Dr. David Camacho

@inproceedings{santamaria2023exploring,

title = {Exploring Multiple Classification Systems for Online Time Series Anomaly Detection},

author = {Inmaculada Santamaria-Valenzuela and Victor Rodriguez-Fernandez and David Camacho},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {2023 International Conference on Network, Multimedia and Information Technology (NMITCON)},

pages = {1–6},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stevenson2022_starcon2,

title = {A deep learning-based framework for operational all-vs-all conjunction screening},

author = {Emma Stevenson and Víctor Rodríguez-Fernández and Christophe Taillan and Hodei Urrutxua and David Camacho},

year  = {2022},

date = {2022-11-07},

booktitle = {2nd International Stardust Conference (STARCON-2)},

address = {ESTEC, the Netherlands},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{9870215,

title = {Predicting the effects of kinetic impactors on asteroid deflection using end-to-end deep learning},

author = {Emma Stevenson and Riansares Martinez and Victor Rodriguez-Fernandez and David Camacho},

doi = {10.1109/CEC55065.2022.9870215},

year  = {2022},

date = {2022-07-18},

urldate = {2022-07-18},

booktitle = {2022 IEEE Congress on Evolutionary Computation (CEC)},

pages = {1-8},

address = {Padua, Italy},

abstract = {One possible approach to deflect the trajectory of an asteroid on a collision course with the Earth, and prevent a potentially devastating impact, is the use of a kinetic impactor. The upcoming NASA DART and ESA Hera space missions will be the first to study and demonstrate this technique, by driving a spacecraft into the moon of a binary asteroid system with the aim of altering its momentum, and knocking it off course. In this work, we seek to predict critical parameters associated with such an impact, namely the momentum transfer efficiency and axial ratio of the target body, based on light curve data observed from ground before and after the impact in order to give insights into the real effect of the deflection effort. We present here our approach to this problem, which we address from a purely data-driven perspective based on simulated data provided as a part of the Andrea Milani Planetary Defence Challenge, organised by the EU H2020 Stardust-R research network in conjunction with ESA. Formulating the problem as a time series regression task, we develop an end-to-end deep learning pipeline in which we apply the latest advances in deep learning for time series, such as the use of the Transformer architecture as well as ensembling and self-supervised learning techniques. Exploiting these techniques for the challenge, we achieved second place out of the student teams, and fifth place overall without relying on any a priori knowledge of the physics of the asteroid system.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stevenson2022_kepassa,

title = {Deep learning for all-vs-all conjunction detection},

author = {Emma Stevenson and Victor Rodriguez-Fernandez and Hodei Urrutxua and David Camacho},

year  = {2022},

date = {2022-06-01},

urldate = {2022-06-01},

booktitle = {5th Workshop on Key Topics in Orbit Propagation Applied to Space Situational Awareness (KePASSA)},

address = {Logroño, Spain},

abstract = {This paper explores the use of different deep learning techniques for detecting conjunction events in an efficient and accurate way for improved space situational awareness. Framing the problem as a machine learning classification task, we present the performance of different data representations and model architectures on a realistic all-vs-all dataset generated using the CNES BAS3E space surveillance simulation framework, and compare the approaches to operationally used classical filters in screening performance and computational efficiency. Finally, we also investigate a novel methodology for improving the performance and generalisation ability of the models using a pre-trained orbit model, ORBERT, based on self-supervised learning techniques.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stevenson2022_icssa,

title = {An Intelligent System for Robust Decision-Making in the All-vs-All Conjunction Screening Problem},

author = {Luis Sánchez Fernandez-Mellado and Emma Stevenson and Rodriguez-Fernandez and Massimiliano Vasile and David Camacho},

year  = {2022},

date = {2022-04-01},

urldate = {2022-04-01},

booktitle = {3rd IAA Conference on Space Situational Awareness (ICSSA)},

address = {Madrid, Spain},

abstract = {The progressive increase of traffic in space demands new approaches for supporting automatic and robust operational decisions. CASSANDRA, Computational Agent for Space Situational Awareness aNd Debris Remediation Automation, is an intelligent system for Space Environment Management (SEM) intended to assist operators with the management of space traffic by providing robust decision-making support. This paper will present the automatic conjunction screening and collision avoidance manoeuvre pipeline within CASSANDRA, connecting the some of CASSANDRA's modules: Automated Conjunction Screening (ACS), Robust State Estimation (RSE), Intelligent Decision Support System (IDSS) and Collision Avoidance Manoeuvres (CAM). The pipelines allows to screen the catalogue to detect potential conjunctions, perform a detailed analysis of the encounter accounting for uncertainty (aleatory and epistemic) and new observations, provide robust decisions based on the available information and, if necessary, proposed robust optimal CAMs and analyse the impact of the new orbit on the background population. This paper will present the pipeline described above along with an example that illustrates how CASSANDRA can be used to generate robust decisions on the execution of CAMs in an automated way.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stevenson2021_iaass,

title = {Self-supervised machine learning based approach to orbit modelling applied to space traffic management},

author = {Emma Stevenson and Victor Rodriguez-Fernandez and Hodei Urrutxua and Vincent Morand and David Camacho},

year  = {2021},

date = {2021-10-01},

booktitle = {11th International Association for the Advancement of Space Safety Conference (IAASS)},

address = {(Virtual), Osaka, Japan},

abstract = {This paper presents a novel methodology for improving the performance of machine learning based space traffic management tasks through the use of a pre-trained orbit model. Taking inspiration from BERT-like self-supervised language models in the field of natural language processing, we introduce ORBERT, and demonstrate the ability of such a model to leverage large quantities of readily available orbit data to learn meaningful representations that can be used to aid in downstream tasks. As a proof of concept of this approach we consider the task of all vs. all conjunction screening, phrased here as a machine learning time series classification task. We show that leveraging unlabelled orbit data leads to improved performance, and that the proposed approach can be particularly beneficial for tasks where the availability of labelled data is limited.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{huertas-garcia_civic-upm_2021,

title = {CIVIC-UPM at CheckThat! 2021: Integration of Transformers in Misinformation Detection and Topic Classification},

author = {Álvaro Huertas-García and Javier Huertas-Tato and Alejandro Martín and David Camacho},

url = {http://ceur-ws.org/Vol-2936/paper-41.pdf},

year  = {2021},

date = {2021-05-24},

urldate = {2021-05-24},

booktitle = {Conference and Labs of the Evaluation Forum (CLEF) Working Notes},

pages = {520--530},

abstract = {Online Social Networks (OSNs) growth enables and amplifies the quick spread of harmful, manipulative and false information that influence public opinion while sow conflict on social or political issues. Therefore, the development of tools to detect malicious actors and to identify low-credibility information and misinformation sources is a new crucial challenge in the ever-evolving field of Artificial Intelligence. The scope of this paper is to present a Natural Language Processing (NLP) approach that uses Doc2Vec and different state-of-the-art transformer-based models for the CLEF2021 Checkthat! lab Task 3. Through this approach, the results show that it is possible to achieve 41.43% macro-average F1-score in the misinformation detection (Task A) and 67.65% macro-average F1-score in the topic classification (Task B).},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stevenson2021_ecsd,

title = {Artificial Intelligence for All vs. All Conjunction Screening},

author = {Emma Stevenson and Victor Rodriguez-Fernandez and Hodei Urrutxua and Vincent Morand and David Camacho},

url = {http://oa.upm.es/67167/},

year  = {2021},

date = {2021-04-01},

booktitle = {8th European Conference on Space Debris (ECSD)},

address = {(Virtual), Darmstadt, Germany},

abstract = {This paper presents a proof of concept for the application of artificial intelligence (AI) to the problem of efficient, catalogue-wide conjunction screening. Framed as a machine learning classification task, an ensemble of tabular models were trained and deployed on a realistic all vs. all dataset, generated using the CNES BAS3E space surveillance simulation framework, and consisting of 170 million object pairs over a 7-day screening period. The approach was found to outperform classical filters such as the apogee-perigee filter and the Minimum Orbital Intersection Distance (MOID) in terms of screening capability, with the number of missed detections of the approach controlled by the operator. It was also found to be computationally efficient, thus demonstrating the capability of AI algorithms to cope and aid with the scales required for current and future operational all vs. all scenarios.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{huertas-garcia_countering_2021,

title = {Countering Misinformation Through Semantic-Aware Multilingual Models},

author = {Álvaro Huertas-García and Javier Huertas-Tato and Alejandro Martín and David Camacho},

editor = {Hujun Yin and David Camacho and Peter Tino and Richard Allmendinger and Antonio J. Tallón-Ballesteros and Ke Tang and Sung-Bae Cho and Paulo Novais and Susana Nascimento},

doi = {10.1007/978-3-030-91608-4_31},

isbn = {978-3-030-91608-4},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Intelligent Data Engineering and Automated Learning – IDEAL 2021},

pages = {312--323},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {The presence of misinformation and harmful content on social networks is an emerging problem that endangers public health. One of the most successful approaches for detecting, assessing, and providing prompt responses to this misinformation problem is Natural Language Processing (NLP) techniques based on semantic similarity. However, language constitutes one of the most significant barriers to address, denoting the need to develop multilingual tools for an effective fight against misinformation. This paper presents an approach for countering misinformation through a semantic-aware multilingual architecture. Due to the specificity of the task addressed, which involves assessing the level of similarity between a pair of texts in a multilingual scenario, we built an extension of the well-known Semantic Textual Similarity Benchmark (STSb) to 15 languages. This new dataset allows to fine-tune and evaluate multilingual models based on Transformers with a siamese network topology on monolingual and cross-lingual Semantic Textual Similarity (STS) tasks, achieving a maximum average Spearman correlation coefficient of 83.60%. We validate our proposal using the Covid-19 MLIA @ Eval Multilingual Semantic Search Task. The results reported demonstrate that semantic-aware multilingual architectures are successful at measuring the degree of similarity between pairs of texts, while broadening our understanding of the multilingual capabilities of this type of models. The results and the new multilingual STS Benchmark data presented and made publicly in this study constitute an initial step towards extending methods proposed in the literature that employ semantic similarity to combat misinformation at a multilingual level.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stevenson2020_iac,

title = {A deep learning approach to space weather proxy forecasting for orbital prediction},

author = {Emma Stevenson and Victor Rodriguez-Fernandez and Edmondo Minisci and David Camacho},

url = {http://oa.upm.es/64345/},

year  = {2020},

date = {2020-10-01},

booktitle = {71st International Astronautical Congress (IAC)},

address = {The CyberSpace Edition},

abstract = {The effect of atmospheric drag on spacecraft dynamics is considered one of the predominant sources of uncertainty in Low Earth Orbit. These effects are characterised in part by the atmospheric density, a quantity highly correlated to space weather. Current atmosphere models typically account for this through proxy indices such as the F10.7, but with variations in solar radio flux forecasts leading to significant orbit differences over just a few days, prediction of these quantities is a limiting factor in the accurate estimation of future drag conditions, and consequently orbital prediction. This has fundamental implications both in the short term, in the day-to-day management of operational spacecraft, and in the mid-to-long term, in determining satellite orbital lifetime. In this work, a novel deep residual architecture for univariate time series forecasting, N-BEATS, is employed for the prediction of the F10.7 solar proxy on the days-ahead timescales relevant to space operations. This untailored, pure deep learning approach has recently achieved state-of-the-art performance in time series forecasting competitions, outperforming well-established statistical, as well as statistical hybrid models, across a range of domains. The approach was found to be effective in single point forecasting up to 27-days ahead, and was additionally extended to produce forecast uncertainty estimates using deep ensembles. These forecasts were then compared to a persistence baseline and two operationally available forecasts: one statistical (provided by BGS, ESA), and one multi-flux neural network (by CLS, CNES). It was found that the N-BEATS model systematically outperformed the baseline and statistical approaches, and achieved an improved or similar performance to the multi-flux neural network approach despite only learning from a single variable},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{pedemonte2020exploring,

title = {Exploring multi-objective cellular genetic algorithms in community detection problems},

author = {Martín Pedemonte and Ángel Panizo-LLedot and Gema Bello-Orgaz and David Camacho},

doi = {10.1007/978-3-030-62365-4_22},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

booktitle = {International Conference on Intelligent Data Engineering and Automated Learning},

pages = {223--235},

organization = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{panizo2019describing,

title = {Describing alt-right communities and their discourse on twitter during the 2018 us mid-term elections},

author = {Ángel Panizo-LLedot and Javier Torregrosa and Gema Bello-Orgaz and Joshua Thorburn and David Camacho},

doi = {10.1007/978-3-030-36683-4_35},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

booktitle = {International conference on complex networks and their applications},

pages = {427--439},

organization = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{hernandez2019ensemble,

title = {An ensemble algorithm based on deep learning for tuberculosis classification},

author = {Alfonso Hernández and Ángel Panizo-LLedot and David Camacho},

doi = {10.1007/978-3-030-33607-3_17},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

booktitle = {International conference on intelligent data engineering and automated learning},

pages = {145--154},

organization = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{osaba2019combining,

title = {Combining bio-inspired meta-heuristics and novelty search for community detection over evolving graph streams},

author = {Eneko Osaba and Javier Del Ser and Angel Panizo-LLedot and David Camacho and Akemi Galvez and Andres Iglesias},

doi = {10.1145/3319619.3326831},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

booktitle = {Proceedings of the genetic and evolutionary computation conference companion},

pages = {1329--1335},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rodriguez-fernandez_inteligencia_2018,

title = {Inteligencia Artificial aplicada al análisis de entrenamiento en operaciones con UAVs},

author = {Victor Rodriguez-Fernández and Antonio González-Pardo and David Camacho},

isbn = {978-84-9091-357-4},

year  = {2018},

date = {2018-09-01},

urldate = {2018-09-01},

booktitle = {Actas V Congreso Nacional de i+d en Defensa y Seguridad},

pages = {1491--1498},

publisher = {Ministerio de Defensa. Secretaría General Técnica.},

address = {Toledo, España},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{panizo2018artificial,

title = {An Artificial Bee Colony algorithm for optimizing the design of sensor networks},

author = {Ángel Panizo-LLedot and Gema Bello-Orgaz and Mercedes Carnero and José Hernández and Mabel Sánchez and David Camacho},

doi = {10.1007/978-3-030-03496-2_35},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

booktitle = {International Conference on Intelligent Data Engineering and Automated Learning},

pages = {316--324},

organization = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{panizo2018algoritmo,

title = {Un Algoritmo Memético, con búsqueda local basada en Label Propagation, para detectar comunidades en redes dinámicas},

author = {Ángel Panizo-LLedot and Gema Bello-Orgaz and Alfonso Ortega and David Camacho},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

booktitle = {XVIII Conferencia de la Asociación Espa~nola para la Inteligencia Artificial (CAEPIA 2018): avances en Inteligencia Artificial. 23-26 de octubre de 2018 Granada, Espa~na},

pages = {995--1000},

organization = {Asociación Espa~nola para la Inteligencia Artificial (AEPIA)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{panizo2018genetic,

title = {A genetic algorithm with local search based on label propagation for detecting dynamic communities},

author = {Ángel Panizo-LLedot and Gema Bello-Orgaz and David Camacho},

doi = {10.1007/978-3-319-99626-4_28},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

booktitle = {International symposium on intelligent and distributed computing},

pages = {319--328},

organization = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{panizo2018community,

title = {Community finding in dynamic networks using a genetic algorithm improved via a hybrid immigrants scheme},

author = {Ángel Panizo-LLedot and Gema Bello-Orgaz and Alfonso Ortega and David Camacho},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

booktitle = {Data Science and Knowledge Engineering for Sensing Decision Support: Proceedings of the 13th International FLINS Conference (FLINS 2018)},

pages = {591--598},

organization = {World Scientific},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{vasile_artificial_2018,

title = {Artificial intelligence in support to space traffic management},

author = {Massimiliano Vasile and Victor Rodriguez-Fernandez and Romain Serra and David Camacho and Annalisa Riccardi},

isbn = {978-1-5108-5537-3},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

booktitle = {Proceedings of the International Astronautical Congress, IAC},

volume = {1},

pages = {3843--3856},

publisher = {International Astronautical Federation},

address = {Adelaide, Australia},

abstract = {This paper presents an Artificial Intelligence-based decision support system to assist ground operators to plan and implement collision avoidance manoeuvres. When a new conjunction is expected, the system provides the operator with an optimal manoeuvre and an analysis of the possible outcomes. Machine learning techniques are combined with uncertainty quantification and orbital mechanics calculations to support an optimal and reliable management of space traffic. A dataset of collision avoidance manoeuvres has been created by simulating a range of scenarios in which optimal manoeuvres (in the sense of optimal control) are applied to reduce the collision probability between pairs of objects. The consequences of the execution of a manoeuvre are evaluated to assess its benefits against its cost. Consequences are quantified in terms of the need for additional manoeuvres to avoid subsequent collisions. By using this dataset, we train predictive models that forecast the risk of avoiding new collisions, and use them to recommend alternative manoeuvres that may be globally better for the space environment.},

note = {Publisher: International Astronautical Federation (IAF)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/cec/Ramirez-Atencia17,

title = {New Artificial Intelligence approaches for future UAV Ground Control Stations},

author = {Cristian Ramírez-Atencia and Víctor Rodríguez-Fernández and Antonio Gonzalez-Pardo and David Camacho},

url = {https://doi.org/10.1109/CEC.2017.7969645},

doi = {10.1109/CEC.2017.7969645},

isbn = {978-1-5090-4601-0},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

booktitle = {2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia, San Sebastián, Spain, June 5-8, 2017},

pages = {2775--2782},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{martin2017evolving,

title = {Evolving deep neural networks architectures for Android malware classification},

author = {Alejandro Martín and Félix Fuentes-Hurtado and Valery Naranjo and David Camacho},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

booktitle = {Evolutionary Computation (CEC), 2017 IEEE Congress on},

pages = {1659--1666},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Ramirez-Atencia2017b,

title = {A Knee Point Based Evolutionary Multi-objective Optimization for Mission Planning Problems},

author = {Cristian Ramirez-Atencia and Sanaz Mostaghim and David Camacho},

url = {http://doi.acm.org/10.1145/3071178.3071319},

doi = {10.1145/3071178.3071319},

isbn = {978-1-4503-4920-8},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},

pages = {1216--1223},

publisher = {ACM},

address = {Berlin, Germany},

series = {GECCO '17},

abstract = {The current boom of Unmanned Aerial Vehicles (UAVs) is increasing the number of potential industrial and research applications. One of the most demanded topics in this area is related to the automated planning of a UAVs swarm, controlled by one or several Ground Control Stations (GCSs). In this context, there are several variables that influence the selection of the most appropriate plan, such as the makespan, the cost or the risk of the mission. This problem can be seen as a Multi-Objective Optimization Problem (MOP). On previous approaches, the problem was modelled as a Constraint Satisfaction Problem (CSP) and solved using a Multi-Objective Genetic Algorithm (MOGA), so a Pareto Optimal Frontier (POF) was obtained. The main problem with this approach is based on the large number of obtained solutions, which hinders the selection of the best solution. This paper presents a new algorithm that has been designed to obtain the most significant solutions in the POF. This approach is based on Knee Points applied to MOGA. The new algorithm has been proved in a real scenario with different number of optimization variables, the experimental results show a significant improvement of the algorithm performance.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rodriguez2016method,

title = {A Method for Building Predictive HSMMs in Interactive Environments},

author = {Víctor Rodríguez-Fernández and Antonio Gonzalez-Pardo and David Camacho},

doi = {10.1109/CEC.2016.7744187},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

booktitle = {2016 IEEE Congress on Evolutionary Computation (CEC)},

pages = {3146--3153},

publisher = {IEEE},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Ramirez-Atencia2016a,

title = {A Weighted Penalty Fitness for a Hybrid MOGA-CSP to solve Mission Planning Problems},

author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},

url = {http://aida.etsisi.upm.es/wp-content/uploads/2017/03/A-Weighted-Penalty-Fitness-for-a-Hybrid-MOGA-CSP-to-solve-Mission-Planning-Problems.pdf},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

booktitle = {XI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2016)},

pages = {305--314},

abstract = {Unmanned Aerial Vehicles (UAVs) are currently booming due to their high number of potential applications. In Mission Planning problems, several tasks must be performed by a team of UAVs, under the supervision of one or more Ground Control Stations (GCSs). In our approach, we have modelled the problem as a Constraint Satisfaction Problem (CSP), and solved it using a Multi-Objective Genetic Algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission such as the fuel consumption or the makespan. In addition, the fitness function takes a new consideration when solutions are not valid. It uses the number of constraints fulfilled for each solution as a weighted penalty function. In this way, the number of constraints fulfilled is maximized in the elitism phase of the MOGA. Results show that the approach outperforms the convergence with respect to previous results.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{LosadaSuarez2016,

title = {Desarrollo de una plataforma basada en Unity3D para la aplicación de IA en videojuegos},

author = {Óscar Manuel Losada Suárez and Víctor Rodríguez-Fernández and Cristian Ramírez-Atencia and David Camacho},

url = {http://aida.etsisi.upm.es/wp-content/uploads/2017/03/Desarrollo-de-una-plataforma-basada-en-Unity3D-para-la-aplicación-de-IA-en-videojuegos.pdf},

issn = {16130073},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

booktitle = {3rd Congreso de la Sociedad Española para las Ciencias del Videojuego (CoSECiVi 2016)},

volume = {1682},

pages = {135--146},

publisher = {CEUR Workshop},

address = {Barcelona, Spain},

abstract = {La utilización intensiva de diferentes técnicas relacionadas con la Inteligencia Artificial (IA) en el área de los videojuegos ha demostrado ser una necesidad para el campo. El uso de estas técnicas permite dotar de una mayor flexibilidad y adaptabilidad a los juegos que es muy apreciada por los jugadores. Temas como la generación procedimental de contenido, la creación de agentes que puedan jugar a un videojuego de forma competente, o de agentes cuya conducta sea indistinguible de la de un jugador humano atraen a una cantidad creciente de investigadores. El objetivo de este trabajo es la presentación de una plataforma basada en el motor Unity3D que permita de manera simple la integración y prueba de algoritmos de IA. La plataforma ofrecerá como nuevas características, adicionales a las ya disponibles en la actualidad, la utilización de un entorno 3D, el desarrollo de un juego innovador (basado en múltiples agentes), y la exploración de aspectos de juego como el análisis del terreno, la cooperación entre agentes independientes y heterogéneos, la comunicación de información entre los mismos y la formación de jerarquías.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rodriguez2016Finding,

title = {Finding behavioral patterns of UAV operators using Multichannel Hidden Markov Models},

author = {Víctor Rodríguez-Fernández and Antonio Gonzalez-Pardo and David Camacho},

url = {http://dx.doi.org/10.1109/SSCI.2016.7850101},

doi = {10.1109/SSCI.2016.7850101},

isbn = {978-1-5090-4240-1},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

booktitle = {2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, Athens, Greece, December 6-9, 2016},

pages = {1--8},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Ramirez-Atencia2016b,

title = {MOGAMR: A Multi-Objective Genetic Algorithm for Real-Time Mission Replanning},

author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},

doi = {10.1109/SSCI.2016.7850235},

isbn = {978-1-5090-4240-1, 978-1-5090-4241-8},

year  = {2016},

date = {2016-01-01},

booktitle = {2016 IEEE Symposium Series on Computational Intelligence (SSCI)},

abstract = {From the last few years the interest and repercussion on Unmanned Aerial Vehicle (UAV) technologies have been extended from pure military applications to industrial and societal applications. One of the basic tasks to any UAV problems is related to the Mission Planning. This problem is particularly complex when a set of UAVs is considered. In the field of MultiUAV Mission Planning, some approaches have been carried out in the last years. However, there are few works related to realtime Mission Replanning, which is the focus of this work. In Mission Replanning, some changes in the mission, such as the arrival of new tasks, require to update the preplanned solution as fast as possible. In this paper a Multi-Objective Genetic Algorithm for Mission Replanning (MOGAMR) is proposed to handle this problem. This approach uses a set of previous plans (or solutions), generated using an offlline planning process, in order to initialize the population of the algorithm, then acts as a complete regeneration method. In order to simulate a real-time system we have fixed a time limit of 2 minutes. This has been considered as an appropriate time for a human operator to take a decision. Using this time restriction, a set of experiments adding from 1 to 5 new tasks in the Replanning Problems has been carried out. The experiments show that the algorithm works well with this few number of new tasks during the replanning process generating a set of feasible solutions under the time restriction considered.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{martin2016studying,

title = {Studying the Influence of Static API Calls for Hiding Malware},

author = {Alejandro Martín and Héctor D Menéndez and David Camacho},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

booktitle = {Conference of the Spanish Association for Artificial Intelligence},

pages = {363--372},

organization = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{martin2016string,

title = {String-based malware detection for android environments},

author = {Alejandro Martín and Héctor D Menéndez and David Camacho},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

booktitle = {International Symposium on Intelligent and Distributed Computing},

pages = {99--108},

organization = {Springer International Publishing},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{martin2016genetic,

title = {Genetic boosting classification for malware detection},

author = {Alejandro Martín and Héctor D Menéndez and David Camacho},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

booktitle = {Evolutionary Computation (CEC), 2016 IEEE Congress on},

pages = {1030--1037},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{martin2016adroit,

title = {ADROIT: Android malware detection using meta-information},

author = {Alejandro Martín and Alejandro Calleja and Héctor D Menéndez and Juan Tapiador and David Camacho},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

booktitle = {Computational Intelligence (SSCI), 2016 IEEE Symposium Series on},

pages = {1--8},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{@inproceedings,

title = {Diseño de un Simulador de Bajo Coste para Vehículos Aéreos no Tripulados},

author = {Víctor Rodríguez-Fernández and Héctor D Menéndez and David Camacho},

url = {http://aida.etsisi.upm.es/wp-content/uploads/2015/05/RodriguezMenendezCamacho.pdf},

isbn = {978-84-697-2150-6},

year  = {2015},

date = {2015-02-04},

booktitle = {Actas del X Congreso español sobre metaheurísticas, algoritmos evolutivos y bioinspirados: MAEB},

pages = {447-454},

address = {Mérida, Cáceres},

abstract = {La utilización de vehículos aéreos no tripulados, o Unmanned Aircraft Vehicles (UAVs), se ha popularizado enormemente en los últimos tiempos. Estos nuevos sistemas, introducidos en su mayoría por Google, han ganado una importante relevancia, dado que podrían suponer una mejora destacable en la seguridad ciudadana y tienen muchísimas aplicaciones a niveles profesionales dentro de campos muy variados, como la agricultura o el envío postal de paquetes. Sin embargo, el coste a la hora de realizar pruebas en entornos reales con estos dispositivos es muy elevado, por eso se crean simuladores capaces de poner a prueba distintas estrategias a la hora de planificar misiones en estos dispositivos. Este trabajo presenta un simulador orientado a este tipo de sistemas. Este simulador pretende ser una aproximación de bajo coste y fácilmente distribuible, que ayude a simular misiones llevadas a cabo por múltiples drones. Para evaluar su eficacia, se le ha sometido a pruebas de estrés con miles de usuarios, donde ha demostrado tener una respuesta potencialmente competitiva.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ramirez2015hybrid,

title = {A Hybrid MOGA-CSP for Multi-UAV Mission Planning},

author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and María D R-Moreno and David Camacho},

url = {http://aida.etsisi.upm.es/wp-content/uploads/2015/09/ramirez-atenciaHybrid.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference},

pages = {1205--1208},

organization = {ACM},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rodriguez2015multi,

title = {A multi-UAV Mission Planning videogame-based framework for player analysis},

author = {Victor Rodriguez-Fernandez and Cristian Ramirez-Atencia and David Camacho},

url = {http://aida.etsisi.upm.es/wp-content/uploads/2015/09/07257064.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {Evolutionary Computation (CEC), 2015 IEEE Congress on},

pages = {1490--1497},

organization = {IEEE},

abstract = {The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAVs) comprises a set of locations to visit in different time windows, and the actions that the vehicle can perform based on its features, such as sensors, speed or fuel consumption. Although this problem is increasingly more supported by Artificial Intelligence systems, nowadays human factors are still critical to guarantee the success of the designed plan. Studying and analyzing how humans solve this problem is sometimes difficult due to the complexity of the problem and the lack of data available. To overcome this problem, we have developed an analysis framework for Multi-UAV Cooperative Mission Planning Problem (MCMPP) based on a videogame that gamifies the problem and allows a player to design plans for multiple UAVs intuitively. On the other hand, we have also developed a mission planner algorithm based on Constraint Satisfaction Problems (CSPs) and solved with a Multi-Objective Branch & Bound (MOBB) method which optimizes the objective variables of the problem and gets the best solutions in the Pareto Optimal Frontier (POF). To prove the environment potential, we have performed a comparative study between the plans generated by a heterogenous group of human players and the solutions obtained by this planner.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rodriguez2015design,

title = {Design and development of a lightweight multi-UAV simulator},

author = {Victor Rodriguez-Fernandez and Hector D Menendez and David Camacho},

url = {http://aida.etsisi.upm.es/wp-content/uploads/2015/09/cybconf2015.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {Cybernetics (CYBCONF), 2015 IEEE 2nd International Conference on},

pages = {255--260},

organization = {IEEE},

abstract = {UAVs have become enormously popular over the last few years. These new systems could make a remarkable improvement in public safety and have many applications in a variety of fields such as agriculture or postage of packages. In order to test UAV capacities and train UAV mission operators, several simulators are used, and the use of them usually entails high costs. 

This work presents a low-cost and easily distributable simulator, focused on simulating missions carried out by multiple UAVs and extracting data from them. To evaluate its effectiveness, it has been subjected to stress testing with thousands of virtual users, proving to have a potentially competitive response.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/iccci/Ramirez-Atencia15,

title = {Performance Evaluation of Multi-UAV Cooperative Mission Planning Models},

author = {Cristian Ramírez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},

url = {http://dx.doi.org/10.1007/978-3-319-24306-1_20 

http://aida.etsisi.upm.es/wp-content/uploads/2015/09/ramirez-atenciaPerformance.pdf},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

booktitle = {Computational Collective Intelligence - 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part II},

pages = {203--212},

crossref = {DBLP:conf/iccci/2015-2},

abstract = {The Multi-UAV Cooperative Mission Planning Problem (MCMPP) is a complex problem which can be represented with a lower or higher level of complexity. In this paper we present a MCMPP which is modelled as a Constraint Satisfaction Problem (CSP) with 5 increasing levels of complexity. Each level adds additional variables and constraints to the problem. Using previous models, we solve the problem using a Branch and Bound search designed to minimize the fuel consumption and number of UAVs employed in the mission, and the results show how runtime increases as the level of complexity increases in most cases, as expected, but there are some cases where the opposite happens.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/iccci/Rodriguez-Fernandez15,

title = {User Profile Analysis for UAV Operators in a Simulation Environment},

author = {Víctor Rodríguez-Fernández and Héctor D Menéndez and David Camacho},

url = {http://aida.etsisi.upm.es/wp-content/uploads/2015/09/iccci2015.pdfhttp://dx.doi.org/10.1007/978-3-319-24069-5_32},

year  = {2015},

date = {2015-01-01},

booktitle = {Computational Collective Intelligence - 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015. Proceedings, Part I},

pages = {338--347},

crossref = {DBLP:conf/iccci/2015-1},

abstract = {Unmanned Aerial Vehicles have been a growing field of study over the last few years. The use of unmanned systems require a strong human supervision of one or many human operators, responsible for monitoring the mission status and avoiding possible incidents that might alter the execution and success of the operation. The accelerated evolution of these systems is generating a high demand of qualified operators, which requires to redesign the training process to deal with it. This work aims to present an evaluation methodology for inexperienced users. A multi-UAV simulation environment is used to carry out an experiment focused on the extraction of performance profiles, which can be used to evaluate the behavior and learning process of the users. A set of performance metrics is designed to define the profile of a user, and those profiles are discriminated using clustering algorithms. The results are analyzed to extract behavioral patterns that distinguish the users in the experiment, allowing the identification and selection of potential expert operators.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rodriguez2015summary,

title = {A Summary of Player Assessment in a Multi-UAV Mission Planning Serious Game},

author = {Victor Rodriguez-Fernández and Cristian Ramirez-Atencia and David Camacho},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

booktitle = {2nd Congreso de la Sociedad Espa~nola para las Ciencias del Videojuego (CoSeCiVi 2015)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ramirez2014solving,

title = {Solving UAV Mission Planning based on Temporal Constaint Satisfaction Problem using Genetic Algorithms},

author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},

year  = {2014},

date = {2014-09-12},

urldate = {2014-09-12},

booktitle = {Doctoral Program Proceedings of The 20th International Conference on Principles and Practice of Constraint Programming (CP 2014)},

abstract = {The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAV) consists of a set of locations to visit in different time windows, and the actions that the vehicle can perform based on its features such as the payload, speed or fuel capacity. We study how this problem can be formulated as a Temporal Constraint Satisfaction Problem (TCSP). This problem contains several constraints assuring UAVs are assigned to tasks they have enough characteristics to perform, and soft-constraints for optimizing the time and fuel spent in the process. Our goal is to implement this model and then try to solve it using Genetic Algorithms (GAs). For this purpose, we will carry out a mission simulation containing m UAVs with different sensors and characteristics located in different waypoints, and n requested tasks varying mission priorities. The GA will match the model constraints and use a multi-objective function in order to minimize the cost.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}