-line scenarios, data arise from non-stationary processes, which results in a succession of different patterns or events. This work presen...
Preprint
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Over the last few years, convolutional neural networks (CNNs) have dominated the field of computer vision thanks to their ability to extract features and their outstanding performance in classification problems, for example in the automatic analysis of X-rays. Unfortunately, these neural networks are considered black-box algorithms, i.e. it is impo...
Preprint
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Automatic segmentation of multiple organs and tumors from 3D medical images such as magnetic resonance imaging (MRI) and computed tomography (CT) scans using deep learning methods can aid in diagnosing and treating cancer. However, organs often overlap and are complexly connected, characterized by extensive anatomical variation and low contrast. In...
Preprint
Full-text available
Rendering programs have changed the design process completely as they permit to see how the products will look before they are fabricated. However, the rendering process is complicated and takes a significant amount of time, not only in the rendering itself but in the setting of the scene as well. Materials, lights and cameras need to be set in ord...
Article
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Since their inception, learning techniques under the reservoir computing paradigm have shown a great modeling capability for recurrent systems without the computing overheads required for other approaches, specially deep neural networks. Among them, different flavors of echo state networks have attracted many stares through time, mainly due to the...
Article
The accuracy of WiFi fingerprint-based localization is related to the number of reference points, generally, to obtain better positioning accuracy, enough samples must be collected, which will inevitably lead to a huge sampling workload. Thus, it will be of great significance to design an algorithm using sparse samples to achieve positioning accura...
Preprint
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Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other associated events are increasing in the current climate change and global warming risk. The accurate prediction, characterization, and attribution of atmospheric EEs is therefore a key research field, in which many g...
Preprint
In the last few years, the research activity around reinforcement learning tasks formulated over environments with sparse rewards has been especially notable. Among the numerous approaches proposed to deal with these hard exploration problems, intrinsic motivation mechanisms are arguably among the most studied alternatives to date. Advances reporte...
Preprint
There is a broad consensus on the importance of deep learning models in tasks involving complex data. Often, an adequate understanding of these models is required when focusing on the transparency of decisions in human-critical applications. Besides other explainability techniques, trustworthiness can be achieved by using counterfactuals, like the...
Article
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In this work, we consider multitasking in the context of solving multiple optimization problems simultaneously by conducting a single search process. The principal goal when dealing with this scenario is to dynamically exploit the existing complementarities among the problems (tasks) being optimized, helping each other through the exchange of valua...
Preprint
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Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems' persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In...
Article
Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems’ persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In...
Article
Magnetic resonance imaging (MRI) is an important non-invasive clinical tool that can produce high-resolution and reproducible images. However, a long scanning time is required for high-quality MR images, which leads to exhaustion and discomfort of patients, inducing more artefacts due to voluntary movements of the patients and involuntary physiolog...
Article
Synthetic digital twins based on medical data accelerate the acquisition, labelling and decision making procedure in digital healthcare. A core part of digital healthcare twins is modelbased data synthesis, which permits the generation of realistic medical signals without requiring to cope with the modelling complexity of anatomical and biochemical...
Preprint
Full-text available
Synthetic digital twins based on medical data accelerate the acquisition, labelling and decision making procedure in digital healthcare. A core part of digital healthcare twins is model-based data synthesis, which permits the generation of realistic medical signals without requiring to cope with the modelling complexity of anatomical and biochemica...
Article
Nowadays, optimization has become an important issue for industrial systems and product development. From an engineering perspective, optimization implies adjusting or fine tuning the design of the system considering performance factors. Unfortunately, in many real-world problems there are no mathematical techniques capable of solving them within r...
Article
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Most scenarios emerging from the Industry 4.0 paradigm rely on the concept of cyber‐physical production systems (CPPS), which allow them to synergistically connect physical to digital setups so as to integrate them over all stages of product development. Unfortunately, endowing CPPS with AI‐based functionalities poses its own challenges: although a...
Article
Full-text available
Based on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multimodal and high-performance automatic ventricle segmentation method to achieve an efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 14...
Preprint
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In the early stages of human life, babies develop their skills by exploring different scenarios motivated by their inherent satisfaction rather than by extrinsic rewards from the environment. This behavior, referred to as intrinsic motivation, has emerged as one solution to address the exploration challenge derived from reinforcement learning envir...
Preprint
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The upheaval brought by the arrival of the COVID-19 pandemic has continued to bring fresh challenges over the past two years. During this COVID-19 pandemic, there has been a need for rapid identification of infected patients and specific delineation of infection areas in computed tomography (CT) images. Although deep supervised learning methods hav...
Preprint
Full-text available
In recent years, Deep Learning models have shown a great performance in complex optimization problems. They generally require large training datasets, which is a limitation in most practical cases. Transfer learning allows importing the first layers of a pre-trained architecture and connecting them to fully-connected layers to adapt them to a new p...
Article
In the last few years, methods falling within the family of randomization-based machine learning models have grasped a great interest in the Artificial Intelligence community, mainly due to their excellent balance between performance in prediction problems and their computational efficiency. The use of these models for prediction problems related t...
Preprint
Full-text available
Based on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multi-modal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143...
Article
Full-text available
In recent years, the application of artificial intelligence has been revolutionizing the manufacturing industry, becoming one of the key pillars of what has been called Industry 4.0. In this context, we focus on the job shop scheduling problem (JSP), which aims at productions orders to be carried out, but considering the reduction of energy consump...
Article
Full-text available
In clinical medicine, magnetic resonance imaging (MRI) is one of the most important tools for diagnosis, triage, prognosis, and treatment planning. However, MRI suffers from an inherent slow data acquisition process because data is collected sequentially in k -space. In recent years, most MRI reconstruction methods proposed in the literature focus...
Article
Full-text available
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to...
Article
Memetic Algorithms and, in general, approaches underneath the wider Memetic Computing paradigm, have been at the core of a frantic research activity since the very inception of this research area in the late eighties. The community working in this area has so far showcased the benefits of hybridizing population-based algorithms with trajectory-base...
Preprint
Full-text available
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to...
Article
Full-text available
Extremism has grown as a global problem for society in recent years, especially after the apparition of movements such as jihadism. This and other extremist groups have taken advantage of different approaches, such as the use of Social Media, to spread their ideology, promote their acts and recruit followers. The extremist discourse, therefore, is...
Preprint
Traffic forecasting models rely on data that needs to be sensed, processed, and stored. This requires the deployment and maintenance of traffic sensing infrastructure, often leading to unaffordable monetary costs. The lack of sensed locations can be complemented with synthetic data simulations that further lower the economical investment needed for...
Preprint
Full-text available
Magnetic resonance imaging (MRI) is an important non-invasive clinical tool that can produce high-resolution and reproducible images. However, a long scanning time is required for high-quality MR images, which leads to exhaustion and discomfort of patients, inducing more artefacts due to voluntary movements of the patients and involuntary physiolog...
Chapter
The technological growth and progressive maturity of digital manufacturing ecosystems in industrial environments have improved the retrieval of information from many devices, hence laying a rich data substrate that allows for manifold possibilities around industrial data. Among them, information collected by sensors deployed over machines has bloss...
Chapter
Leaf wetness often emerges as the result of the exchange of atmospheric water-soluble gases between the Earth surface and the atmosphere. The importance of this feature resides in the relationship that exists between leaf wetness and various plant diseases. In order to measure this variable, there is a need for deploying physical sensors to capture...
Preprint
Full-text available
In clinical medicine, magnetic resonance imaging (MRI) is one of the most important tools for diagnosis, triage, prognosis, and treatment planning. However, MRI suffers from an inherent slow data acquisition process because data is collected sequentially in k-space. In recent years, most MRI reconstruction methods proposed in the literature focus o...
Article
Dynamic CT angiography derived from CT perfusion data can obviate a separate coronary CT angiography and the use of ionizing radiation and contrast agent, thereby enhancing patient safety. However, the image quality of dynamic CT angiography is inferior to standard CT angiography images in many studies. This paper proposes an explainable generative...
Conference Paper
The detection, location, and behavior recognition of human beings in different environments is not only a subject of a wide range of studies, but has also triggered the development of a large number of applications, including those which enhance sustainability and efficiency of infrastructures. For instance, the estimation of the occupancy could im...
Preprint
Full-text available
Transfer Optimization, understood as the exchange of information among solvers to improve their performance, has gained a remarkable attention from the Swarm and Evolutionary Computation community in the last years. This research area is young but grows at a fast pace, being at the core of a corpus of literature that expands day after day. It is un...
Conference Paper
Scenarios dealing with data streams often undergo changes in data distribution, which ultimately lead to a performance degradation of algorithms learning from such data flows (concept drift). This phenomenon calls for the adoption of adaptive learning strategies for algorithms to perform resiliently after a change occurs. A multiplicity of approach...
Article
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In the Industry 4.0 era, the visualization and real-time automatic monitoring of smart cities supported by the Internet of Things is becoming increasingly important. The use of filtering algorithms in smart city monitoring is a feasible method for this purpose. However, maintaining fast and accurate monitoring in complex surveillance environments w...
Article
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Data stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, giving rise to a phenomenon referred to as concept drift. Thus, learning models must detect and adapt to such changes, so as to exhibit a good predictive performance after...
Article
In the last few years, visual sensors are deployed almost everywhere, generating a massive amount of surveillance video data in smart cities that can be inspected intelligently to recognize anomalous events. In this work, we present an efficient and robust framework to recognize anomalies from surveillance Big Video Data (BVD) using Artificial Inte...
Article
Full-text available
Medical artificial intelligence (AI) systems have been remarkably successful, even outperforming human performance at certain tasks. There is no doubt that AI is important to improve human health in many ways and will disrupt various medical workflows in the future. Using AI to solve problems in medicine beyond the lab, in routine environments, we...
Conference Paper
The sharp growth of the global urban population entails a series of challenges for city councils towards enhancing the quality and safety of pedestrian infrastructures. Among them, an accurate identification of streets that are prone to experiencing congestion might improve the design and management of urban spaces and assets. However, the behavior...
Conference Paper
Classification models learned from data streams often assume the availability of true labels after predicting new examples, either instantly or with some delay with respect to inference time. However, in many real-world scenarios comprising sensors, actuators and robotics warms, this assumption may not realistically hold, since the supervision of n...
Chapter
The problem of learning over non-stationary ranking streams arises naturally, particularly in recommender systems. The rankings represent the preferences of a population, and the non-stationarity means that the distribution of preferences changes over time. We propose an algorithm that learns the current distribution of ranking in an online manner....
Article
Due to recent advances in the film industry, the production of movies has grown exponentially, which has led to challenges in what is referred to as discoverability: given the overwhelming number of choices, choosing which film to watch has become a tedious task for audiences. Movie summarization (MS) could help, as it presents the central theme of...
Article
Full-text available
This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles...
Article
Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a growing research topic with many competitive bio-inspired algorithms being proposed every year. In such an active area, preparing a successful proposal of a new bio-inspired algorithm is not an easy task. Given the maturity of this research field, proposing a...
Conference Paper
Nowadays, Machine Learning algorithms enjoy a great momentum in multiple engineering and scientific fields. In the context of road traffic forecasting, the number of contribu- tions resorting to these modeling techniques is increasing steadily over the last decade, in particular those based on deep neural networks. In parallel, randomization based...
Article
Deep Learning methods have been proven to be flexible to model complex phenomena. This has also been the case of Intelligent Transportation Systems, in which several areas such as vehicular perception and traffic analysis have widely embraced Deep Learning as a core modeling technology. Particularly in short-term traffic forecasting, the capability...
Article
Full-text available
Evolutionary computation has largely exhibited its potential to complement conventional learning algorithms in a variety of Machine Learning tasks, especially those related to unsupervised (clustering) and supervised learning. It has not been until lately when the computational efficiency of evolutionary solvers has been put in prospective for trai...
Article
Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously. Among the different approaches that can address this problem effectively, Evolutionary Multitasking resorts to concepts from Evolutionary Computation to solve multiple problems within a single search process. In this paper we introdu...
Article
CCTV cameras installed for continuous surveillance generate enormous amounts of data daily, forging the term Big Video Data (BVD). The active practice of BVD includes intelligent surveillance and activity recognition, among other challenging tasks. To efficiently address these tasks, the computer vision research community has provided monitoring sy...
Preprint
Full-text available
Most of state of the art methods applied on time series consist of deep learning methods that are too complex to be interpreted. This lack of interpretability is a major drawback, as several applications in the real world are critical tasks, such as the medical field or the autonomous driving field. The explainability of models applied on time seri...
Article
Full-text available
The purpose of this paper is to provide a methodological framework to identify traffic conditions based on non-calibrated video recordings captured from unmanned aerial vehicles (UAV) using deep learning. To this end, we propose two complementary to each other approaches: (i) identify in real time, with minimal computational cost, traffic condition...
Article
In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design and use of metaheuristics, large difficulties still remain in regards to the understandability, algori...