Modelado e implementación de algoritmos de movimiento para robots holonómicos de tres ruedas

Este trabajo ha consistido en el desarrollo e implementación de un modelo de control para el movimiento de un robot omnidireccional de estructura rígida con tres ruedas. Ha tenido como objetivo que el robot autorregule su desplazamiento, y no haya que hacer nada más que indicarle el punto de destino. Tras la introducción en los dos primeros capítulos, donde se presenta el tema y se exponen los principales conceptos relacionados con el modelado de algoritmos para el movimiento de robots móviles, en las secciones de diseño y de desarrollo se describe en profundidad el proyecto realizado. Es en la sección de diseño donde se explican los modelos que subyacen bajo las funciones. Éstos son dos: el modelo cinemático inverso, que con la geometría del robot determina la velocidad de cada rueda, para moverse hasta la posición destino; y el modelo de los motores, que controla el funcionamiento de éstos y los regula según sea necesario. En el desarrollo se analizan las cuestiones surgidas a lo largo de toda la elaboración del proyecto. Que son, la concepción de la máquina de estados para medir la velocidad de las ruedas, determinar la frecuencia más adecuada para computar la velocidad, cómo calcular la función de transferencia y la adaptación del paper. Finalmente se describen las pruebas llevadas a cabo, junto con la discusión de los resultados, y la conclusión, donde se comentan las carencias del modelo utilizado. Los resultados obtenidos han obligado a la realización de cambios para un correcto funcionamiento, dichos cambios se aclaran en esa misma sección

IFBiD: Inference-Free Bias Detection

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)This paper is the first to explore an automatic way to detect bias in deep convolutional neural networks by simply look- ing at their weights. Furthermore, it is also a step towards understanding neural networks and how they work. We show that it is indeed possible to know if a model is biased or not simply by looking at its weights, without the model infer- ence for an specific input. We analyze how bias is encoded in the weights of deep networks through a toy example using the Colored MNIST database and we also provide a realistic case study in gender detection from face images using state- of-the-art methods and experimental resources. To do so, we generated two databases with 36K and 48K biased models each. In the MNIST models we were able to detect whether they presented a strong or low bias with more than 99% ac- curacy, and we were also able to classify between four levels of bias with more than 70% accuracy. For the face models, we achieved 90% accuracy in distinguishing between models biased towards Asian, Black, or Caucasian ethnicityThis work has been supported by projects: TRESPASSETN (MSCA-ITN-2019-860813), PRIMA (MSCAITN-2019-860315), BIBECA (RTI2018-101248-B-I00 MINECO/FEDER), and BB for TAI (PID2021-127641OB-I00 MICINN/FEDER). I. Serna is supported by a FPI fellowship from UAM

Estudios de marcado y recaptura de especies marinas

Los resultados obtenidos del marcado y posterior recaptura de los ejemplares son una herramienta muy valiosa para contribuir a mejorar el conocimiento de la biología y ecología de una especie, examinando ciertos aspectos como son: el crecimiento, los movimientos o migraciones, la mortalidad o supervivencia, la abundancia y distribución de la especie, el hábitat y diferenciación de poblaciones o stocks. Actualmente la técnica de marcado se aplica a muchas especies, tanto terrestres como marinas, pertenecientes a diversos grupos zoológicos: peces, crustáceos, reptiles, moluscos y mamíferos. Este libro repasa algunos ejemplos de marcado de especies marinas de interés comercial. No todas las especies pueden ser marcadas, porque es necesario cumplir una serie de requisitos para poder llevar a cabo con éxito un experimento de marcado. En uno de los apartados de esta guía, se describen los distintos aspectos a tener en cuenta para obtener buenos resultados. Se describen los principales proyectos de marcado actualmente en ejecución o en marcha llevados a cabo por el Instituto Español de Oceanografía (IEO). En primer lugar, se describe brevemente la especie, su distribución, crecimiento, reproducción, alimentación, etc. A continuación, se presenta la información del marcado, es decir, campañas realizadas, número de ejemplares marcados y algunos de los resultados obtenidos hasta la fecha a partir de las recapturas disponibles. En algunas especies, los programas de marcado se llevan realizando desde hace más de 20 años, como es el caso del atún rojo, por lo que la información disponible es bastante amplia. En otros casos por el contrario como la merluza, los proyectos son relativamente recientes, no obstante los resultados son bastante interesantes y prometedores.Nowadays many different marine animals are being tagged. This book summarizes recent tagging programs carried out by the Spanish Institute of Oceanography (IEO). Although the objectives of these various studies mainly depend on the species and each project in particular, the general aim is to better understand the biology and ecology of these animals the structure and dynamics of their populations and their capacity to respond to human activities. This book provides an overview of different aspects of this technique such as a brief history of tagging, the types of tags currently used, including both conventional and electronic tags, where and how to put them on the marine animals, some recommendations regarding how to perform a tagging survey and where to go or what to do if anyone recovers a tagged fish or marine animal. The book then summarizes the main species tagged by the IEO, making a short description of their biology followed by some of the results obtained from tagging studies undertaken until now. Other applications are to know the spatial distribution (spawning or feeding areas), estimate growth parameters, mortality and survival rates, longevity, the size of the population or identifying stocks. Nowadays the advances in electronics have also open new fields such us the possibility of tracking an animal and knowing its habitat preferences and behaviour. Besides some of these tags have the capacity of recording this information during long periods and sending the data from long distances even without the need to recover the animal. Tagging activities constitute a very useful tool to improve the knowledge of many species and contribute to their management and conservation. For that reason this methodology is included in many IEO projects in which other activities like the monitoring of the fishery (landings, fishing effort, fleet characteristics, fishing areas, biological sampling, etc.) are carried out. Some projects are related with coastal pelagic fisheries including anchovy, sardine and mackerel or oceanic pelagic fisheries like tuna and billfish species and pelagic sharks. Others are focused on benthic and demersal species such as hake, black spot seabream, anglerfish, flatfish, etc. Nevertheless not all species can be tagged, as they have to survive being caught and handled before being release. For this reason, tagging techniques may not easily be applied to some species

Estudios de marcado y recaptura de especies marinas

Los resultados obtenidos del marcado y posterior recaptura de los ejemplares son una herramienta muy valiosa para contribuir a mejorar el conocimiento de la biología y ecología de una especie, examinando ciertos aspectos como son: el crecimiento, los movimientos o migraciones, la mortalidad o supervivencia, la abundancia y distribución de la especie, el hábitat y diferenciación de poblaciones o stocks. Actualmente la técnica de marcado se aplica a muchas especies, tanto terrestres como marinas, pertenecientes a diversos grupos zoológicos: peces, crustáceos, reptiles, moluscos y mamíferos. Este libro repasa algunos ejemplos de marcado de especies marinas de interés comercial. No todas las especies pueden ser marcadas, porque es necesario cumplir una serie de requisitos para poder llevar a cabo con éxito un experimento de marcado. En uno de los apartados de esta guía, se describen los distintos aspectos a tener en cuenta para obtener buenos resultados. Se describen los principales proyectos de marcado actualmente en ejecución o en marcha llevados a cabo por el Instituto Español de Oceanografía (IEO). En primer lugar, se describe brevemente la especie, su distribución, crecimiento, reproducción, alimentación, etc. A continuación, se presenta la información del marcado, es decir, campañas realizadas, número de ejemplares marcados y algunos de los resultados obtenidos hasta la fecha a partir de las recapturas disponibles. En algunas especies, los programas de marcado se llevan realizando desde hace más de 20 años, como es el caso del atún rojo, por lo que la información disponible es bastante amplia. En otros casos por el contrario como la merluza, los proyectos son relativamente recientes, no obstante los resultados son bastante interesantes y prometedores.Nowadays many different marine animals are being tagged. This book summarizes recent tagging programs carried out by the Spanish Institute of Oceanography (IEO). Although the objectives of these various studies mainly depend on the species and each project in particular, the general aim is to better understand the biology and ecology of these animals the structure and dynamics of their populations and their capacity to respond to human activities. This book provides an overview of different aspects of this technique such as a brief history of tagging, the types of tags currently used, including both conventional and electronic tags, where and how to put them on the marine animals, some recommendations regarding how to perform a tagging survey and where to go or what to do if anyone recovers a tagged fish or marine animal. The book then summarizes the main species tagged by the IEO, making a short description of their biology followed by some of the results obtained from tagging studies undertaken until now. Other applications are to know the spatial distribution (spawning or feeding areas), estimate growth parameters, mortality and survival rates, longevity, the size of the population or identifying stocks. Nowadays the advances in electronics have also open new fields such us the possibility of tracking an animal and knowing its habitat preferences and behaviour. Besides some of these tags have the capacity of recording this information during long periods and sending the data from long distances even without the need to recover the animal. Tagging activities constitute a very useful tool to improve the knowledge of many species and contribute to their management and conservation. For that reason this methodology is included in many IEO projects in which other activities like the monitoring of the fishery (landings, fishing effort, fleet characteristics, fishing areas, biological sampling, etc.) are carried out. Some projects are related with coastal pelagic fisheries including anchovy, sardine and mackerel or oceanic pelagic fisheries like tuna and billfish species and pelagic sharks. Others are focused on benthic and demersal species such as hake, black spot seabream, anglerfish, flatfish, etc. Nevertheless not all species can be tagged, as they have to survive being caught and handled before being release. For this reason, tagging techniques may not easily be applied to some species.Versión del edito

Comprendiendo el sesgo a Mmúltiples niveles en algoritmos de visión artificial con aplicación al Reconocimiento Facial

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de Lectura: 16-01-202

FairCVtest Demo: Understanding Bias in Multimodal Learning with a Testbed in Fair Automatic Recruitment

© ACM 2020. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ICMI '20: Proceedings of the 2020 International Conference on Multimodal Interaction, https://doi.org/10.1145/3382507.3421165With the aim of studying how current multimodal AI algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, this demonstrator experiments over an automated recruitment testbed based on Curriculum Vitae: FairCVtest. The presence of decision-making algorithms in society is rapidly increasing nowadays, while concerns about their transparency and the possibility of these algorithms becoming new sources of discrimination are arising. This demo shows the capacity of the Artificial Intelligence (AI) behind a recruitment tool to extract sensitive information from unstructured data, and exploit it in combination to data biases in undesirable (unfair) ways. Aditionally, the demo includes a new algorithm (SensitiveNets) for discrimination-aware learning which eliminates sensitive information in our multimodal AI frameworkThis work has been supported by projects: BIBECA (RTI2018-101248-B-I00 from MINECO/FEDER), TRESPASS-ETN (H2020-MSCA-ITN-2019-860813), PRIMA (H2020-MSCA-ITN-2019-860315); and by Accentur

Sensitive loss: Improving accuracy and fairness of face representations with discrimination-aware deep learning

We propose a discrimination-aware learning method to improve both the accuracy and fairness of biased face recognition algorithms. The most popular face recognition benchmarks assume a distribution of subjects without paying much attention to their demographic attributes. In this work, we perform a comprehensive discrimination-aware experimentation of deep learning-based face recognition. We also propose a notational framework for algorithmic discrimination with application to face biometrics. The experiments include three popular face recognition models and three public databases composed of 64,000 identities from different demographic groups characterized by sex and ethnicity. We experimentally show that learning processes based on the most used face databases have led to popular pre-trained deep face models that present evidence of strong algorithmic discrimination. Finally, we propose a discrimination-aware learning method, Sensitive Loss, based on the popular triplet loss function and a sensitive triplet generator. Our approach works as an add-on to pre-trained networks and is used to improve their performance in terms of average accuracy and fairness. The method shows results comparable to state-of-the-art de-biasing networks and represents a step forward to prevent discriminatory automatic systemsThe authors would like to thank Manuel Cebrian and Iyad Rahwan for their constructive feedback and inspiring talks. This work has been supported by projects: TRESPASS-ETN (MSCA-ITN-2019-860813), PRIMA (MSCA-ITN-2019-860315), BIBECA (RTI2018-101248-B-I00 MINECO/FEDER), and BBforTAI (PID2021-127641OB-I00 MICINN/FEDER). I. Serna is supported by a research fellowship from the Universidad Autónoma de Madrid (FPI-UAM-2020). A. Morales is supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with Autonomous University of Madrid in the line Encouragement of the Research of Young Researchers, in the context of the V PRICIT (Regional Programme of Research and Technological Innovation

OTB-morph: One-Time Biometrics via Morphing applied to Face Templates

© 2022 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksCancelable biometrics refers to a group of techniques in which the biometric inputs are transformed intentionally using a key before processing or storage. This transformation is repeatable enabling subsequent biometric comparisons. This paper introduces a new scheme for cancelable biometrics aimed at protecting the templates against potential attacks, applicable to any biometric-based recognition system. Our proposed scheme is based on time-varying keys obtained from morphing random biometric information. An experimental implementation of the proposed scheme is given for face biometrics. The results confirm that the proposed approach is able to withstand against leakage attacks while improving the recognition performanceThis work has been supported by projects: PRIMA (ITN-2019-860315), TRESPASS-ETN (ITN-2019-860813), and BIBECA (RTI2018-101248-B-I00 MINECO/FEDER). M.G. is supported by PRIMA and I.S. is supported by a FPI fellowship from Univ. Autonoma de Madrid

OTB-morph: one-time biometrics via morphing

Cancelable biometrics are a group of techniques to transform the input biometric to an irreversible feature intentionally using a transformation function and usually a key in order to provide security and privacy in biometric recognition systems. This transformation is repeatable enabling subsequent biometric comparisons. This paper introduces a new idea to be exploited as a transformation function for cancelable biometrics aimed at protecting templates against iterative optimization attacks. Our proposed scheme is based on time-varying keys (random biometrics in our case) and morphing transformations. An experimental implementation of the proposed scheme is given for face biometrics. The results confirm that the proposed approach is able to withstand leakage attacks while improving the recognition performanceThis work has been supported by projects: PRIMA (H2020-MSCA-ITN-2019- 860315), TRESPASS-ETN (H2020-MSCA-ITN-2019-860813), and BBforTAI (PID2021-127641OB-I00 MICINN/FEDER). M.G. is supported by PRIMA and I.S. is supported by an FPI fellowship from Univ. Autonoma de Madri

Measuring bias in aI models: An statistical approach introducing N-Sigma

The new regulatory framework proposal on Artificial Intelligence (AI) published by the European Commission establishes a new risk-based legal approach. The proposal highlights the need to develop adequate risk assessments for the different uses of AI. This risk assessment should address, among others, the detection and mitigation of bias in AI. In this work we analyze statistical approaches to measure biases in automatic decision-making systems. We focus our experiments in face recognition technologies. We propose a novel way to measure the biases in machine learning models using a statistical approach based on the N-Sigma method. N-Sigma is a popular statistical approach used to validate hypotheses in general science such as physics and social areas and its application to machine learning is yet unexplored. In this work we study how to apply this methodology to develop new risk assessment frameworks based on bias analysis and we discuss the main advantages and drawbacks with respect to other popular statistical testsSupport by project BBforTAI (PID2021-127641OB-I00 MICINN/FEDER). D. deAlcala is supported by a FPU Fellowship (FPU21/05785) from the Spanish MI