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A Multi-Modal Assessment Framework for Comparison of Specialized Deep Learning and General-Purpose Large Language Models
Mohammad Nadeem;Shahab Saquib Sohail;Dag Øivind Madsen;Ahmed Abrahim Alzahrani;Javier Del Ser;Khan Muhammad
IEEE Transactions on Big Data
Year: 2025 | Early Access Article |
Recent years have witnessed tremendous advancements in Al tools (e.g., ChatGPT, GPT 4, and Bard), driven by the growing power, reasoning, and efficiency of Large Language Models (LLMs). LLMs have been shown to excel in tasks ranging from poem writing and coding to essay generation and puzzle solving. Despite their proficiency in general queries, specialized tasks such as metaphor understanding and...Show More
Neuromorphic Technology Insights in Spain
Xabier Iturbe;Luis Camuñas-Mesa;Bernabé Linares-Barranco;Teresa Serrano-Gotarredona;Mireia B. González;Francesca Campabadal;Josep Maria Margarit-Taulé;Francisco Serra-Graells;Lluís Terés;Pablo Lanillos;Víctor Lopez;Juan Bisquert;Antonio Guerrero;Marisa López-Vallejo;Pablo Ituero;Eduardo Juarez;Andrés Otero;Miguel Holgado;Antonio Rubio;Rosa Rodríguez;Rodrigo Picos;Enrique Miranda;Jordi Suñé;Jordi Sort;Juan B. Roldan;Francisco Jiménez;Salvador Dueñas;Helena Castán;Albert Cirera;Lluís Marsal;Koldo Basterretxea;Armando Astarloa;Francisco Barranco;Alejandro Linares-Barranco;Ángel Jiménez;Anibal Ollero;J. R. Martinez-de-Dios;Rafael Goma;Serafim Rodrigues;Javier Del Ser;Elías Torres;Roger Espasa;Adrià Bofill-Petit;Renato Turchetta;Stijn Goossens;Germán Vergara;Tigers Jonuzi;Iñigo Artundo;Unai Sainz;David Ruiz;David Gonzalez-Arjona;Raúl Regada;Marta Pedró;Ander Aramburu;Oscar Lara-Rapp;Javier Pérez de Arrilucea;Sarah Terreri;Marta Fernández;Michel I. Ulloa;Alfonso Gabarrón
2024 IEEE 24th International Conference on Nanotechnology (NANO)
Year: 2024 | Conference Paper |
This paper provides an overview of the main research activities carried out by Spanish organizations in areas related to neuromorphic technologies, spanning physical, materials, circuitry, and architectural levels. It also discusses the potential of these technologies to create competitive advantages for the Spanish industry, and to foster new applications and business opportunities via deep-tech ...Show More
Neuromorphic Technology Insights in Spain
Xabier Iturbe;Luis Camuñas-Mesa;Bernabé Linares-Barranco;Teresa Serrano-Gotarredona;Mireia B. González;Francesca Campabadal;Josep Maria Margarit-Taulé;Francisco Serra-Graells;Lluís Terés;Pablo Lanillos;Víctor Lopez;Juan Bisquert;Antonio Guerrero;Marisa López-Vallejo;Pablo Ituero;Eduardo Juarez;Andrés Otero;Miguel Holgado;Antonio Rubio;Rosa Rodríguez;Rodrigo Picos;Enrique Miranda;Jordi Suñé;Jordi Sort;Juan B. Roldan;Francisco Jiménez;Salvador Dueñas;Helena Castán;Albert Cirera;Lluís Marsal;Koldo Basterretxea;Armando Astarloa;Francisco Barranco;Alejandro Linares-Barranco;Ángel Jiménez;Anibal Ollero;J. R. Martinez-de-Dios;Rafael Goma;Serafim Rodrigues;Javier Del Ser;Elías Torres;Roger Espasa;Adrià Bofill-Petit;Renato Turchetta;Stijn Goossens;Germán Vergara;Tigers Jonuzi;Iñigo Artundo;Unai Sainz;David Ruiz;David Gonzalez-Arjona;Raúl Regada;Marta Pedró;Ander Aramburu;Oscar Lara-Rapp;Javier Pérez de Arrilucea;Sarah Terreri;Marta Fernández;Michel I. Ulloa;Alfonso Gabarrón
Year: 2024 | Conference Paper |
Advancing towards Safe Reinforcement Learning over Sparse Environments with Out-of-Distribution Observations: Detection and Adaptation Strategies
2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
Safety in AI-based systems is among the highest research priorities, particularly when such systems are deployed in real-world scenarios subject to uncertainties and unpredictable inputs. Among them, the presence of long-tailed stimuli (Out-of-Distribution data, OoD) has captured much interest in recent times, giving rise to many proposals over the years to detect unfamiliar inputs to the model an...Show More
Defense Strategy against Byzantine Attacks in Federated Machine Learning: Developments towards Explainability
Nuria Rodríguez-Barroso;Javier Del Ser;M. Victoria Luzón;Francisco Herrera
2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Year: 2024 | Conference Paper |
The rise of high-risk AI systems has led to escalating concerns, prompting regulatory efforts such as the recently approved EU AI Act. In this context, the development of responsible AI systems is crucial. To this end, trustworthy AI techniques aim at requirements (including transparency, privacy awareness and fairness) that contribute to the development of responsible, robust and safe AI systems....Show More
On the Design and Performance of a Novel Metaheuristic Solver for the Extended Colored Traveling Salesman Problem
Branko Miloradović;Eneko Osaba;Javier Del Ser;Vuk Vujović;Alessandro V. Papadopoulos
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Intelligent transportation systems face various challenges, including traffic congestion, environmental pollution, and inefficient transportation management. Optimizing routes and schedules for efficient delivery of goods and services can mitigate the aforementioned problems. Many transportation and routing problems can be modeled as variants of the Traveling Salesmen Problem (TSP) depending on th...Show More
Efficient Platooning for Urban Last Mile Logistics Using Multi-Objective Optimization: Performance Comparison and Load Strategies
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Urban logistics face significant challenges due to increasing traffic congestion, environmental concerns, and the growing demand for efficient goods transportation within cities. Platooning, a concept by which multiple vehicles travel in close proximity to each other, has emerged as a promising solution to effectively tackle some of the major challenges of urban last mile distribution. However, th...Show More
Enhanced Generalization Through Prioritization and Diversity in Self-Imitation Reinforcement Learning Over Procedural Environments with Sparse Rewards
2023 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2023 | Conference Paper |
Exploration poses a fundamental challenge in Reinforcement Learning (RL) with sparse rewards, limiting an agent's ability to learn optimal decision-making due to a lack of informative feedback signals. Self-Imitation Learning (self-IL) has emerged as a promising approach for exploration, leveraging a replay buffer to store and reproduce successful behaviors. However, traditional self-IL methods, w...Show More
A Graph-Based Methodology for the Sensorless Estimation of Road Traffic Profiles
Eric L. Manibardo;Ibai Laña;Esther Villar-Rodriguez;Javier Del Ser
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 traffic monitoring. One of the most common data g...Show More
Communication Technologies for Edge Learning and Inference: A Novel Framework, Open Issues, and Perspectives
Khan Muhammad;Javier Del Ser;Naercio Magaia;Ramon Fonseca;Tanveer Hussain;Amir H. Gandomi;Mahmoud Daneshmand;Victor Hugo C de Albuquerque
With the continuous advancement of smart devices and their demand for data, the complex computation that was previously exclusive to the cloud server is now moving toward the edge of the network. For numerous reasons (e.g., applications demanding low latencies and data privacy), data-based computation has been brought closer to the originating source, forging the edge computing paradigm. Together ...Show More
Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels
Ming Li;Yingying Fang;Zeyu Tang;Chibudom Onuorah;Jun Xia;Javier Del Ser;Simon Walsh;Guang Yang
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 have been established quickly, the scarcity of both i...Show More
Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
Khan Muhammad;Tanveer Hussain;Hayat Ullah;Javier Del Ser;Mahdi Rezaei;Neeraj Kumar;Mohammad Hijji;Paolo Bellavista;Victor Hugo C. de Albuquerque
Scene understanding plays a crucial role in autonomous driving by utilizing sensory data for contextual information extraction and decision making. Beyond modeling advances, the enabler for vehicles to become aware of their surroundings is the availability of visual sensory data, which expand the vehicular perception and realizes vehicular contextual awareness in real-world environments. Research ...Show More
Can Post-hoc Explanations Effectively Detect Out-of-Distribution Samples?
2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Year: 2022 | Conference Paper |
Cited by: Papers (4)
Today there is consensus around the importance of explainability as a mandatory feature in practical deployments of Artificial Intelligence (AI) models. Most research activity reported so far in the eXplainable AI (XAI) research arena has stressed on proposing new techniques for eliciting such explanations, together with different approaches for measuring their effectivity to increase the trustwor...Show More
Group’n Route: An Edge Learning-Based Clustering and Efficient Routing Scheme Leveraging Social Strength for the Internet of Vehicles
Naercio Magaia;Pedro Ferreira;Paulo Rogério Pereira;Khan Muhammad;Javier Del Ser;Victor Hugo C. de Albuquerque
The Internet of Vehicles (IoV) is undoubtedly at the core of the future of intelligent transportation. It will prevail over the road ecosystem, and it will have a huge impact on our lives throughout the provision of seamless connectivity among diverse transportation means. For the network to operate efficiently, the data needs to be quickly spread throughout the network, which requires low computa...Show More
Deep Learning for Road Traffic Forecasting: Does it Make a Difference?
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 of Deep Learning to deliver good results has gene...Show More
Human Short Long-Term Cognitive Memory Mechanism for Visual Monitoring in IoT-Assisted Smart Cities
Shuai Wang;Xinyu Liu;Shuai Liu;Khan Muhammad;Ali Asghar Heidari;Javier Del Ser;Victor Hugo C. de Albuquerque
IEEE Internet of Things Journal
Cited by: Papers (61)
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 with restricted resources remains a major challenge...Show More
Adaptive Dendritic Cell-Deep Learning Approach for Industrial Prognosis Under Changing Conditions
Alberto Diez-Olivan;Patxi Ortego;Javier Del Ser;Itziar Landa-Torres;Diego Galar;David Camacho;Basilio Sierra
Industrial prognosis refers to the prediction of failures of an industrial asset based on data collected by Internet of Things sensors. Prognostic models can experience the undesired effects of concept drift, namely, the presence of nonstationary phenomena that affects the data collected over time. Consequently, fault patterns learned from data become obsolete. To overcome this issue, contextual a...Show More
Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions
Khan Muhammad;Amin Ullah;Jaime Lloret;Javier Del Ser;Victor Hugo C. de Albuquerque
Advances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human drivers with the help of advanced artificial intelligence (AI) techniques. Recently, deep learning (DL) approaches have solved several real-world problems of complex nature. However, their strengths in terms of control proce...Show More
Multiview Summarization and Activity Recognition Meet Edge Computing in IoT Environments
Tanveer Hussain;Khan Muhammad;Amin Ullah;Javier Del Ser;Amir H. Gandomi;Muhammad Sajjad;Sung Wook Baik;Victor Hugo C. de Albuquerque
IEEE Internet of Things Journal
Cited by: Papers (31)
Multiview video summarization (MVS) has not received much attention from the research community due to inter-view correlations and views' overlapping, etc. The majority of previous MVS works are offline, relying on only summary, and require additional communication bandwidth and transmission time, with no focus on foggy environments. We propose an edge intelligence-based MVS and activity recogniti...Show More
Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey
Khan Muhammad;Salman Khan;Javier Del Ser;Victor Hugo C. de Albuquerque
Brain tumor is one of the most dangerous cancers in people of all ages, and its grade recognition is a challenging problem for radiologists in health monitoring and automated diagnosis. Recently, numerous methods based on deep learning have been presented in the literature for brain tumor classification (BTC) in order to assist radiologists for a better diagnostic analysis. In this overview, we pr...Show More
An Efficient and Scalable Simulation Model for Autonomous Vehicles With Economical Hardware
Muhammad Sajjad;Muhammad Irfan;Khan Muhammad;Javier Del Ser;Javier Sanchez-Medina;Sergey Andreev;Weiping Ding;Jong Weon Lee
Autonomous vehicles rely on sophisticated hardware and software technologies for acquiring holistic awareness of their immediate surroundings. Deep learning methods have effectively equipped modern self-driving cars with high levels of such awareness. However, their application requires high-end computational hardware, which makes utilization infeasible for the legacy vehicles that constitute most...
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First Page of the Article
Show MoreDilated LSTM Networks for Short-Term Traffic Forecasting using Network-Wide Vehicle Trajectory Data
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
Year: 2020 | Conference Paper |
Cited by: Papers (8)
Short-term traffic forecasting is anticipated as an always evolving research topic, boosted by the tremendous recent advances of Machine Learning and Deep Learning, as well as computational power of modern PCs. In this paper, the Dilated Recurrent Neural Networks are introduced in traffic forecasting. Their architecture promotes the deployment of long-term relations and prevents common issues of R...Show More
On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking
Eneko Osaba;Aritz D. Martinez;Jesus L. Lobo;Ibai Laña;Javier Del Ser
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
Year: 2020 | Conference Paper |
Cited by: Papers (4)
Multitasking optimization is a recently introduced paradigm, focused on the simultaneous solving of multiple optimization problem instances (tasks). The goal of multitasking environments is to dynamically exploit existing complementarities and synergies among tasks, helping each other through the transfer of genetic material. More concretely, Evolutionary Multitasking (EM) regards to the resolutio...Show More
Data-driven Predictive Modeling of Traffic and Air Flow for the Improved Efficiency of Tunnel Ventilation Systems
Ibai Laña;Ignacio Iñaki Olabarrieta;Javier Del Ser;Luis Rodriguez
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
Year: 2020 | Conference Paper |
Cited by: Papers (1)
Tunnel ventilation systems are strictly controlled by safety regulations. Such regulations define not only their operating conditions during fire situations, but also the way in which they should be activated when the accumulation of pollutant gases reaches certain thresholds that are considered unsafe. In addition to these exceptional circumstances, evacuation of tunnel gases is produced naturall...Show More
Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithmic Design, Performance Comparison and Genetic Transferability Analysis
Eneko Osaba;Aritz D. Martinez;Jesus L. Lobo;Javier Del Ser;Francisco Herrera
2020 IEEE Congress on Evolutionary Computation (CEC)
Year: 2020 | Conference Paper |
Cited by: Papers (10)
Multitasking optimization is an incipient research area which is lately gaining a notable research momentum. Unlike traditional optimization paradigm that focuses on solving a single task at a time, multitasking addresses how multiple optimization problems can be tackled simultaneously by performing a single search process. The main objective to achieve this goal efficiently is to exploit synergie...Show More
Taming the Latency in Multi-User VR 360°: A QoE-Aware Deep Learning-Aided Multicast Framework
Cristina Perfecto;Mohammed S. Elbamby;Javier Del Ser;Mehdi Bennis
Immersive virtual reality (VR) applications require ultra-high data rate and low-latency for smooth operation. Hence in this paper, aiming to improve VR experience in multi-user VR wireless video streaming, a deep-learning aided scheme for maximizing the quality of the delivered video chunks with low-latency is proposed. Therein the correlations in the predicted field of view (FoV) and locations o...Show More
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