Search Results for author:
Found 23 papers, 6 papers with code
AIDA-UPM at SemEval-2022 Task 5: Exploring Multimodal Late Information Fusion for Multimedia Automatic Misogyny Identification
The main contribution of this paper is the exploration of different late fusion methods to boost the performance of the combination based on the Transformer-based model and Convolutional Neural Networks (CNN) for text and image, respectively.
Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning
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.
A revision on Multi-Criteria Decision Making methods for Multi-UAV Mission Planning Support
In this work, a DSS consisting of ranking and filtering systems, which order and reduce the optimal solutions, has been designed.
An Effective Networks Intrusion Detection Approach Based on Hybrid Harris Hawks and Multi-Layer Perceptron
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.
Camouflage is all you need: Evaluating and Enhancing Language Model Robustness Against Camouflage Adversarial Attacks
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.
Constrained multi-objective optimization for multi-UAV planning
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.
Solving Complex Multi-UAV Mission Planning Problems using Multi-objective Genetic Algorithms
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.
Self-supervised Machine Learning Based Approach to Orbit Modelling Applied to Space Traffic Management
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.
Understanding writing style in social media with a supervised contrastively pre-trained transformer
Additionally, we attain promising results on PAN verification challenges using a single dense layer, with our model serving as an embedding encoder.
Spain on Fire: A novel wildfire risk assessment model based on image satellite processing and atmospheric information
Each year, wildfires destroy larger areas of Spain, threatening numerous ecosystems.
DeepVATS: Deep Visual Analytics for Time Series
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.
Countering Malicious Content Moderation Evasion in Online Social Networks: Simulation and Detection of Word Camouflage
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.
Multilingual Named Entity Recognition
named-entity-recognition
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PART: Pre-trained Authorship Representation Transformer
The model is evaluated on these datasets, achieving zero-shot 72. 39\% and 86. 73\% accuracy and top-5 accuracy respectively on the joint evaluation dataset when determining authorship from a set of 250 different authors.
Deep learning for understanding multilabel imbalanced Chest X-ray datasets
To apply these algorithms in different fields and test how the methodology works, we need to use eXplainable AI techniques.
Exploring Dimensionality Reduction Techniques in Multilingual Transformers
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
BERTuit: Understanding Spanish language in Twitter through a native transformer
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.
A Review of Deep Learning-based Approaches for Deepfake Content Detection
Recent advancements in deep learning generative models have raised concerns as they can create highly convincing counterfeit images and videos.
FacTeR-Check: Semi-automated fact-checking through Semantic Similarity and Natural Language Inference
Our society produces and shares overwhelming amounts of information through Online Social Networks (OSNs).
SILT: Efficient transformer training for inter-lingual inference
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.
Fusing CNNs and statistical indicators to improve image classification
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.
Ensembles of Convolutional Neural Networks models for pediatric pneumonia diagnosis
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.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools
Social network based applications have experienced exponential growth in recent years.
sKPNSGA-II: Knee point based MOEA with self-adaptive angle for Mission Planning Problems
Nevertheless, some problems have many objectives which lead to a large number of non-dominated solutions obtained by the optimization algorithms.
