2023
Stevenson, Emma; Rodríguez-Fernández, Víctor; Urrutxua, Hodei; Camacho, David
Benchmarking deep learning approaches for all-vs-all conjunction screening Journal Article
In: Advances in Space Research, 2023.
Abstract | Links | BibTeX | Tags: Conjunction Assessment, Deep Learning, Filtering, Machine Learning, Orbits, Space, Space Debris, Space Environment Management, Space Situational Awareness, Space Traffic Management, Time Series
@article{nokey,
title = {Benchmarking deep learning approaches for all-vs-all conjunction screening},
author = {Emma Stevenson and Víctor Rodríguez-Fernández and Hodei Urrutxua and David Camacho},
doi = {https://doi.org/10.1016/j.asr.2023.01.036},
year = {2023},
date = {2023-01-23},
urldate = {2023-01-23},
journal = {Advances in Space Research},
abstract = {The all-vs-all problem, for which conjunctions are screened for over all possible sets of catalogued objects, is crucial for space traffic management and space situational awareness, but is a computational challenge owing to the vast and growing number of possible conjunction pairs. In this work, we present the application of deep learning techniques to this problem, framing conjunction screening as a machine learning classification task. We investigate the performance of different input data representations and model architectures 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. These approaches are benchmarked against operationally used classical filters in both screening capability and computational efficiency, and the ability of deep learning algorithms to cope and aid with the scales required for current and future operational all-vs-all scenarios is demonstrated.},
keywords = {Conjunction Assessment, Deep Learning, Filtering, Machine Learning, Orbits, Space, Space Debris, Space Environment Management, Space Situational Awareness, Space Traffic Management, Time Series},
pubstate = {published},
tppubtype = {article}
}
The all-vs-all problem, for which conjunctions are screened for over all possible sets of catalogued objects, is crucial for space traffic management and space situational awareness, but is a computational challenge owing to the vast and growing number of possible conjunction pairs. In this work, we present the application of deep learning techniques to this problem, framing conjunction screening as a machine learning classification task. We investigate the performance of different input data representations and model architectures 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. These approaches are benchmarked against operationally used classical filters in both screening capability and computational efficiency, and the ability of deep learning algorithms to cope and aid with the scales required for current and future operational all-vs-all scenarios is demonstrated.
2022
Stevenson, Emma; Rodríguez-Fernández, Víctor; Taillan, Christophe; Urrutxua, Hodei; Camacho, David
A deep learning-based framework for operational all-vs-all conjunction screening Proceedings Article
In: 2nd International Stardust Conference (STARCON-2), ESTEC, the Netherlands, 2022.
BibTeX | Tags: Conjunction Assessment, Deep Learning, Filtering, Machine Learning, Space Debris, Space Environment Management, Transfer Learning
@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 = {Conjunction Assessment, Deep Learning, Filtering, Machine Learning, Space Debris, Space Environment Management, Transfer Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Stevenson, Emma; Rodriguez-Fernandez, Victor; Urrutxua, Hodei
Towards graph-based machine learning for conjunction assessment Proceedings Article
In: 2022 Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS), Maui, Hawaii, USA, 2022.
Abstract | BibTeX | Tags: Conjunction Assessment, Deep Learning, Graph Neural Networks, Machine Learning, Space Debris, Space Situational Awareness
@inproceedings{stevenson2022_amos,
title = {Towards graph-based machine learning for conjunction assessment},
author = {Emma Stevenson and Victor Rodriguez-Fernandez and Hodei Urrutxua},
year = {2022},
date = {2022-09-19},
urldate = {2022-09-19},
booktitle = {2022 Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS)},
address = {Maui, Hawaii, USA},
abstract = {In the face of increasing space traffic, the deployment of large constellations, and a growing debris field, identifying potentially catastrophic collisions is an increasingly daunting and computationally challenging task. In this work, we present a novel graph-based machine learning approach for detecting conjunctions between catalogued space objects to aid in this task. Modelling conjunction events as edges between pairs of object nodes, we introduce a graphical representation of the all-vs-all scenario (so-called as it considers conjunction events between all catalogued objects, both active and debris) that is able to profit from recent advancements in Graph Neural Networks, and make a step towards efficient, machine learning based conjunction assessment. For this, we develop a methodology to predict the existence of upcoming conjunction links over a given screening period, which we frame as a graph-to-graph link prediction task, and present some initial findings that demonstrate the learning potential of the proposed approach.},
keywords = {Conjunction Assessment, Deep Learning, Graph Neural Networks, Machine Learning, Space Debris, Space Situational Awareness},
pubstate = {published},
tppubtype = {inproceedings}
}
In the face of increasing space traffic, the deployment of large constellations, and a growing debris field, identifying potentially catastrophic collisions is an increasingly daunting and computationally challenging task. In this work, we present a novel graph-based machine learning approach for detecting conjunctions between catalogued space objects to aid in this task. Modelling conjunction events as edges between pairs of object nodes, we introduce a graphical representation of the all-vs-all scenario (so-called as it considers conjunction events between all catalogued objects, both active and debris) that is able to profit from recent advancements in Graph Neural Networks, and make a step towards efficient, machine learning based conjunction assessment. For this, we develop a methodology to predict the existence of upcoming conjunction links over a given screening period, which we frame as a graph-to-graph link prediction task, and present some initial findings that demonstrate the learning potential of the proposed approach.
Huertas-García, Álvaro; Martín, Alejandro; Huertas-Tato, Javier; Camacho, David
Exploring Dimensionality Reduction Techniques in Multilingual Transformers Miscellaneous
CoRR, 2022.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Computational Intelligence, Deep Learning, Embeddings, Feature Selection, Machine Learning, Natural Language Processing, Sentence alignment, Text Analysis, Transformers
@misc{nokey,
title = {Exploring Dimensionality Reduction Techniques in Multilingual Transformers},
author = {Álvaro Huertas-García and Alejandro Martín and Javier Huertas-Tato and David Camacho},
url = {https://doi.org/10.48550/arxiv.2204.08415},
doi = {10.48550/ARXIV.2204.08415},
year = {2022},
date = {2022-04-18},
urldate = {2022-04-18},
abstract = {Both in scientific literature and in industry,, Semantic and context-aware Natural Language Processing-based solutions have been gaining importance in recent years. The possibilities and performance shown by these models when dealing with complex Language Understanding tasks is unquestionable, from conversational agents to the fight against disinformation in social networks. In addition, considerable attention is also being paid to developing multilingual models to tackle the language bottleneck. The growing need to provide more complex models implementing all these features has been accompanied by an increase in their size, without being conservative in the number of dimensions required. This paper aims to give a comprehensive account of the impact of a wide variety of dimensional reduction techniques on the performance of different state-of-the-art multilingual Siamese Transformers, including unsupervised dimensional reduction techniques such as linear and nonlinear feature extraction, feature selection, and manifold techniques. In order to evaluate the effects of these techniques, we considered the multilingual extended version of Semantic Textual Similarity Benchmark (mSTSb) and two different baseline approaches, one using the pre-trained version of several models and another using their fine-tuned STS version. The results evidence that it is possible to achieve an average reduction in the number of dimensions of 91.58%±2.59% and 54.65%±32.20%, respectively. This work has also considered the consequences of dimensionality reduction for visualization purposes. The results of this study will significantly contribute to the understanding of how different tuning approaches affect performance on semantic-aware tasks and how dimensional reduction techniques deal with the high-dimensional embeddings computed for the STS task and their potential for highly demanding NLP tasks },
howpublished = {CoRR},
keywords = {Artificial Intelligence, Computational Intelligence, Deep Learning, Embeddings, Feature Selection, Machine Learning, Natural Language Processing, Sentence alignment, Text Analysis, Transformers},
pubstate = {published},
tppubtype = {misc}
}
Both in scientific literature and in industry,, Semantic and context-aware Natural Language Processing-based solutions have been gaining importance in recent years. The possibilities and performance shown by these models when dealing with complex Language Understanding tasks is unquestionable, from conversational agents to the fight against disinformation in social networks. In addition, considerable attention is also being paid to developing multilingual models to tackle the language bottleneck. The growing need to provide more complex models implementing all these features has been accompanied by an increase in their size, without being conservative in the number of dimensions required. This paper aims to give a comprehensive account of the impact of a wide variety of dimensional reduction techniques on the performance of different state-of-the-art multilingual Siamese Transformers, including unsupervised dimensional reduction techniques such as linear and nonlinear feature extraction, feature selection, and manifold techniques. In order to evaluate the effects of these techniques, we considered the multilingual extended version of Semantic Textual Similarity Benchmark (mSTSb) and two different baseline approaches, one using the pre-trained version of several models and another using their fine-tuned STS version. The results evidence that it is possible to achieve an average reduction in the number of dimensions of 91.58%±2.59% and 54.65%±32.20%, respectively. This work has also considered the consequences of dimensionality reduction for visualization purposes. The results of this study will significantly contribute to the understanding of how different tuning approaches affect performance on semantic-aware tasks and how dimensional reduction techniques deal with the high-dimensional embeddings computed for the STS task and their potential for highly demanding NLP tasks
2021
Stevenson, Emma; Rodriguez-Fernandez, Victor; Urrutxua, Hodei; Morand, Vincent; Camacho, David
Self-supervised machine learning based approach to orbit modelling applied to space traffic management Proceedings Article
In: 11th International Association for the Advancement of Space Safety Conference (IAASS), (Virtual), Osaka, Japan, 2021.
Abstract | BibTeX | Tags: Machine Learning, Orbits, Self-supervised Learning, Space Debris, Space Traffic Management
@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 = {Machine Learning, Orbits, Self-supervised Learning, Space Debris, Space Traffic Management},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Huertas-García, Álvaro; Huertas-Tato, Javier; Martín, Alejandro; Camacho, David
CIVIC-UPM at CheckThat! 2021: Integration of Transformers in Misinformation Detection and Topic Classification Proceedings Article
In: Conference and Labs of the Evaluation Forum (CLEF) Working Notes, pp. 520–530, 2021.
Abstract | Links | BibTeX | Tags: Computational Intelligence, Deep Learning, Disinformation, Information Distortion, Machine Learning, Natural Language Processing, Social Networks, Text Analysis, Transformers
@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 = {Computational Intelligence, Deep Learning, Disinformation, Information Distortion, Machine Learning, Natural Language Processing, Social Networks, Text Analysis, Transformers},
pubstate = {published},
tppubtype = {inproceedings}
}
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).
2008
R-Moreno, Maria D; Camacho, David; Obieta, Unai
A plan-based tool for automatic eLearning courses redesign Journal Article
In: IJCSA, vol. 5, no. 1, pp. 33-48, 2008.
BibTeX | Tags: Machine Learning, Planning
@article{DBLP:journals/ijcsa/Rodriguez-MorenoCO08,
title = {A plan-based tool for automatic eLearning courses redesign},
author = {Maria D R-Moreno and David Camacho and Unai Obieta},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
journal = {IJCSA},
volume = {5},
number = {1},
pages = {33-48},
keywords = {Machine Learning, Planning},
pubstate = {published},
tppubtype = {article}
}
1999
Borrajo, Daniel; Camacho, David; Silva, Andres
Multistrategy Relational Learning of Heuristics for Problem Solving Proceedings Article
In: Proceedings of the Nineteenth SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence, Cambridge, UK, 1999.
BibTeX | Tags: Machine Learning
@inproceedings{es99-ilp,
title = {Multistrategy Relational Learning of Heuristics for Problem Solving},
author = {Daniel Borrajo and David Camacho and Andres Silva},
year = {1999},
date = {1999-12-07},
booktitle = {Proceedings of the Nineteenth SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence},
address = {Cambridge, UK},
keywords = {Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}