@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{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{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{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{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}

}