Showing 1–50 of 56 results for author: Camacho, D

FMMVCC: Fuzzy Mamba-based Multi-View Contrastive Clustering for Univariate Time Series

Authors: Donato Cerciello, Leonardo Schiavo, Angel Panizo-LLedot, Javier Huertas Tato, David Camacho

Abstract: In many realistic scenarios, large volumes of time series data are generated with limited or expensive annotations. This limitation makes supervised learning methods difficult to apply and leads to the use of unsupervised approaches capable of discovering meaningful structures directly from raw data. Clustering therefore plays a crucial role in organizing time series into groups that share similar… ▽ More In many realistic scenarios, large volumes of time series data are generated with limited or expensive annotations. This limitation makes supervised learning methods difficult to apply and leads to the use of unsupervised approaches capable of discovering meaningful structures directly from raw data. Clustering therefore plays a crucial role in organizing time series into groups that share similar temporal patterns, enabling exploratory analysis and downstream tasks without requiring manual labeling. However, existing deep clustering methods often struggle to capture long-range temporal dependencies or rely on architectures with high computational cost. This paper introduces FMMVCC, a Mamba-based deep clustering framework for time series that leverages state space sequence modeling to efficiently learn temporal representations with linear complexity. Additionally, it utilizes multi-view self-supervised learning with temporal masking and augmentations. Experimental evaluation in 15 benchmark datasets proves that FMMVCC consistently outperforms state-of-the-art baselines, achieving the best overall performance in 29 of 60 total metric evaluations and the highest average rank in all tested scenarios. △ Less

Submitted 8 July, 2026; originally announced July 2026.

Journal ref: Proc. 2026 6th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE), IEEE, 2026

Behind the Mask: A Taxonomic Analysis of Activities in Online Social Networks

Authors: Debora F De Souza, Gabriela Beltrao, Berta Chulvi, Sergio Dantonio, Mehmet Gokay Ozerim, Javier Torregrosa, Adrian Giron, Angel Panizo, Pablo Miralles Gonzalez, Helena Liz, Javier Huertas Tato, Sonia Sousa, Alejandro Martin, Monika Maciuliene, David Camacho

Abstract: The broadcast of disinformation in online social networks (OSN) is a growing concern examined across several disciplines, including human-computer interaction (HCI). The pervasive issue has been prompting novel approaches to identify the malicious actors behind the dissemination of deceptive and fabricated content. Analyzing the characteristics and activities of these actors, we designed a taxonom… ▽ More The broadcast of disinformation in online social networks (OSN) is a growing concern examined across several disciplines, including human-computer interaction (HCI). The pervasive issue has been prompting novel approaches to identify the malicious actors behind the dissemination of deceptive and fabricated content. Analyzing the characteristics and activities of these actors, we designed a taxonomy informed by collaboration with subject matter experts (SMEs) and a review of the academic literature. Our study explores how to distinguish the characteristics, activities, and strategies of malicious actors on OSN and examines how they contribute to the spread of disinformation. We describe the design process and the application of the taxonomy in a case study analyzing anti-migration discourse in social media channels, and reflect on its potential to aid researchers and practitioners in the responsible design of network systems. △ Less

Submitted 25 June, 2026; originally announced June 2026.

xList-Hate: A Checklist-Based Framework for Interpretable and Generalizable Hate Speech Detection

Authors: Adrián Girón, Pablo Miralles, Javier Huertas-Tato, Sergio D'Antonio, David Camacho

Abstract: Hate speech detection is commonly framed as a direct binary classification problem despite being a composite concept defined through multiple interacting factors that vary across legal frameworks, platform policies, and annotation guidelines. As a result, supervised models often overfit dataset-specific definitions and exhibit limited robustness under domain shift and annotation noise. We introd… ▽ More Hate speech detection is commonly framed as a direct binary classification problem despite being a composite concept defined through multiple interacting factors that vary across legal frameworks, platform policies, and annotation guidelines. As a result, supervised models often overfit dataset-specific definitions and exhibit limited robustness under domain shift and annotation noise. We introduce xList-Hate, a diagnostic framework that decomposes hate speech detection into a checklist of explicit, concept-level questions grounded in widely shared normative criteria. Each question is independently answered by a large language model (LLM), producing a binary diagnostic representation that captures hateful content features without directly predicting the final label. These diagnostic signals are then aggregated by a lightweight, fully interpretable decision tree, yielding transparent and auditable predictions. We evaluate it across multiple hate speech benchmarks and model families, comparing it against zero-shot LLM classification and in-domain supervised fine-tuning. While supervised methods typically maximize in-domain performance, we consistently improves cross-dataset robustness and relative performance under domain shift. In addition, qualitative analysis of disagreement cases provides evidence that the framework can be less sensitive to certain forms of annotation inconsistency and contextual ambiguity. Crucially, the approach enables fine-grained interpretability through explicit decision paths and factor-level analysis. Our results suggest that reframing hate speech detection as a diagnostic reasoning task, rather than a monolithic classification problem, provides a robust, explainable, and extensible alternative for content moderation. △ Less

Submitted 5 February, 2026; originally announced February 2026.

Supervised Contrastive Learning for Few-Shot AI-Generated Image Detection and Attribution

Authors: Jaime Álvarez Urueña, David Camacho, Javier Huertas Tato

Abstract: The rapid advancement of generative artificial intelligence has enabled the creation of synthetic images that are increasingly indistinguishable from authentic content, posing significant challenges for digital media integrity. This problem is compounded by the accelerated release cycle of novel generative models, which renders traditional detection approaches (reliant on periodic retraining) comp… ▽ More The rapid advancement of generative artificial intelligence has enabled the creation of synthetic images that are increasingly indistinguishable from authentic content, posing significant challenges for digital media integrity. This problem is compounded by the accelerated release cycle of novel generative models, which renders traditional detection approaches (reliant on periodic retraining) computationally infeasible and operationally impractical. This work proposes a novel two-stage detection framework designed to address the generalization challenge inherent in synthetic image detection. The first stage employs a vision deep learning model trained via supervised contrastive learning to extract discriminative embeddings from input imagery. Critically, this model was trained on a strategically partitioned subset of available generators, with specific architectures withheld from training to rigorously ablate cross-generator generalization capabilities. The second stage utilizes a k-nearest neighbors (k-NN) classifier operating on the learned embedding space, trained in a few-shot learning paradigm incorporating limited samples from previously unseen test generators. With merely 150 images per class in the few-shot learning regime, which are easily obtainable from current generation models, the proposed framework achieves an average detection accuracy of 91.3%, representing a 5.2 percentage point improvement over existing approaches . For the source attribution task, the proposed approach obtains improvements of of 14.70% and 4.27% in AUC and OSCR respectively on an open set classification context, marking a significant advancement toward robust, scalable forensic attribution systems capable of adapting to the evolving generative AI landscape without requiring exhaustive retraining protocols. △ Less

Submitted 21 November, 2025; v1 submitted 20 November, 2025; originally announced November 2025.

Comments: 17 pages, 6 figures, 6 tables

ACM Class: I.2.10; I.4.10

CLIP4VI-ReID: Learning Modality-shared Representations via CLIP Semantic Bridge for Visible-Infrared Person Re-identification

Authors: Xiaomei Yang, Xizhan Gao, Sijie Niu, Fa Zhu, Guang Feng, Xiaofeng Qu, David Camacho

Abstract: This paper proposes a novel CLIP-driven modality-shared representation learning network named CLIP4VI-ReID for VI-ReID task, which consists of Text Semantic Generation (TSG), Infrared Feature Embedding (IFE), and High-level Semantic Alignment (HSA). Specifically, considering the huge gap in the physical characteristics between natural images and infrared images, the TSG is designed to generate tex… ▽ More This paper proposes a novel CLIP-driven modality-shared representation learning network named CLIP4VI-ReID for VI-ReID task, which consists of Text Semantic Generation (TSG), Infrared Feature Embedding (IFE), and High-level Semantic Alignment (HSA). Specifically, considering the huge gap in the physical characteristics between natural images and infrared images, the TSG is designed to generate text semantics only for visible images, thereby enabling preliminary visible-text modality alignment. Then, the IFE is proposed to rectify the feature embeddings of infrared images using the generated text semantics. This process injects id-related semantics into the shared image encoder, enhancing its adaptability to the infrared modality. Besides, with text serving as a bridge, it enables indirect visible-infrared modality alignment. Finally, the HSA is established to refine the high-level semantic alignment. This process ensures that the fine-tuned text semantics only contain id-related information, thereby achieving more accurate cross-modal alignment and enhancing the discriminability of the learned modal-shared representations. Extensive experimental results demonstrate that the proposed CLIP4VI-ReID achieves superior performance than other state-of-the-art methods on some widely used VI-ReID datasets. △ Less

Submitted 3 August, 2026; v1 submitted 13 November, 2025; originally announced November 2025.

Comments: This article has been accepted for publication in IEEE Transactions on Biometrics, Behavior, and Identity Science

LLM one-shot style transfer for Authorship Attribution and Verification

Authors: Pablo Miralles-González, Javier Huertas-Tato, Alejandro Martín, David Camacho

Abstract: Computational stylometry studies writing style through quantitative textual patterns, enabling applications such as authorship attribution, identity linking, and plagiarism detection. Existing supervised and contrastive approaches often rely on datasets with spurious correlations, conflating style with topic. Despite the relevance of language modeling to these tasks, the pre-training of modern lar… ▽ More Computational stylometry studies writing style through quantitative textual patterns, enabling applications such as authorship attribution, identity linking, and plagiarism detection. Existing supervised and contrastive approaches often rely on datasets with spurious correlations, conflating style with topic. Despite the relevance of language modeling to these tasks, the pre-training of modern large language models (LLMs) has been underutilized in general authorship analysis. We introduce an unsupervised framework that uses the log-probabilities of an LLM to measure style transferability between two texts. This framework takes advantage of the extensive CLM pre-training and in-context capabilities of modern LLMs. Our approach avoids explicit supervision with spuriously correlated data. Our method substantially outperforms unsupervised prompting-based baselines at similar model sizes and exceeds contrastively trained models when controlling for topical overlap. Our framework's performance improves with model size. In the case of authorship verification, we present an additional mechanism that increases test-time computation to improve accuracy; enabling flexible trade-offs between computational cost and task performance. △ Less

Submitted 18 December, 2025; v1 submitted 15 October, 2025; originally announced October 2025.

Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics

Authors: Inmaculada Santamaria-Valenzuela, Victor Rodriguez-Fernandez, Javier Huertas-Tato, Jong Hyuk Park, David Camacho

Abstract: The present study explores the interpretability of latent spaces produced by time series foundation models, focusing on their potential for visual analysis tasks. Specifically, we evaluate the MOMENT family of models, a set of transformer-based, pre-trained architectures for multivariate time series tasks such as: imputation, prediction, classification, and anomaly detection. We evaluate the capac… ▽ More The present study explores the interpretability of latent spaces produced by time series foundation models, focusing on their potential for visual analysis tasks. Specifically, we evaluate the MOMENT family of models, a set of transformer-based, pre-trained architectures for multivariate time series tasks such as: imputation, prediction, classification, and anomaly detection. We evaluate the capacity of these models on five datasets to capture the underlying structures in time series data within their latent space projection and validate whether fine tuning improves the clarity of the resulting embedding spaces. Notable performance improvements in terms of loss reduction were observed after fine tuning. Visual analysis shows limited improvement in the interpretability of the embeddings, requiring further work. Results suggest that, although Time Series Foundation Models such as MOMENT are robust, their latent spaces may require additional methodological refinements to be adequately interpreted, such as alternative projection techniques, loss functions, or data preprocessing strategies. Despite the limitations of MOMENT, foundation models supose a big reduction in execution time and so a great advance for interactive visual analytics. △ Less

Submitted 26 April, 2025; originally announced April 2025.

Comments: Currently under review at the International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)

Pushing the boundary on Natural Language Inference

Authors: Pablo Miralles-González, Javier Huertas-Tato, Alejandro Martín, David Camacho

Abstract: Natural Language Inference (NLI) is a central task in natural language understanding with applications in fact-checking, question answering, and information retrieval. Despite its importance, current NLI systems heavily rely on supervised learning with datasets that often contain annotation artifacts and biases, limiting generalization and real-world applicability. In this work, we apply a reinfor… ▽ More Natural Language Inference (NLI) is a central task in natural language understanding with applications in fact-checking, question answering, and information retrieval. Despite its importance, current NLI systems heavily rely on supervised learning with datasets that often contain annotation artifacts and biases, limiting generalization and real-world applicability. In this work, we apply a reinforcement learning-based approach using Group Relative Policy Optimization (GRPO) for Chain-of-Thought (CoT) learning in NLI, eliminating the need for labeled rationales and enabling this type of training on more challenging datasets such as ANLI. We fine-tune 7B, 14B, and 32B language models using parameter-efficient techniques (LoRA and QLoRA), demonstrating strong performance across standard and adversarial NLI benchmarks. Our 32B AWQ-quantized model surpasses state-of-the-art results on 7 out of 11 adversarial sets$\unicode{x2013}$or on all of them considering our replication$\unicode{x2013}$within a 22GB memory footprint, showing that robust reasoning can be retained under aggressive quantization. This work provides a scalable and practical framework for building robust NLI systems without sacrificing inference quality. △ Less

Submitted 6 May, 2025; v1 submitted 25 April, 2025; originally announced April 2025.

Influence Operations in Social Networks

Authors: Javier Pastor-Galindo, Pantaleone Nespoli, José A. Ruipérez-Valiente, David Camacho

Abstract: An important part of online activities are intended to control the public opinion and behavior, being considered currently a global threat. This article identifies and conceptualizes seven online strategies employed in social media influence operations. These procedures are quantified through the analysis of 80 incidents of foreign information manipulation and interference (FIMI), estimating their… ▽ More An important part of online activities are intended to control the public opinion and behavior, being considered currently a global threat. This article identifies and conceptualizes seven online strategies employed in social media influence operations. These procedures are quantified through the analysis of 80 incidents of foreign information manipulation and interference (FIMI), estimating their real-world usage and combination. Finally, we suggest future directions for research on influence operations. △ Less

Submitted 17 February, 2025; originally announced February 2025.

Statistical Analysis of Risk Assessment Factors and Metrics to Evaluate Radicalisation in Twitter

Authors: Raul Lara-Cabrera, Antonio Gonzalez-Pardo, David Camacho

Abstract: Nowadays, Social Networks have become an essential communication tools producing a large amount of information about their users and their interactions, which can be analysed with Data Mining methods. In the last years, Social Networks are being used to radicalise people. In this paper, we study the performance of a set of indicators and their respective metrics, devoted to assess the risk of radi… ▽ More Nowadays, Social Networks have become an essential communication tools producing a large amount of information about their users and their interactions, which can be analysed with Data Mining methods. In the last years, Social Networks are being used to radicalise people. In this paper, we study the performance of a set of indicators and their respective metrics, devoted to assess the risk of radicalisation of a precise individual on three different datasets. Keyword-based metrics, even though depending on the written language, performs well when measuring frustration, perception of discrimination as well as declaration of negative and positive ideas about Western society and Jihadism, respectively. However, metrics based on frequent habits such as writing ellipses are not well enough to characterise a user in risk of radicalisation. The paper presents a detailed description of both, the set of indicators used to asses the radicalisation in Social Networks and the set of datasets used to evaluate them. Finally, an experimental study over these datasets are carried out to evaluate the performance of the metrics considered. △ Less

Submitted 28 January, 2025; originally announced January 2025.

Journal ref: Future Generation Computer Systems, 93, 971-978 (2019)

On the locality bias and results in the Long Range Arena

Authors: Pablo Miralles-González, Javier Huertas-Tato, Alejandro Martín, David Camacho

Abstract: The Long Range Arena (LRA) benchmark was designed to evaluate the performance of Transformer improvements and alternatives in long-range dependency modeling tasks. The Transformer and its main variants performed poorly on this benchmark, and a new series of architectures such as State Space Models (SSMs) gained some traction, greatly outperforming Transformers in the LRA. Recent work has shown tha… ▽ More The Long Range Arena (LRA) benchmark was designed to evaluate the performance of Transformer improvements and alternatives in long-range dependency modeling tasks. The Transformer and its main variants performed poorly on this benchmark, and a new series of architectures such as State Space Models (SSMs) gained some traction, greatly outperforming Transformers in the LRA. Recent work has shown that with a denoising pre-training phase, Transformers can achieve competitive results in the LRA with these new architectures. In this work, we discuss and explain the superiority of architectures such as MEGA and SSMs in the Long Range Arena, as well as the recent improvement in the results of Transformers, pointing to the positional and local nature of the tasks. We show that while the LRA is a benchmark for long-range dependency modeling, in reality most of the performance comes from short-range dependencies. Using training techniques to mitigate data inefficiency, Transformers are able to reach state-of-the-art performance with proper positional encoding. In addition, with the same techniques, we were able to remove all restrictions from SSM convolutional kernels and learn fully parameterized convolutions without decreasing performance, suggesting that the design choices behind SSMs simply added inductive biases and learning efficiency for these particular tasks. Our insights indicate that LRA results should be interpreted with caution and call for a redesign of the benchmark. △ Less

Submitted 24 January, 2025; originally announced January 2025.

Not all tokens are created equal: Perplexity Attention Weighted Networks for AI generated text detection

Authors: Pablo Miralles-González, Javier Huertas-Tato, Alejandro Martín, David Camacho

Abstract: The rapid advancement in large language models (LLMs) has significantly enhanced their ability to generate coherent and contextually relevant text, raising concerns about the misuse of AI-generated content and making it critical to detect it. However, the task remains challenging, particularly in unseen domains or with unfamiliar LLMs. Leveraging LLM next-token distribution outputs offers a theore… ▽ More The rapid advancement in large language models (LLMs) has significantly enhanced their ability to generate coherent and contextually relevant text, raising concerns about the misuse of AI-generated content and making it critical to detect it. However, the task remains challenging, particularly in unseen domains or with unfamiliar LLMs. Leveraging LLM next-token distribution outputs offers a theoretically appealing approach for detection, as they encapsulate insights from the models' extensive pre-training on diverse corpora. Despite its promise, zero-shot methods that attempt to operationalize these outputs have met with limited success. We hypothesize that one of the problems is that they use the mean to aggregate next-token distribution metrics across tokens, when some tokens are naturally easier or harder to predict and should be weighted differently. Based on this idea, we propose the Perplexity Attention Weighted Network (PAWN), which uses the last hidden states of the LLM and positions to weight the sum of a series of features based on metrics from the next-token distribution across the sequence length. Although not zero-shot, our method allows us to cache the last hidden states and next-token distribution metrics on disk, greatly reducing the training resource requirements. PAWN shows competitive and even better performance in-distribution than the strongest baselines (fine-tuned LMs) with a fraction of their trainable parameters. Our model also generalizes better to unseen domains and source models, with smaller variability in the decision boundary across distribution shifts. It is also more robust to adversarial attacks, and if the backbone has multilingual capabilities, it presents decent generalization to languages not seen during supervised training, with LLaMA3-1B reaching a mean macro-averaged F1 score of 81.46% in cross-validation with nine languages. △ Less

Submitted 14 July, 2025; v1 submitted 7 January, 2025; originally announced January 2025.

Isolating authorship from content with semantic embeddings and contrastive learning

Authors: Javier Huertas-Tato, Adrián Girón-Jiménez, Alejandro Martín, David Camacho

Abstract: Authorship has entangled style and content inside. Authors frequently write about the same topics in the same style, so when different authors write about the exact same topic the easiest way out to distinguish them is by understanding the nuances of their style. Modern neural models for authorship can pick up these features using contrastive learning, however, some amount of content leakage is al… ▽ More Authorship has entangled style and content inside. Authors frequently write about the same topics in the same style, so when different authors write about the exact same topic the easiest way out to distinguish them is by understanding the nuances of their style. Modern neural models for authorship can pick up these features using contrastive learning, however, some amount of content leakage is always present. Our aim is to reduce the inevitable impact and correlation between content and authorship. We present a technique to use contrastive learning (InfoNCE) with additional hard negatives synthetically created using a semantic similarity model. This disentanglement technique aims to distance the content embedding space from the style embedding space, leading to embeddings more informed by style. We demonstrate the performance with ablations on two different datasets and compare them on out-of-domain challenges. Improvements are clearly shown on challenging evaluations on prolific authors with up to a 10% increase in accuracy when the settings are particularly hard. Trials on challenges also demonstrate the preservation of zero-shot capabilities of this method as fine tuning. △ Less

Submitted 27 November, 2024; originally announced November 2024.

A CLIP-based siamese approach for meme classification

Authors: Javier Huertas-Tato, Christos Koutlis, Symeon Papadopoulos, David Camacho, Ioannis Kompatsiaris

Abstract: Memes are an increasingly prevalent element of online discourse in social networks, especially among young audiences. They carry ideas and messages that range from humorous to hateful, and are widely consumed. Their potentially high impact requires adequate means of control to moderate their use in large scale. In this work, we propose SimCLIP a deep learning-based architecture for cross-modal und… ▽ More Memes are an increasingly prevalent element of online discourse in social networks, especially among young audiences. They carry ideas and messages that range from humorous to hateful, and are widely consumed. Their potentially high impact requires adequate means of control to moderate their use in large scale. In this work, we propose SimCLIP a deep learning-based architecture for cross-modal understanding of memes, leveraging a pre-trained CLIP encoder to produce context-aware embeddings and a Siamese fusion technique to capture the interactions between text and image. We perform an extensive experimentation on seven meme classification tasks across six datasets. We establish a new state of the art in Memotion7k with a 7.25% relative F1-score improvement, and achieve super-human performance on Harm-P with 13.73% F1-Score improvement. Our approach demonstrates the potential for compact meme classification models, enabling accurate and efficient meme monitoring. We share our code at https://github.com/jahuerta92/meme-classification-simclip △ Less

Submitted 9 September, 2024; originally announced September 2024.

Exploring Scalability in Large-Scale Time Series in DeepVATS framework

Authors: Inmaculada Santamaria-Valenzuela, Victor Rodriguez-Fernandez, David Camacho

Abstract: Visual analytics is essential for studying large time series due to its ability to reveal trends, anomalies, and insights. DeepVATS is a tool that merges Deep Learning (Deep) with Visual Analytics (VA) for the analysis of large time series data (TS). It has three interconnected modules. The Deep Learning module, developed in R, manages the load of datasets and Deep Learning models from and to the… ▽ More Visual analytics is essential for studying large time series due to its ability to reveal trends, anomalies, and insights. DeepVATS is a tool that merges Deep Learning (Deep) with Visual Analytics (VA) for the analysis of large time series data (TS). It has three interconnected modules. The Deep Learning module, developed in R, manages the load of datasets and Deep Learning models from and to the Storage module. This module also supports models training and the acquisition of the embeddings from the latent space of the trained model. The Storage module operates using the Weights and Biases system. Subsequently, these embeddings can be analyzed in the Visual Analytics module. This module, based on an R Shiny application, allows the adjustment of the parameters related to the projection and clustering of the embeddings space. Once these parameters are set, interactive plots representing both the embeddings, and the time series are shown. This paper introduces the tool and examines its scalability through log analytics. The execution time evolution is examined while the length of the time series is varied. This is achieved by resampling a large data series into smaller subsets and logging the main execution and rendering times for later analysis of scalability. △ Less

Submitted 8 August, 2024; originally announced August 2024.

Comments: Admitted pending publication in Lecture Notes in Network and Systems (LNNS) series (Springer). Code available at https://github.com/vrodriguezf/deepvats

Report number: The 13th Conference on Information Technology and its Applications

Journal ref: The 13th Conference on Information Technology and its Applications, Lecture Notes in Networks and Systems, vol. 937, Springer, Cham, 2024, pp. 244-255

Generative Design of Periodic Orbits in the Restricted Three-Body Problem

Authors: Alvaro Francisco Gil, Walther Litteri, Victor Rodriguez-Fernandez, David Camacho, Massimiliano Vasile

Abstract: The Three-Body Problem has fascinated scientists for centuries and it has been crucial in the design of modern space missions. Recent developments in Generative Artificial Intelligence hold transformative promise for addressing this longstanding problem. This work investigates the use of Variational Autoencoder (VAE) and its internal representation to generate periodic orbits. We utilize a compreh… ▽ More The Three-Body Problem has fascinated scientists for centuries and it has been crucial in the design of modern space missions. Recent developments in Generative Artificial Intelligence hold transformative promise for addressing this longstanding problem. This work investigates the use of Variational Autoencoder (VAE) and its internal representation to generate periodic orbits. We utilize a comprehensive dataset of periodic orbits in the Circular Restricted Three-Body Problem (CR3BP) to train deep-learning architectures that capture key orbital characteristics, and we set up physical evaluation metrics for the generated trajectories. Through this investigation, we seek to enhance the understanding of how Generative AI can improve space mission planning and astrodynamics research, leading to novel, data-driven approaches in the field. △ Less

Submitted 7 August, 2024; originally announced August 2024.

Comments: SPAICE Conference 2024 (7 pages)

Journal ref: Proceedings of SPAICE 2024, pp. 430-436

DisTrack: a new Tool for Semi-automatic Misinformation Tracking in Online Social Networks

Authors: Guillermo Villar-Rodríguez, Álvaro Huertas-García, Alejandro Martín, Javier Huertas-Tato, David Camacho

Abstract: Introduction: This article introduces DisTrack, a methodology and a tool developed for tracking and analyzing misinformation within Online Social Networks (OSNs). DisTrack is designed to combat the spread of misinformation through a combination of Natural Language Processing (NLP) Social Network Analysis (SNA) and graph visualization. The primary goal is to detect misinformation, track its propaga… ▽ More Introduction: This article introduces DisTrack, a methodology and a tool developed for tracking and analyzing misinformation within Online Social Networks (OSNs). DisTrack is designed to combat the spread of misinformation through a combination of Natural Language Processing (NLP) Social Network Analysis (SNA) and graph visualization. The primary goal is to detect misinformation, track its propagation, identify its sources, and assess the influence of various actors within the network. Methods: DisTrack's architecture incorporates a variety of methodologies including keyword search, semantic similarity assessments, and graph generation techniques. These methods collectively facilitate the monitoring of misinformation, the categorization of content based on alignment with known false claims, and the visualization of dissemination cascades through detailed graphs. The tool is tailored to capture and analyze the dynamic nature of misinformation spread in digital environments. Results: The effectiveness of DisTrack is demonstrated through three case studies focused on different themes: discredit/hate speech, anti-vaccine misinformation, and false narratives about the Russia-Ukraine conflict. These studies show DisTrack's capabilities in distinguishing posts that propagate falsehoods from those that counteract them, and tracing the evolution of misinformation from its inception. Conclusions: The research confirms that DisTrack is a valuable tool in the field of misinformation analysis. It effectively distinguishes between different types of misinformation and traces their development over time. By providing a comprehensive approach to understanding and combating misinformation in digital spaces, DisTrack proves to be an essential asset for researchers and practitioners working to mitigate the impact of false information in online social environments. △ Less

Submitted 1 August, 2024; originally announced August 2024.

Beyond Point Masses. II. Non-Keplerian Shape Effects are Detectable in Several TNO Binaries

Authors: Benjamin C. N. Proudfoot, Darin A. Ragozzine, Meagan L. Thatcher, Will Grundy, Dallin J. Spencer, Tahina M. Alailima, Sawyer Allen, Penelope C. Bowden, Susanne Byrd, Conner D. Camacho, Gibson H. Campbell, Edison P. Carlisle, Jacob A. Christensen, Noah K. Christensen, Kaelyn Clement, Benjamin J. Derieg, Mara K. Dille, Cristian Dorrett, Abigail L. Ellefson, Taylor S. Fleming, N. J. Freeman, Ethan J. Gibson, William G. Giforos, Jacob A. Guerrette, Olivia Haddock , et al. (38 additional authors not shown)

Abstract: About 40 transneptunian binaries (TNBs) have fully determined orbits with about 10 others being solved except for breaking the mirror ambiguity. Despite decades of study almost all TNBs have only ever been analyzed with a model that assumes perfect Keplerian motion (e.g., two point masses). In reality, all TNB systems are non-Keplerian due to non-spherical shapes, possible presence of undetected s… ▽ More About 40 transneptunian binaries (TNBs) have fully determined orbits with about 10 others being solved except for breaking the mirror ambiguity. Despite decades of study almost all TNBs have only ever been analyzed with a model that assumes perfect Keplerian motion (e.g., two point masses). In reality, all TNB systems are non-Keplerian due to non-spherical shapes, possible presence of undetected system components, and/or solar perturbations. In this work, we focus on identifying candidates for detectable non-Keplerian motion based on sample of 45 well-characterized binaries. We use MultiMoon, a non-Keplerian Bayesian inference tool, to analyze published relative astrometry allowing for non-spherical shapes of each TNB system's primary. We first reproduce the results of previous Keplerian fitting efforts with MultiMoon, which serves as a comparison for the non-Keplerian fits and confirms that these fits are not biased by the assumption of a Keplerian orbit. We unambiguously detect non-Keplerian motion in 8 TNB systems across a range of primary radii, mutual orbit separations, and system masses. As a proof of concept for non-Keplerian fitting, we perform detailed fits for (66652) Borasisi-Pabu, possibly revealing a $J_2 \approx 0.44$, implying Borasisi (and/or Pabu) may be a contact binary or an unresolved compact binary. However, full confirmation of this result will require new observations. This work begins the next generation of TNB analyses that go beyond the point mass assumption to provide unique and valuable information on the physical properties of TNBs with implications for their formation and evolution. △ Less

Submitted 19 March, 2024; originally announced March 2024.

Comments: Accepted to AJ

Journal ref: AJ 167 144 (2024)

Extending QGroundControl for Automated Mission Planning of UAVs

Authors: Cristian Ramirez-Atencia, David Camacho

Abstract: Unmanned Aerial Vehicle (UAVs) have become very popular in the last decade due to some advantages such as strong terrain adaptation, low cost, zero casualties, and so on. One of the most interesting advances in this field is the automation of mission planning (task allocation) and real-time replanning, which are highly useful to increase the autonomy of the vehicle and reduce the operator workload… ▽ More Unmanned Aerial Vehicle (UAVs) have become very popular in the last decade due to some advantages such as strong terrain adaptation, low cost, zero casualties, and so on. One of the most interesting advances in this field is the automation of mission planning (task allocation) and real-time replanning, which are highly useful to increase the autonomy of the vehicle and reduce the operator workload. These automated mission planning and replanning systems require a Human Computer Interface (HCI) that facilitates the visualization and selection of plans that will be executed by the vehicles. In addition, most missions should be assessed before their real-life execution. This paper extends QGroundControl, an open-source simulation environment for flight control of multiple vehicles, by adding a mission designer that permits the operator to build complex missions with tasks and other scenario items; an interface for automated mission planning and replanning, which works as a test bed for different algorithms, and a Decision Support System (DSS) that helps the operator in the selection of the plan. In this work, a complete guide of these systems and some practical use cases are provided. △ Less

Submitted 28 February, 2024; originally announced February 2024.

Comments: Preprint submitted and accepted in Sensors

Journal ref: Sensors 18, no. 7: 2339 (2018)

Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning

Authors: Cristian Ramirez-Atencia, Javier Del Ser, David Camacho

Abstract: Management and mission planning over a swarm of unmanned aerial vehicle (UAV) remains to date as a challenging research trend in what regards to this particular type of aircrafts. These vehicles are controlled by a number of ground control station (GCS), from which they are commanded to cooperatively perform different tasks in specific geographic areas of interest. Mathematically the problem of co… ▽ More Management and mission planning over a swarm of unmanned aerial vehicle (UAV) remains to date as a challenging research trend in what regards to this particular type of aircrafts. These vehicles are controlled by a number of ground control station (GCS), from which they are commanded to cooperatively perform different tasks in specific geographic areas of interest. Mathematically the problem of coordinating and assigning tasks to a swarm of UAV can be modeled as a constraint satisfaction problem, whose complexity and multiple conflicting criteria has hitherto motivated the adoption of multi-objective solvers such as multi-objective evolutionary algorithm (MOEA). The encoding approach consists of different alleles representing the decision variables, whereas the fitness function checks that all constraints are fulfilled, minimizing the optimization criteria of the problem. In problems of high complexity involving several tasks, UAV and GCS, where the space of search is huge compared to the space of valid solutions, the convergence rate of the algorithm increases significantly. To overcome this issue, this work proposes a weighted random generator for the creation and mutation of new individuals. The main objective of this work is to reduce the convergence rate of the MOEA solver for multi-UAV mission planning using weighted random strategies that focus the search on potentially better regions of the solution space. Extensive experimental results over a diverse range of scenarios evince the benefits of the proposed approach, which notably improves this convergence rate with respect to a naïve MOEA approach. △ Less

Submitted 28 February, 2024; originally announced February 2024.

Comments: Preprint submitted and accepted in Swarm and Evolutionary Computation

Journal ref: Cristian Ramirez Atencia, Javier Del Ser, David Camacho: Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning. Swarm and Evolutionary Computation, Volume 44, Pages 480-495, 2019

A revision on Multi-Criteria Decision Making methods for Multi-UAV Mission Planning Support

Authors: Cristian Ramirez-Atencia, Victor Rodriguez-Fernandez, David Camacho

Abstract: Over the last decade, Unmanned Aerial Vehicles (UAVs) have been extensively used in many commercial applications due to their manageability and risk avoidance. One of the main problems considered is the Mission Planning for multiple UAVs, where a solution plan must be found satisfying the different constraints of the problem. This problem has multiple variables that must be optimized simultaneousl… ▽ More Over the last decade, Unmanned Aerial Vehicles (UAVs) have been extensively used in many commercial applications due to their manageability and risk avoidance. One of the main problems considered is the Mission Planning for multiple UAVs, where a solution plan must be found satisfying the different constraints of the problem. This problem has multiple variables that must be optimized simultaneously, such as the makespan, the cost of the mission or the risk. Therefore, the problem has a lot of possible optimal solutions, and the operator must select the final solution to be executed among them. In order to reduce the workload of the operator in this decision process, a Decision Support System (DSS) becomes necessary. In this work, a DSS consisting of ranking and filtering systems, which order and reduce the optimal solutions, has been designed. With regard to the ranking system, a wide range of Multi-Criteria Decision Making (MCDM) methods, including some fuzzy MCDM, are compared on a multi-UAV mission planning scenario, in order to study which method could fit better in a multi-UAV decision support system. Expert operators have evaluated the solutions returned, and the results show, on the one hand, that fuzzy methods generally achieve better average scores, and on the other, that all of the tested methods perform better when the preferences of the operators are biased towards a specific variable, and worse when their preferences are balanced. For the filtering system, a similarity function based on the proximity of the solutions has been designed, and on top of that, a threshold is tuned empirically to decide how to filter solutions without losing much of the hypervolume of the space of solutions. △ Less

Submitted 28 February, 2024; originally announced February 2024.

Comments: Preprint submitted and acepted in Expert Systems with Applications

Journal ref: Expert Systems with Applications, Volume 160, 2020, 113708

An Effective Networks Intrusion Detection Approach Based on Hybrid Harris Hawks and Multi-Layer Perceptron

Authors: Moutaz Alazab, Ruba Abu Khurma, Pedro A. Castillo, Bilal Abu-Salih, Alejandro Martin, David Camacho

Abstract: This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks Optimization algorithm (HHO) to optimize Multilayer Perceptron learning by optimizing bias and weight parameters. HHO-MLP aims to select optimal parameters in its learning process to minimize intrusion detection errors in networks. HHO-MLP has been implemented using EvoloPy NN framework, an open-source Python tool s… ▽ More This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks Optimization algorithm (HHO) to optimize Multilayer Perceptron learning by optimizing bias and weight parameters. HHO-MLP aims to select optimal parameters in its learning process to minimize intrusion detection errors in networks. HHO-MLP has been implemented using EvoloPy NN framework, an open-source Python tool specialized for training MLPs using evolutionary algorithms. For purposes of comparing the HHO model against other evolutionary methodologies currently available, specificity and sensitivity measures, accuracy measures, and mse and rmse measures have been calculated using KDD datasets. Experiments have demonstrated the HHO MLP method is effective at identifying malicious patterns. HHO-MLP has been tested against evolutionary algorithms like Butterfly Optimization Algorithm (BOA), Grasshopper Optimization Algorithms (GOA), and Black Widow Optimizations (BOW), with validation by Random Forest (RF), XG-Boost. HHO-MLP showed superior performance by attaining top scores with accuracy rate of 93.17%, sensitivity level of 89.25%, and specificity percentage of 95.41%. △ Less

Submitted 21 February, 2024; originally announced February 2024.

Camouflage is all you need: Evaluating and Enhancing Language Model Robustness Against Camouflage Adversarial Attacks

Authors: Álvaro Huertas-García, Alejandro Martín, Javier Huertas-Tato, David Camacho

Abstract: Adversarial attacks represent a substantial challenge in Natural Language Processing (NLP). This study undertakes a systematic exploration of this challenge in two distinct phases: vulnerability evaluation and resilience enhancement of Transformer-based models under adversarial attacks. In the evaluation phase, we assess the susceptibility of three Transformer configurations, encoder-decoder, en… ▽ More Adversarial attacks represent a substantial challenge in Natural Language Processing (NLP). This study undertakes a systematic exploration of this challenge in two distinct phases: vulnerability evaluation and resilience enhancement of Transformer-based models under adversarial attacks. In the evaluation phase, we assess the susceptibility of three Transformer configurations, encoder-decoder, encoder-only, and decoder-only setups, to adversarial attacks of escalating complexity across datasets containing offensive language and misinformation. Encoder-only models manifest a 14% and 21% performance drop in offensive language detection and misinformation detection tasks, respectively. Decoder-only models register a 16% decrease in both tasks, while encoder-decoder models exhibit a maximum performance drop of 14% and 26% in the respective tasks. The resilience-enhancement phase employs adversarial training, integrating pre-camouflaged and dynamically altered data. This approach effectively reduces the performance drop in encoder-only models to an average of 5% in offensive language detection and 2% in misinformation detection tasks. Decoder-only models, occasionally exceeding original performance, limit the performance drop to 7% and 2% in the respective tasks. Although not surpassing the original performance, Encoder-decoder models can reduce the drop to an average of 6% and 2% respectively. Results suggest a trade-off between performance and robustness, with some models maintaining similar performance while gaining robustness. Our study and adversarial training techniques have been incorporated into an open-source tool for generating camouflaged datasets. However, methodology effectiveness depends on the specific camouflage technique and data encountered, emphasizing the need for continued exploration. △ Less

Submitted 15 February, 2024; originally announced February 2024.

Comments: 19 pages, 8 figures, 5 tables

Constrained multi-objective optimization for multi-UAV planning

Authors: Cristian Ramirez-Atencia, David Camacho

Abstract: Over the last decade, developments in unmanned aerial vehicles (UAVs) has greatly increased, and they are being used in many fields including surveillance, crisis management or automated mission planning. This last field implies the search of plans for missions with multiple tasks, UAVs and ground control stations; and the optimization of several objectives, including makespan, fuel consumption or… ▽ More Over the last decade, developments in unmanned aerial vehicles (UAVs) has greatly increased, and they are being used in many fields including surveillance, crisis management or automated mission planning. This last field implies the search of plans for missions with multiple tasks, UAVs and ground control stations; and the optimization of several objectives, including makespan, fuel consumption or cost, among others. In this work, this problem has been solved using a multi-objective evolutionary algorithm combined with a constraint satisfaction problem model, which is used in the fitness function of the algorithm. The algorithm has been tested on several missions of increasing complexity, and the computational complexity of the different element considered in the missions has been studied. △ Less

Submitted 9 February, 2024; originally announced February 2024.

Comments: Preprint of the article submitted and published in Journal of Ambient Intelligence and Humanized Computing

Journal ref: Journal of Ambient Intelligence and Humanized Computing 10, 2467-2484, 2019

Solving Complex Multi-UAV Mission Planning Problems using Multi-objective Genetic Algorithms

Authors: Cristian Ramirez-Atencia, Gema Bello-Orgaz, Maria D R-Moreno, David Camacho

Abstract: Due to recent booming of UAVs technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission Planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring inform… ▽ More Due to recent booming of UAVs technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission Planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring information) for the vehicles, typically over a time period. These vehicles are controlled from Ground Control Stations (GCSs) where human operators use rudimentary systems. This paper presents a new Multi-Objective Genetic Algorithm for solving complex Mission Planning Problems (MPP) involving a team of UAVs and a set of GCSs. A hybrid fitness function has been designed using a Constraint Satisfaction Problem (CSP) to check if solutions are valid and Pareto-based measures to look for optimal solutions. The algorithm has been tested on several datasets optimizing different variables of the mission, such as the makespan, the fuel consumption, distance, etc. Experimental results show that the new algorithm is able to obtain good solutions, however as the problem becomes more complex, the optimal solutions also become harder to find. △ Less

Submitted 9 February, 2024; originally announced February 2024.

Comments: This is a preprint version of the article submitted and published in Soft Computing

Journal ref: Soft Computing 21, 4883-4900, 2017

Self-supervised Machine Learning Based Approach to Orbit Modelling Applied to Space Traffic Management

Authors: Emma Stevenson, Victor Rodriguez-Fernandez, Hodei Urrutxua, Vincent Morand, David Camacho

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 o… ▽ More 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. △ Less

Submitted 11 December, 2023; originally announced December 2023.

Comments: Presented at the 2021 International Association for the Advancement of Space Safety (IAASS) Conf

Journal ref: Proc. 11th International Association for the Advancement of Space Safety (IAASS 2021) Conf

Understanding writing style in social media with a supervised contrastively pre-trained transformer

Authors: Javier Huertas-Tato, Alejandro Martin, David Camacho

Abstract: Online Social Networks serve as fertile ground for harmful behavior, ranging from hate speech to the dissemination of disinformation. Malicious actors now have unprecedented freedom to misbehave, leading to severe societal unrest and dire consequences, as exemplified by events such as the Capitol assault during the US presidential election and the Antivaxx movement during the COVID-19 pandemic. Un… ▽ More Online Social Networks serve as fertile ground for harmful behavior, ranging from hate speech to the dissemination of disinformation. Malicious actors now have unprecedented freedom to misbehave, leading to severe societal unrest and dire consequences, as exemplified by events such as the Capitol assault during the US presidential election and the Antivaxx movement during the COVID-19 pandemic. Understanding online language has become more pressing than ever. While existing works predominantly focus on content analysis, we aim to shift the focus towards understanding harmful behaviors by relating content to their respective authors. Numerous novel approaches attempt to learn the stylistic features of authors in texts, but many of these approaches are constrained by small datasets or sub-optimal training losses. To overcome these limitations, we introduce the Style Transformer for Authorship Representations (STAR), trained on a large corpus derived from public sources of 4.5 x 10^6 authored texts involving 70k heterogeneous authors. Our model leverages Supervised Contrastive Loss to teach the model to minimize the distance between texts authored by the same individual. This author pretext pre-training task yields competitive performance at zero-shot with PAN challenges on attribution and clustering. Additionally, we attain promising results on PAN verification challenges using a single dense layer, with our model serving as an embedding encoder. Finally, we present results from our test partition on Reddit. Using a support base of 8 documents of 512 tokens, we can discern authors from sets of up to 1616 authors with at least 80\% accuracy. We share our pre-trained model at huggingface (https://huggingface.co/AIDA-UPM/star) and our code is available at (https://github.com/jahuerta92/star) △ Less

Submitted 17 October, 2023; originally announced October 2023.

Spain on Fire: A novel wildfire risk assessment model based on image satellite processing and atmospheric information

Authors: Helena Liz-López, Javier Huertas-Tato, Jorge Pérez-Aracil, Carlos Casanova-Mateo, Julia Sanz-Justo, David Camacho

Abstract: Each year, wildfires destroy larger areas of Spain, threatening numerous ecosystems. Humans cause 90% of them (negligence or provoked) and the behaviour of individuals is unpredictable. However, atmospheric and environmental variables affect the spread of wildfires, and they can be analysed by using deep learning. In order to mitigate the damage of these events we proposed the novel Wildfire Asses… ▽ More Each year, wildfires destroy larger areas of Spain, threatening numerous ecosystems. Humans cause 90% of them (negligence or provoked) and the behaviour of individuals is unpredictable. However, atmospheric and environmental variables affect the spread of wildfires, and they can be analysed by using deep learning. In order to mitigate the damage of these events we proposed the novel Wildfire Assessment Model (WAM). Our aim is to anticipate the economic and ecological impact of a wildfire, assisting managers resource allocation and decision making for dangerous regions in Spain, Castilla y León and Andalucía. The WAM uses a residual-style convolutional network architecture to perform regression over atmospheric variables and the greenness index, computing necessary resources, the control and extinction time, and the expected burnt surface area. It is first pre-trained with self-supervision over 100,000 examples of unlabelled data with a masked patch prediction objective and fine-tuned using 311 samples of wildfires. The pretraining allows the model to understand situations, outclassing baselines with a 1,4%, 3,7% and 9% improvement estimating human, heavy and aerial resources; 21% and 10,2% in expected extinction and control time; and 18,8% in expected burnt area. Using the WAM we provide an example assessment map of Castilla y León, visualizing the expected resources over an entire region. △ Less

Submitted 8 June, 2023; originally announced June 2023.

DeepVATS: Deep Visual Analytics for Time Series

Authors: Victor Rodriguez-Fernandez, David Montalvo, Francesco Piccialli, Grzegorz J. Nalepa, David Camacho

Abstract: The field of Deep Visual Analytics (DVA) has recently arisen from the idea of developing Visual Interactive Systems supported by deep learning, in order to provide them with large-scale data processing capabilities and to unify their implementation across different data and domains. In this paper we present DeepVATS, an open-source tool that brings the field of DVA into time series data. DeepVATS… ▽ More The field of Deep Visual Analytics (DVA) has recently arisen from the idea of developing Visual Interactive Systems supported by deep learning, in order to provide them with large-scale data processing capabilities and to unify their implementation across different data and domains. In this paper we present DeepVATS, an open-source tool that brings the field of DVA into time series data. DeepVATS trains, in a self-supervised way, a masked time series autoencoder that reconstructs patches of a time series, and projects the knowledge contained in the embeddings of that model in an interactive plot, from which time series patterns and anomalies emerge and can be easily spotted. The tool includes a back-end for data processing pipeline and model training, as well as a front-end with a interactive user interface. We report on results that validate the utility of DeepVATS, running experiments on both synthetic and real datasets. The code is publicly available on https://github.com/vrodriguezf/deepvats △ Less

Submitted 19 May, 2023; v1 submitted 7 February, 2023; originally announced February 2023.

Comments: Submitted to Elsevier's Knowledge Based Systems journal. Code available at https://github.com/vrodriguezf/deepvats

Journal ref: Knowledge-Based Systems, 277, 2023, p.110793

Countering Malicious Content Moderation Evasion in Online Social Networks: Simulation and Detection of Word Camouflage

Authors: Álvaro Huertas-García, Alejandro Martín, Javier Huertas Tato, David Camacho

Abstract: Content moderation is the process of screening and monitoring user-generated content online. It plays a crucial role in stopping content resulting from unacceptable behaviors such as hate speech, harassment, violence against specific groups, terrorism, racism, xenophobia, homophobia, or misogyny, to mention some few, in Online Social Platforms. These platforms make use of a plethora of tools to de… ▽ More Content moderation is the process of screening and monitoring user-generated content online. It plays a crucial role in stopping content resulting from unacceptable behaviors such as hate speech, harassment, violence against specific groups, terrorism, racism, xenophobia, homophobia, or misogyny, to mention some few, in Online Social Platforms. These platforms make use of a plethora of tools to detect and manage malicious information; however, malicious actors also improve their skills, developing strategies to surpass these barriers and continuing to spread misleading information. Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems. In response to this recent ongoing issue, this paper presents an innovative approach to address this linguistic trend in social networks through the simulation of different content evasion techniques and a multilingual Transformer model for content evasion detection. In this way, we share with the rest of the scientific community a multilingual public tool, named "pyleetspeak" to generate/simulate in a customizable way the phenomenon of content evasion through automatic word camouflage and a multilingual Named-Entity Recognition (NER) Transformer-based model tuned for its recognition and detection. The multilingual NER model is evaluated in different textual scenarios, detecting different types and mixtures of camouflage techniques, achieving an overall weighted F1 score of 0.8795. This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content on social networks, making the fight against information disorders more effective. △ Less

Submitted 27 December, 2022; originally announced December 2022.

Comments: 21 pages, 4 figures, 6 tables, Fast Track from IDEAL 21 Conference

PART: Pre-trained Authorship Representation Transformer

Authors: Javier Huertas-Tato, Alejandro Martin, David Camacho

Abstract: Authors writing documents imprint identifying information within their texts: vocabulary, registry, punctuation, misspellings, or even emoji usage. Previous works use hand-crafted features or classification tasks to train their authorship models, leading to poor performance on out-of-domain authors. Using stylometric representations is more suitable, but this by itself is an open research challeng… ▽ More Authors writing documents imprint identifying information within their texts: vocabulary, registry, punctuation, misspellings, or even emoji usage. Previous works use hand-crafted features or classification tasks to train their authorship models, leading to poor performance on out-of-domain authors. Using stylometric representations is more suitable, but this by itself is an open research challenge. In this paper, we propose PART, a contrastively trained model fit to learn \textbf{authorship embeddings} instead of semantics. We train our model on ~1.5M texts belonging to 1162 literature authors, 17287 blog posters and 135 corporate email accounts; a heterogeneous set with identifiable writing styles. We evaluate the model on current challenges, achieving competitive performance. We also evaluate our model on test splits of the datasets achieving zero-shot 72.39\% accuracy when bounded to 250 authors, a 54\% and 56\% higher than RoBERTa embeddings. We qualitatively assess the representations with different data visualizations on the available datasets, observing features such as gender, age, or occupation of the author. △ Less

Submitted 9 May, 2025; v1 submitted 30 September, 2022; originally announced September 2022.

Deep learning for understanding multilabel imbalanced Chest X-ray datasets

Authors: Helena Liz, Javier Huertas-Tato, Manuel Sánchez-Montañés, Javier Del Ser, David Camacho

Abstract: 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 impossible to understand how the algorithm has achieve… ▽ More 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 impossible to understand how the algorithm has achieved the final result. To apply these algorithms in different fields and test how the methodology works, we need to use eXplainable AI techniques. Most of the work in the medical field focuses on binary or multiclass classification problems. However, in many real-life situations, such as chest X-rays, radiological signs of different diseases can appear at the same time. This gives rise to what is known as "multilabel classification problems". A disadvantage of these tasks is class imbalance, i.e. different labels do not have the same number of samples. The main contribution of this paper is a Deep Learning methodology for imbalanced, multilabel chest X-ray datasets. It establishes a baseline for the currently underutilised PadChest dataset and a new eXplainable AI technique based on heatmaps. This technique also includes probabilities and inter-model matching. The results of our system are promising, especially considering the number of labels used. Furthermore, the heatmaps match the expected areas, i.e. they mark the areas that an expert would use to make the decision. △ Less

Submitted 28 July, 2022; originally announced July 2022.

Modelling stellar activity with Gaussian process regression networks

Authors: J. D. Camacho, J. P. Faria, P. T. P. Viana

Abstract: Stellar photospheric activity is known to limit the detection and characterisation of extra-solar planets. In particular, the study of Earth-like planets around Sun-like stars requires data analysis methods that can accurately model the stellar activity phenomena affecting radial velocity (RV) measurements. Gaussian Process Regression Networks (GPRNs) offer a principled approach to the analysis of… ▽ More Stellar photospheric activity is known to limit the detection and characterisation of extra-solar planets. In particular, the study of Earth-like planets around Sun-like stars requires data analysis methods that can accurately model the stellar activity phenomena affecting radial velocity (RV) measurements. Gaussian Process Regression Networks (GPRNs) offer a principled approach to the analysis of simultaneous time-series, combining the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian Processes. Using HARPS-N solar spectroscopic observations encompassing three years, we demonstrate that this framework is capable of jointly modelling RV data and traditional stellar activity indicators. Although we consider only the simplest GPRN configuration, we are able to describe the behaviour of solar RV data at least as accurately as previously published methods. We confirm the correlation between the RV and stellar activity time series reaches a maximum at separations of a few days, and find evidence of non-stationary behaviour in the time series, associated with an approaching solar activity minimum. △ Less

Submitted 15 December, 2022; v1 submitted 13 May, 2022; originally announced May 2022.

Comments: 30 pages, 23 figures, accepted for publication on MNRAS

Exploring Dimensionality Reduction Techniques in Multilingual Transformers

Authors: Álvaro Huertas-García, Alejandro Martín, Javier Huertas-Tato, David Camacho

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, considera… ▽ More 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 $91.58\% \pm 2.59\%$ and $54.65\% \pm 32.20\%$, 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 △ Less

Submitted 18 April, 2022; originally announced April 2022.

Comments: 22 pages, 4 figures and 8 tables

BERTuit: Understanding Spanish language in Twitter through a native transformer

Authors: Javier Huertas-Tato, Alejandro Martin, David Camacho

Abstract: The appearance of complex attention-based language models such as BERT, Roberta or GPT-3 has allowed to address highly complex tasks in a plethora of scenarios. However, when applied to specific domains, these models encounter considerable difficulties. This is the case of Social Networks such as Twitter, an ever-changing stream of information written with informal and complex language, where each… ▽ More The appearance of complex attention-based language models such as BERT, Roberta or GPT-3 has allowed to address highly complex tasks in a plethora of scenarios. However, when applied to specific domains, these models encounter considerable difficulties. This is the case of Social Networks such as Twitter, an ever-changing stream of information written with informal and complex language, where each message requires careful evaluation to be understood even by humans given the important role that context plays. Addressing tasks in this domain through Natural Language Processing involves severe challenges. When powerful state-of-the-art multilingual language models are applied to this scenario, language specific nuances use to get lost in translation. To face these challenges we present \textbf{BERTuit}, the larger transformer proposed so far for Spanish language, pre-trained on a massive dataset of 230M Spanish tweets using RoBERTa optimization. Our motivation is to provide a powerful resource to better understand Spanish Twitter and to be used on applications focused on this social network, with special emphasis on solutions devoted to tackle the spreading of misinformation in this platform. BERTuit is evaluated on several tasks and compared against M-BERT, XLM-RoBERTa and XLM-T, very competitive multilingual transformers. The utility of our approach is shown with applications, in this case: a zero-shot methodology to visualize groups of hoaxes and profiling authors spreading disinformation. Misinformation spreads wildly on platforms such as Twitter in languages other than English, meaning performance of transformers may suffer when transferred outside English speaking communities. △ Less

Submitted 13 June, 2022; v1 submitted 7 April, 2022; originally announced April 2022.

Comments: Support: 1) BBVA FOUNDATION - CIVIC, 2) Spanish Ministry of Science and Innovation - FightDIS (PID2020-117263GB-100) and XAI-Disinfodemics (PLEC2021-007681), 3) Comunidad Autonoma de Madrid - S2018/TCS-4566, 4) European Comission - IBERIFIER (2020-EU-IA-0252), 5) Digital Future Society (Mobile World Capital Barcelona) - DisTrack, 6) UPM - Programa de Excelencia para el Profesorado Universitario

A Review of Deep Learning-based Approaches for Deepfake Content Detection

Authors: Leandro A. Passos, Danilo Jodas, Kelton A. P. da Costa, Luis A. Souza Júnior, Douglas Rodrigues, Javier Del Ser, David Camacho, João Paulo Papa

Abstract: Recent advancements in deep learning generative models have raised concerns as they can create highly convincing counterfeit images and videos. This poses a threat to people's integrity and can lead to social instability. To address this issue, there is a pressing need to develop new computational models that can efficiently detect forged content and alert users to potential image and video manipu… ▽ More Recent advancements in deep learning generative models have raised concerns as they can create highly convincing counterfeit images and videos. This poses a threat to people's integrity and can lead to social instability. To address this issue, there is a pressing need to develop new computational models that can efficiently detect forged content and alert users to potential image and video manipulations. This paper presents a comprehensive review of recent studies for deepfake content detection using deep learning-based approaches. We aim to broaden the state-of-the-art research by systematically reviewing the different categories of fake content detection. Furthermore, we report the advantages and drawbacks of the examined works, and prescribe several future directions towards the issues and shortcomings still unsolved on deepfake detection. △ Less

Submitted 15 February, 2024; v1 submitted 12 February, 2022; originally announced February 2022.

The EXPRES Stellar Signals Project II. State of the Field in Disentangling Photospheric Velocities

Authors: Lily L. Zhao, Debra A. Fischer, Eric B. Ford, Alex Wise, Michaël Cretignier, Suzanne Aigrain, Oscar Barragan, Megan Bedell, Lars A. Buchhave, João D. Camacho, Heather M. Cegla, Jessi Cisewski-Kehe, Andrew Collier Cameron, Zoe L. de Beurs, Sally Dodson-Robinson, Xavier Dumusque, João P. Faria, Christian Gilbertson, Charlotte Haley, Justin Harrell, David W. Hogg, Parker Holzer, Ancy Anna John, Baptiste Klein, Marina Lafarga , et al. (18 additional authors not shown)

Abstract: Measured spectral shifts due to intrinsic stellar variability (e.g., pulsations, granulation) and activity (e.g., spots, plages) are the largest source of error for extreme precision radial velocity (EPRV) exoplanet detection. Several methods are designed to disentangle stellar signals from true center-of-mass shifts due to planets. The EXPRES Stellar Signals Project (ESSP) presents a self-consist… ▽ More Measured spectral shifts due to intrinsic stellar variability (e.g., pulsations, granulation) and activity (e.g., spots, plages) are the largest source of error for extreme precision radial velocity (EPRV) exoplanet detection. Several methods are designed to disentangle stellar signals from true center-of-mass shifts due to planets. The EXPRES Stellar Signals Project (ESSP) presents a self-consistent comparison of 22 different methods tested on the same extreme-precision spectroscopic data from EXPRES. Methods derived new activity indicators, constructed models for mapping an indicator to the needed RV correction, or separated out shape- and shift-driven RV components. Since no ground truth is known when using real data, relative method performance is assessed using the total and nightly scatter of returned RVs and agreement between the results of different methods. Nearly all submitted methods return a lower RV RMS than classic linear decorrelation, but no method is yet consistently reducing the RV RMS to sub-meter-per-second levels. There is a concerning lack of agreement between the RVs returned by different methods. These results suggest that continued progress in this field necessitates increased interpretability of methods, high-cadence data to capture stellar signals at all timescales, and continued tests like the ESSP using consistent data sets with more advanced metrics for method performance. Future comparisons should make use of various well-characterized data sets -- such as solar data or data with known injected planetary and/or stellar signals -- to better understand method performance and whether planetary signals are preserved. △ Less

Submitted 25 January, 2022; originally announced January 2022.

Comments: 33 pages (+12 pages of Appendix), 10 figures, 8 tables, accepted for publication in AJ

FacTeR-Check: Semi-automated fact-checking through Semantic Similarity and Natural Language Inference

Authors: Alejandro Martín, Javier Huertas-Tato, Álvaro Huertas-García, Guillermo Villar-Rodríguez, David Camacho

Abstract: Our society produces and shares overwhelming amounts of information through Online Social Networks (OSNs). Within this environment, misinformation and disinformation have proliferated, becoming a public safety concern in most countries. Allowing the public and professionals to efficiently find reliable evidences about the factual veracity of a claim is a crucial step to mitigate this harmful sprea… ▽ More Our society produces and shares overwhelming amounts of information through Online Social Networks (OSNs). Within this environment, misinformation and disinformation have proliferated, becoming a public safety concern in most countries. Allowing the public and professionals to efficiently find reliable evidences about the factual veracity of a claim is a crucial step to mitigate this harmful spread. To this end, we propose FacTeR-Check, a multilingual architecture for semi-automated fact-checking that can be used for either applications designed for the general public and by fact-checking organisations. FacTeR-Check enables retrieving fact-checked information, unchecked claims verification and tracking dangerous information over social media. This architectures involves several modules developed to evaluate semantic similarity, to calculate natural language inference and to retrieve information from Online Social Networks. The union of all these components builds a semi-automated fact-checking tool able of verifying new claims, to extract related evidence, and to track the evolution of a hoax on a OSN. While individual modules are validated on related benchmarks (mainly MSTS and SICK), the complete architecture is validated using a new dataset called NLI19-SP that is publicly released with COVID-19 related hoaxes and tweets from Spanish social media. Our results show state-of-the-art performance on the individual benchmarks, as well as producing a useful analysis of the evolution over time of 61 different hoaxes. △ Less

Submitted 16 February, 2022; v1 submitted 27 October, 2021; originally announced October 2021.

SWEET-Cat 2.0: The Cat just got SWEETer; Higher quality spectra and precise parallaxes from GAIA eDR3

Authors: S. G. Sousa, V. Adibekyan, E. Delgado-Mena, N. C. Santos, B. Rojas-Ayala, B. M. T. B. Soares, H. Legoinha, S. Ulmer-Moll, J. D. Camacho, S. C. C. Barros, O. D. S. Demangeon, S. Hoyer, G. Israelian, A. Mortier, M. Tsantaki, M. Monteiro

Abstract: Aims. The catalog of Stars With ExoplanETs (SWEET-Cat) was originally introduced in 2013. Since then many more exoplanets have been confirmed, increasing significantly the number of host stars listed there. A crucial step toward a comprehensive understanding of these new worlds is the precise and homogeneous characterization of their host stars. Better spectroscopic stellar parameters along with n… ▽ More Aims. The catalog of Stars With ExoplanETs (SWEET-Cat) was originally introduced in 2013. Since then many more exoplanets have been confirmed, increasing significantly the number of host stars listed there. A crucial step toward a comprehensive understanding of these new worlds is the precise and homogeneous characterization of their host stars. Better spectroscopic stellar parameters along with new results from Gaia eDR3 provide updated and precise parameters for the discovered planets. A new version of the catalog, whose homogeneity in the derivation of the parameters is key to unraveling star-planet connections, is available to the community. Methods. We made use of high-resolution spectra for planet-host stars, either observed by our team or collected through public archives. The spectroscopic stellar parameters were derived for the spectra following the same homogeneous process using ARES and MOOG (ARES+MOOG) as for the previous SWEET-Cat releases. We re-derived parameters for the stars in the catalog using better quality spectra and/or using the most recent versions of the codes. Moreover, the new SWEET-Cat table can now be more easily combined with the planet properties listed both at the Extrasolar Planets Encyclopedia and at the NASA exoplanet archive to perform statistical analyses of exoplanets. We also made use of the recent GAIA eDR3 parallaxes and respective photometry to derive consistent and accurate surface gravity values for the host stars. Results. We increased the number of stars with homogeneous parameters by more than 40\% (from 645 to 928). We reviewed and updated the metallicity distributions of stars hosting planets with different mass regimes comparing the low-mass planets (< 30M$_{\oplus}$) with the high-mass planets. The new data strengthen previous results showing the possible trend in the metallicity-period-mass diagram for low-mass planets. △ Less

Submitted 10 September, 2021; originally announced September 2021.

Comments: 14 pages, 10 figures, accepted for A&A

Journal ref: A&A 656, A53 (2021)

Warm terrestrial planet with half the mass of Venus transiting a nearby star

Authors: Olivier D. S. Demangeon, M. R. Zapatero Osorio, Y. Alibert, S. C. C. Barros, V. Adibekyan, H. M. Tabernero, A. Antoniadis-Karnavas, J. D. Camacho, A. Suárez Mascareño, M. Oshagh, G. Micela, S. G. Sousa, C. Lovis, F. A. Pepe, R. Rebolo, S. Cristiani, N. C. Santos, R. Allart, C. Allende Prieto, D. Bossini, F. Bouchy, A. Cabral, M. Damasso, P. Di Marcantonio, V. D'Odorico , et al. (20 additional authors not shown)

Abstract: The advent of a new generation of radial velocity instruments has allowed us to break the one Earth-mass barrier. We report a new milestone in this context with the detection of the lowest-mass planet measured so far using radial velocities: L 98-59 b, a rocky planet with half the mass of Venus. It is part of a system composed of three known transiting terrestrial planets (planets b to d). We anno… ▽ More The advent of a new generation of radial velocity instruments has allowed us to break the one Earth-mass barrier. We report a new milestone in this context with the detection of the lowest-mass planet measured so far using radial velocities: L 98-59 b, a rocky planet with half the mass of Venus. It is part of a system composed of three known transiting terrestrial planets (planets b to d). We announce the discovery of a fourth nontransiting planet with a minimum mass of 3.06_{-0.37}^{+0.33} MEarth and an orbital period of 12.796_{-0.019}^{+0.020} days and report indications for the presence of a fifth nontransiting terrestrial planet. With a minimum mass of 2.46_{-0.82}^{+0.66} MEarth and an orbital period 23.15_{-0.17}^{+0.60} days, this planet, if confirmed, would sit in the middle of the habitable zone of the L 98-59 system. L 98-59 is a bright M dwarf located 10.6 pc away. Positioned at the border of the continuous viewing zone of the James Webb Space Telescope, this system is destined to become a corner stone for comparative exoplanetology of terrestrial planets. The three transiting planets have transmission spectrum metrics ranging from 49 to 255, which makes them prime targets for an atmospheric characterization with the James Webb Space Telescope, the Hubble Space Telescope, Ariel, or ground-based facilities such as NIRPS or ESPRESSO. With an equilibrium temperature ranging from 416 to 627 K, they offer a unique opportunity to study the diversity of warm terrestrial planets. L 98-59 b and c have densities of 3.6_{-1.5}^{+1.4} and 4.57_{-0.85}^{+0.77} g.cm^{-3}, respectively, and have very similar bulk compositions with a small iron core that represents only 12 to 14 % of the total mass, and a small amount of water. However, with a density of 2.95_{-0.51}^{+0.79} g.cm^{-3} and despite a similar core mass fraction, up to 30 % of the mass of L 98-59 d might be water. △ Less

Submitted 6 August, 2021; originally announced August 2021.

Comments: 38 pages, 23 figures, Accepted for publication in Astronomy & Astrophysics

Journal ref: A&A 653, A41 (2021)

The SOPHIE search for northern extrasolar planets. XVIII: Six new cold Jupiters, including one of the most eccentric exoplanet orbits

Authors: O. D. S. Demangeon, S. Dalal, G. Hébrard, B. Nsamba, F. Kiefer, J. D. Camacho, J. Sahlmann, L. Arnold, N. Astudillo-Defru, X. Bonfils, I. Boisse, F. Bouchy, V. Bourrier, T. Campante, X. Delfosse, M. Deleuil, R. F. Díaz, J. Faria, T. Forveille, N. Hara, N. Heidari, M. J. Hobson, T. Lopez, C. Moutou, J. Rey , et al. (6 additional authors not shown)

Abstract: Context. Due to their low transit probability, the long-period planets are, as a population, only partially probed by transit surveys. Radial velocity surveys thus have a key role to play, in particular for giant planets. Cold Jupiters induce a typical radial velocity semi-amplitude of 10m.s^{-1}, which is well within the reach of multiple instruments that have now been in operation for more than… ▽ More Context. Due to their low transit probability, the long-period planets are, as a population, only partially probed by transit surveys. Radial velocity surveys thus have a key role to play, in particular for giant planets. Cold Jupiters induce a typical radial velocity semi-amplitude of 10m.s^{-1}, which is well within the reach of multiple instruments that have now been in operation for more than a decade. Aims. We take advantage of the ongoing radial velocity survey with the sophie high-resolution spectrograph, which continues the search started by its predecessor elodie to further characterize the cold Jupiter population. Methods. Analyzing the radial velocity data from six bright solar-like stars taken over a period of up to 15 years, we attempt the detection and confirmation of Keplerian signals. Results. We announce the discovery of six planets, one per system, with minimum masses in the range 2.99-8.3 Mjup and orbital periods between 200 days and 10 years. The data do not provide enough evidence to support the presence of additional planets in any of these systems. The analysis of stellar activity indicators confirms the planetary nature of the detected signals. Conclusions. These six planets belong to the cold and massive Jupiter population, and four of them populate its eccentric tail. In this respect, HD 80869 b stands out as having one of the most eccentric orbits, with an eccentricity of 0.862^{+0.028}_{-0.018}. These planets can thus help to better constrain the migration and evolution processes at play in the gas giant population. Furthermore, recent works presenting the correlation between small planets and cold Jupiters indicate that these systems are good candidates to search for small inner planets. △ Less

Submitted 13 July, 2021; v1 submitted 11 July, 2021; originally announced July 2021.

Comments: 24 pages, 12 figures, Accepted for publication in Astronomy & Astrophysics

Journal ref: A&A 653, A78 (2021)

Relationships Between Mutations of Brauer Configuration Algebras and Some Diophantine Equations

Authors: Agustín Moreno Cañadas, Juan David Camacho, Isaías David Marín Gaviria

Abstract: Mutations on Brauer configurations are introduced and associated with some suitable automata in order to solve generalizations of the Chicken McNugget problem. Besides, based on marked order polytopes a new class of diophantine equations called Gelfand-Tsetlin equations are also solved. The approach allows giving an algebraic description of the schedule of an AES key via some suitable non-determin… ▽ More Mutations on Brauer configurations are introduced and associated with some suitable automata in order to solve generalizations of the Chicken McNugget problem. Besides, based on marked order polytopes a new class of diophantine equations called Gelfand-Tsetlin equations are also solved. The approach allows giving an algebraic description of the schedule of an AES key via some suitable non-deterministic finite automata (NFA). △ Less

Submitted 24 May, 2021; originally announced May 2021.

A survey on extremism analysis using Natural Language Processing

Authors: Javier Torregrosa, Gema Bello-Orgaz, Eugenio Martinez-Camara, Javier Del Ser, David Camacho

Abstract: Extremism research has grown as an open problem for several countries during recent years, especially due to 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. Natural Language Processing (NLP) represents a way of detecting th… ▽ More Extremism research has grown as an open problem for several countries during recent years, especially due to 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. Natural Language Processing (NLP) represents a way of detecting this type of content, and several authors make use of it to describe and discriminate the discourse held by this groups, with the final objective of detecting and preventing its spread. This survey aims to review the contributions of NLP to the field of extremism research, providing the reader with a comprehensive picture of the state of the art of this research area. The content includes a description and comparison of the frequently used NLP techniques, how they were applied, the insights they provided, the most frequently used NLP software tools and the availability of datasets and data sources for research. Finally, research questions are approached and answered with highlights from the review, while future trends, challenges and directions derived from these highlights are suggested. △ Less

Submitted 21 April, 2021; v1 submitted 28 March, 2021; originally announced April 2021.

SILT: Efficient transformer training for inter-lingual inference

Authors: Javier Huertas-Tato, Alejandro Martín, David Camacho

Abstract: The ability of transformers to perform precision tasks such as question answering, Natural Language Inference (NLI) or summarising, have enabled them to be ranked as one of the best paradigm to address Natural Language Processing (NLP) tasks. NLI is one of the best scenarios to test these architectures, due to the knowledge required to understand complex sentences and established relationships bet… ▽ More The ability of transformers to perform precision tasks such as question answering, Natural Language Inference (NLI) or summarising, have enabled them to be ranked as one of the best paradigm to address Natural Language Processing (NLP) tasks. NLI is one of the best scenarios to test these architectures, due to the knowledge required to understand complex sentences and established relationships between a hypothesis and a premise. Nevertheless, these models suffer from incapacity to generalise to other domains or difficulties to face multilingual and interlingual scenarios. The leading pathway in the literature to address these issues involve designing and training extremely large architectures, which leads to unpredictable behaviours and to establish barriers which impede broad access and fine tuning. In this paper, we propose a new architecture called Siamese Inter-Lingual Transformer (SILT), to efficiently align multilingual embeddings for Natural Language Inference, allowing for unmatched language pairs to be processed. SILT leverages siamese pre-trained multi-lingual transformers with frozen weights where the two input sentences attend each other to later be combined through a matrix alignment method. The experimental results carried out in this paper evidence that SILT allows to reduce drastically the number of trainable parameters while allowing for inter-lingual NLI and achieving state-of-the-art performance on common benchmarks. We make our code and dataset available at https://github.com/jahuerta92/siamese-inter-lingual-transformer. △ Less

Submitted 17 May, 2021; v1 submitted 17 March, 2021; originally announced March 2021.

Comments: This research is funded by the project CIVIC: Intelligent characterisation of the veracity of the information related to COVID-19, granted by BBVA FOUNDATION GRANTS FOR SCIENTIFIC RESEARCH TEAMS SARS-CoV-2 and COVID-19

Fusing CNNs and statistical indicators to improve image classification

Authors: Javier Huertas-Tato, Alejandro Martín, Julián Fierrez, David Camacho

Abstract: Convolutional Networks have dominated the field of computer vision for the last ten years, exhibiting extremely powerful feature extraction capabilities and outstanding classification performance. The main strategy to prolong this trend relies on further upscaling networks in size. However, costs increase rapidly while performance improvements may be marginal. We hypothesise that adding heterogene… ▽ More Convolutional Networks have dominated the field of computer vision for the last ten years, exhibiting extremely powerful feature extraction capabilities and outstanding classification performance. The main strategy to prolong this trend relies on further upscaling networks in size. However, costs increase rapidly while performance improvements may be marginal. We hypothesise that adding heterogeneous sources of information may be more cost-effective to a CNN than building a bigger network. In this paper, an ensemble method is proposed for accurate image classification, fusing automatically detected features through Convolutional Neural Network architectures with a set of manually defined statistical indicators. Through a combination of the predictions of a CNN and a secondary classifier trained on statistical features, better classification performance can be cheaply achieved. We test multiple learning algorithms and CNN architectures on a diverse number of datasets to validate our proposal, making public all our code and data via GitHub. According to our results, the inclusion of additional indicators and an ensemble classification approach helps to increase the performance in 8 of 9 datasets, with a remarkable increase of more than 10% precision in two of them. △ Less

Submitted 4 June, 2021; v1 submitted 20 December, 2020; originally announced December 2020.

Comments: 16 pages

Ensembles of Convolutional Neural Networks models for pediatric pneumonia diagnosis

Authors: Helena Liz, Manuel Sánchez-Montañés, Alfredo Tagarro, Sara Domínguez-Rodríguez, Ron Dagan, David Camacho

Abstract: Pneumonia is a lung infection that causes 15% of childhood mortality, over 800,000 children under five every year, all over the world. This pathology is mainly caused by viruses or bacteria. X-rays imaging analysis is one of the most used methods for pneumonia diagnosis. These clinical images can be analyzed using machine learning methods such as convolutional neural networks (CNN), which learn to… ▽ More Pneumonia is a lung infection that causes 15% of childhood mortality, over 800,000 children under five every year, all over the world. This pathology is mainly caused by viruses or bacteria. X-rays imaging analysis is one of the most used methods for pneumonia diagnosis. These clinical images can be analyzed using machine learning methods such as convolutional neural networks (CNN), which learn to extract critical features for the classification. However, the usability of these systems is limited in medicine due to the lack of interpretability, because of these models cannot be used to generate an understandable explanation (from a human-based perspective), about how they have reached those results. Another problem that difficults the impact of this technology is the limited amount of labeled data in many medicine domains. The main contributions of this work are two fold: the first one is the design of a new explainable artificial intelligence (XAI) technique based on combining the individual heatmaps obtained from each model in the ensemble. This allows to overcome the explainability and interpretability problems of the CNN "black boxes", highlighting those areas of the image which are more relevant to generate the classification. The second one is the development of new ensemble deep learning models to classify chest X-rays that allow highly competitive results using small datasets for training. We tested our ensemble model using a small dataset of pediatric X-rays (950 samples) with low quality and anatomical variability (which represents one of the biggest challenges). We also tested other strategies such as single CNNs trained from scratch and transfer learning using CheXNet. Our results show that our ensemble model outperforms these strategies obtaining highly competitive results. Finally, we confirmed the robustness of our approach using another pneumonia diagnosis dataset [1]. △ Less

Submitted 4 June, 2021; v1 submitted 29 September, 2020; originally announced October 2020.

The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools

Authors: David Camacho, Àngel Panizo-LLedot, Gema Bello-Orgaz, Antonio Gonzalez-Pardo, Erik Cambria

Abstract: Social network based applications have experienced exponential growth in recent years. One of the reasons for this rise is that this application domain offers a particularly fertile place to test and develop the most advanced computational techniques to extract valuable information from the Web. The main contribution of this work is three-fold: (1) we provide an up-to-date literature review of the… ▽ More Social network based applications have experienced exponential growth in recent years. One of the reasons for this rise is that this application domain offers a particularly fertile place to test and develop the most advanced computational techniques to extract valuable information from the Web. The main contribution of this work is three-fold: (1) we provide an up-to-date literature review of the state of the art on social network analysis (SNA);(2) we propose a set of new metrics based on four essential features (or dimensions) in SNA; (3) finally, we provide a quantitative analysis of a set of popular SNA tools and frameworks. We have also performed a scientometric study to detect the most active research areas and application domains in this area. This work proposes the definition of four different dimensions, namely Pattern & Knowledge discovery, Information Fusion & Integration, Scalability, and Visualization, which are used to define a set of new metrics (termed degrees) in order to evaluate the different software tools and frameworks of SNA (a set of 20 SNA-software tools are analyzed and ranked following previous metrics). These dimensions, together with the defined degrees, allow evaluating and measure the maturity of social network technologies, looking for both a quantitative assessment of them, as to shed light to the challenges and future trends in this active area. △ Less

Submitted 21 February, 2020; originally announced February 2020.

Comments: This paper is currently under evaluation in Information Fusion journal

sKPNSGA-II: Knee point based MOEA with self-adaptive angle for Mission Planning Problems

Authors: Cristian Ramirez-Atencia, Sanaz Mostaghim, David Camacho

Abstract: Real-world and complex problems have usually many objective functions that have to be optimized all at once. Over the last decades, Multi-Objective Evolutionary Algorithms (MOEAs) are designed to solve this kind of problems. Nevertheless, some problems have many objectives which lead to a large number of non-dominated solutions obtained by the optimization algorithms. The large set of non-dominate… ▽ More Real-world and complex problems have usually many objective functions that have to be optimized all at once. Over the last decades, Multi-Objective Evolutionary Algorithms (MOEAs) are designed to solve this kind of problems. Nevertheless, some problems have many objectives which lead to a large number of non-dominated solutions obtained by the optimization algorithms. The large set of non-dominated solutions hinders the selection of the most appropriate solution by the decision maker. This paper presents a new algorithm that has been designed to obtain the most significant solutions from the Pareto Optimal Frontier (POF). This approach is based on the cone-domination applied to MOEA, which can find the knee point solutions. In order to obtain the best cone angle, we propose a hypervolume-distribution metric, which is used to self-adapt the angle during the evolving process. This new algorithm has been applied to the real world application in Unmanned Air Vehicle (UAV) Mission Planning Problem. The experimental results show a significant improvement of the algorithm performance in terms of hypervolume, number of solutions, and also the required number of generations to converge. △ Less

Submitted 20 February, 2020; originally announced February 2020.

Comments: Submitted to Applied Soft Computing

Privacy in Data Service Composition

Authors: Mahmoud Barhamgi, Charith Perera, Chia-Mu Yu, Djamal Benslimane, David Camacho, Christine Bonnet

Abstract: In modern information systems different information features, about the same individual, are often collected and managed by autonomous data collection services that may have different privacy policies. Answering many end-users' legitimate queries requires the integration of data from multiple such services. However, data integration is often hindered by the lack of a trusted entity, often called a… ▽ More In modern information systems different information features, about the same individual, are often collected and managed by autonomous data collection services that may have different privacy policies. Answering many end-users' legitimate queries requires the integration of data from multiple such services. However, data integration is often hindered by the lack of a trusted entity, often called a mediator, with which the services can share their data and delegate the enforcement of their privacy policies. In this paper, we propose a flexible privacy-preserving data integration approach for answering data integration queries without the need for a trusted mediator. In our approach, services are allowed to enforce their privacy policies locally. The mediator is considered to be untrusted, and only has access to encrypted information to allow it to link data subjects across the different services. Services, by virtue of a new privacy requirement, dubbed k-Protection, limiting privacy leaks, cannot infer information about the data held by each other. End-users, in turn, have access to privacy-sanitized data only. We evaluated our approach using an example and a real dataset from the healthcare application domain. The results are promising from both the privacy preservation and the performance perspectives. △ Less

Submitted 3 January, 2020; originally announced January 2020.

Decoding the radial velocity variations of HD41248 with ESPRESSO

Authors: J. P. Faria, V. Adibekyan, E. M. Amazo-Gómez, S. C. C. Barros, J. D. Camacho, O. Demangeon, P. Figueira, A. Mortier, M. Oshagh, F. Pepe, N. C. Santos, J. Gomes da Silva, A. R. Costa Silva, S. G. Sousa, S. Ulmer-Moll, P. T. P. Viana

Abstract: Twenty-four years after the discoveries of the first exoplanets, the radial-velocity (RV) method is still one of the most productive techniques to detect and confirm exoplanets. But stellar magnetic activity can induce RV variations large enough to make it difficult to disentangle planet signals from the stellar noise. In this context, HD41248 is an interesting planet-host candidate, with RV obser… ▽ More Twenty-four years after the discoveries of the first exoplanets, the radial-velocity (RV) method is still one of the most productive techniques to detect and confirm exoplanets. But stellar magnetic activity can induce RV variations large enough to make it difficult to disentangle planet signals from the stellar noise. In this context, HD41248 is an interesting planet-host candidate, with RV observations plagued by activity-induced signals. We report on ESPRESSO observations of HD41248 and analyse them together with previous observations from HARPS with the goal of evaluating the presence of orbiting planets. Using different noise models within a general Bayesian framework designed for planet detection in RV data, we test the significance of the various signals present in the HD41248 dataset. We use Gaussian processes as well as a first-order moving average component to try to correct for activity-induced signals. At the same time, we analyse photometry from the TESS mission, searching for transits and rotational modulation in the light curve. The number of significantly detected Keplerian signals depends on the noise model employed, which can range from 0 with the Gaussian process model to 3 with a white noise model. We find that the Gaussian process alone can explain the RV data while allowing for the stellar rotation period and active region evolution timescale to be constrained. The rotation period estimated from the RVs agrees with the value determined from the TESS light curve. Based on the data that is currently available, we conclude that the RV variations of HD41248 can be explained by stellar activity (using the Gaussian process model) in line with the evidence from activity indicators and the TESS photometry. △ Less

Submitted 26 November, 2019; originally announced November 2019.

Comments: Accepted for publication in A&A

Journal ref: A&A 635, A13 (2020)