FaceSampler: Selección automática de mostras do usuario para un sistema non colaborativo de verificación de rostros

Traballo de Fin de grao en Enxeñaría InformáticaUsualmente, nun sistema de reco~necemento facial com un, t omase unha imaxe do rostro do individuo e proc esase para obter os seus puntos caracter sticos. Logo, contr astase esta informaci on cun modelo dese suxeito, o que permite clasi car a cara como pertencente a persoa ou non. Para obter dito modelo, sen embargo, prec sase dispor dun n umero su ciente de mostras de adestramento da cara do individuo, que permitan constru r unha codi caci on do rostro o m ais robusta e discriminante posible. Esta e a raz on pola que se foron creando nos ultimos anos grandes bancos de datos de imaxes anotadas de acceso p ublico como LFW [2], FDDB [3] ou YTF [4]. Sen embargo, s o se constru ron uns poucos bancos de datos de acceso p ublico para o reco~necemento biom etrico no ambito dos dispositivos m obiles, como MOBIO [5] ou o de PASC [6], o que impediu o avance nesta li~na. Pero, o que e peor, as imaxes provintes deste tipo de bancos soen estar moi nesgadas, isto e, existen diferenzas signi cativas entre elas. A captaci on de imaxes mediante c amaras en dispositivos m obiles est a suxeita a caracter sticas espec cas non s o a nivel de sensores (escala, resoluci on, etc.), sen on tam en relacionadas coa forma de usar tales dispositivos (orientaci on, expresi on, iluminaci on, etc.). Todo isto esixe que, para acadar o mellor rendemento, se deba dispor de mostras de caras para a aprendizaxe espec cas do contexto. Neces tase, por en, unha ferramenta que poida ser instalada polo individuo no seu m obil e que o \acompa~ne" no seu d a a d a. Esta ferramenta debe traballar sen a colaboraci on do suxeito, pero debe saber cando capturar imaxes para obter boas mostras do seu rostro. Para elo, compre que se axude da informaci on relativa ao estado do dispositivo: orientaci on, iluminaci on ambiental, manipulaci on por parte do usuario, etc. A trav es deste traballo pret endese apoiar esta li~na de investigaci on, en particular no relativo a aprendizaxe autom atica (no propio dispositivo m obil) para a captaci on das caras dos usuarios. Pres entase as FaceSampler, unha aplicaci on Android que implementa a proposta anterior e ten unha dobre utilidade. Por un lado, funciona como ferramenta stand-alone que permite obter as mostras dos rostros dos usuarios. Polo outro, nun futuro poder an aproveitarse as s uas funcionalidades para traballar como mecanismo de selecci on de mostras de cada usuario durante todo o tempo de vida dunha aplicaci on de veri caci on de caras.Universidade de Santiago de Compostela. Departamento Electrónica e Computació

Ensemble and continual federated learning for classifcation tasks

Federated learning is the state-of-the-art paradigm for training a learning model collaboratively across multiple distributed devices while ensuring data privacy. Under this framework, different algorithms have been developed in recent years and have been successfully applied to real use cases. The vast majority of work in federated learning assumes static datasets and relies on the use of deep neural networks. However, in real world problems, it is common to have a continual data stream, which may be non stationary, leading to phenomena such as concept drift. Besides, there are many multi-device applications where other, non-deep strategies are more suitable, due to their simplicity, explainability, or generalizability, among other reasons. In this paper we present Ensemble and Continual Federated Learning, a federated architecture based on ensemble techniques for solving continual classification tasks. We propose the global federated model to be an ensemble, consisting of several independent learners, which are locally trained. Thus, we enable a flexible aggregation of heterogeneous client models, which may differ in size, structure, or even algorithmic family. This ensemble-based approach, together with drift detection and adaptation mechanisms, also allows for continual adaptation in situations where data distribution changes over time. In order to test our proposal and illustrate how it works, we have evaluated it in different tasks related to human activity recognition using smartphonesOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research has received financial support from AEI/FEDER (European Union) Grant Number PID2020-119367RB-I00, as well as the Consellería de Cultura, Educación e Universitade of Galicia (accreditation ED431G-2019/04, ED431G2019/01, and ED431C2018/29), and the European Regional Development Fund (ERDF). It has also been supported by the Ministerio de Universidades of Spain in the FPU 2017 program (FPU17/04154)S

Dataset bias exposed in face verification

This is the peer reviewed version of the following article: López‐López, E., Pardo, X.M., Regueiro, C.V., Iglesias, R. and Casado, F.E. (2019), Dataset bias exposed in face verification. IET Biom., 8: 249-258, which has been published in final form at https://doi.org/10.1049/iet-bmt.2018.5224. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived VersionsMost facial verification methods assume that training and testing sets contain independent and identically distributed samples, although, in many real applications, this assumption does not hold. Whenever gathering a representative dataset in the target domain is unfeasible, it is necessary to choose one of the already available (source domain) datasets. Here, a study was performed over the differences among six public datasets, and how this impacts on the performance of the learned methods. In the considered scenario of mobile devices, the individual of interest is enrolled using a few facial images taken in the operational domain, while training impostors are drawn from one of the public available datasets. This work tried to shed light on the inherent differences among the datasets, and potential harms that should be considered when they are combined for training and testing. Results indicate that a drop in performance occurs whenever training and testing are done on different datasets compared to the case of using the same dataset in both phases. However, the decay strongly depends on the kind of features. Besides, the representation of samples in the feature space reveals insights into what extent bias is an endogenous or an exogenous factorThis work has received financial support from the Xunta de Galicia, Consellería de Cultura, Educación e Ordenación Universitaria (Accreditation 2016–2019, EDG431G/01 and ED431G/08, and reference competitive group 2014–2017, GRC2014/030), the European Union: European Social Fund (ESF), European Regional Development Fund (ERDF) and FEDER funds and (AEI/FEDER, UE) grant number TIN2017‐90135‐R. Eric López had received financial support from the Xunta de Galicia and the European Union (European Social Fund ‐ ESF)S

Walking Recognition in Mobile Devices

Presently, smartphones are used more and more for purposes that have nothing to do with phone calls or simple data transfers. One example is the recognition of human activity, which is relevant information for many applications in the domains of medical diagnosis, elderly assistance, indoor localization, and navigation. The information captured by the inertial sensors of the phone (accelerometer, gyroscope, and magnetometer) can be analyzed to determine the activity performed by the person who is carrying the device, in particular in the activity of walking. Nevertheless, the development of a standalone application able to detect the walking activity starting only from the data provided by these inertial sensors is a complex task. This complexity lies in the hardware disparity, noise on data, and mostly the many movements that the smartphone can experience and which have nothing to do with the physical displacement of the owner. In this work, we explore and compare several approaches for identifying the walking activity. We categorize them into two main groups: the first one uses features extracted from the inertial data, whereas the second one analyzes the characteristic shape of the time series made up of the sensors readings. Due to the lack of public datasets of inertial data from smartphones for the recognition of human activity under no constraints, we collected data from 77 different people who were not connected to this research. Using this dataset, which we published online, we performed an extensive experimental validation and comparison of our proposalsThis research has received financial support from AEI/FEDER (European Union) grant number TIN2017-90135-R, as well as the Consellería de Cultura, Educación e Ordenación Universitaria of Galicia (accreditation 2016–2019, ED431G/01 and ED431G/08, reference competitive group ED431C2018/29, and grant ED431F2018/02), and the European Regional Development Fund (ERDF). It has also been supported by the Ministerio de Educación, Cultura y Deporte of Spain in the FPU 2017 program (FPU17/04154), and the Ministerio de Economía, Industria y Competitividad in the Industrial PhD 2014 program (DI-14-06920)S

Concept drift detection and adaptation for federated and continual learning

Smart devices, such as smartphones, wearables, robots, and others, can collect vast amounts of data from their environment. This data is suitable for training machine learning models, which can significantly improve their behavior, and therefore, the user experience. Federated learning is a young and popular framework that allows multiple distributed devices to train deep learning models collaboratively while preserving data privacy. Nevertheless, this approach may not be optimal for scenarios where data distribution is non-identical among the participants or changes over time, causing what is known as concept drift. Little research has yet been done in this field, but this kind of situation is quite frequent in real life and poses new challenges to both continual and federated learning. Therefore, in this work, we present a new method, called Concept-Drift-Aware Federated Averaging (CDA-FedAvg). Our proposal is an extension of the most popular federated algorithm, Federated Averaging (FedAvg), enhancing it for continual adaptation under concept drift. We empirically demonstrate the weaknesses of regular FedAvg and prove that CDA-FedAvg outperforms it in this type of scenarioThis research has received financial support from AEI/FEDER (EU) grant number TIN2017-90135-R, as well as the Consellería de Cultura, Educación e Ordenación Universitaria of Galicia (accreditation 2016–2019, ED431G/01 and ED431G/08, reference competitive group ED431C2018/29, and grant ED431F2018/02), and the European Regional Development Fund (ERDF). It has also been supported by the Ministerio de Universidades of Spain in the FPU 2017 program (FPU17/04154)S

100 años investigando el mar. El IEO en su centenario (1914-2014).

Se trata de un libro que pretende divulgar a la sociedad las principales investigaciones multidisciplinares llevadas a cabo por el Instituto Español de Oceanografía durante su primer siglo de vida, y dar a conocer la historia del organismo, de su Sede Central y de los nueve centros oceanográficos repartidos por los litorales mediterráneo y atlántico, en la península y archipiélagos.Kongsberg 20

Continual federated machine learning under concept drift

A aprendizaxe federada é un paradigma de aprendizaxe automática que permite adestrar modelos de maneira distribuída, entre múltiples dispositivos, sen poñer en riscos a privacidade dos usuarios. O obxectivo desta tese é o de desenvolver novas estratexias de aprendizaxe federada que, mantendo tódalas vantaxes que esta tecnoloxía xa proporciona, permitan tamén lidar con escenarios continuos, en situacións non estacionarias suxeitas a derivas de concepto. As dúas contribucións máis importantes da tese son o método Concept-Drift-Aware Federated Averaging (CDA-FedAvg) e a arquitectura Ensemble and Continual Federated Learning (ECFL). Por unha banda, CDA-FedAvg permite o adestramento de redes neuronais profundas de maneira federada e ao longo do tempo, sendo ademáis capaz de detectar derivas de concepto e adaptarse a elas. Por outra banda, ECFL propón que o modelo federado sexa un comité, de xeito que estea composto de múltiples modelos locais independentes, un por cliente. Isto permite levar a cabo o adestramento federado sen restricións sobre o tipo de algoritmo de aprendizaxe a empregar, o que lle outorga á nosa proposta certa vantaxe en canto a simplicidade, flexibilidade e robustez. ECFL tamén está deseñada para aprender ao longo do tempo. Ademais, ao longo da tese avaliamos as nosas propostas teóricas en distintos casos de uso, incluíndo o recoñecemento da actividade humana en smartphones e a asistencia a usuarios en cadeiras de rodas robóticas.2023-11-1

Non-IID data and Continual Learning processes in Federated Learning: a long road ahead

Federated Learning is a novel framework that allows multiple devices or institutions to train a machine learning model collaboratively while preserving their data private. This decentralized approach is prone to suffer the consequences of data statistical heterogeneity, both across the different entities and over time, which may lead to a lack of convergence. To avoid such issues, different methods have been proposed in the past few years. However, data may be heterogeneous in lots of different ways, and current proposals do not always determine the kind of heterogeneity they are considering. In this work, we formally classify data statistical heterogeneity and review the most remarkable learning Federated Learning strategies that are able to face it. At the same time, we introduce approaches from other machine learning frameworks. In particular, Continual Learning strategies are worthy of special attention, since they are able to handle habitual kinds of data heterogeneity. Throughout this paper, we present many methods that could be easily adapted to the Federated Learning settings to improve its performance. Apart from theoretically discussing the negative impact of data heterogeneity, we examine it and show some empirical results using different types of non-IID dataThis work has received financial support from AEI/FEDER (EU) grant number PID2020-119367RB-I00. It has also been supported by the Xunta de Galicia - Consellería de Cultura, Educación e Universidade (Centros de investigación de Galicia accreditation 2019–2022 ED431G-2019/04 and ED431G2019/01, and Reference Competitive Groups accreditation 2021–2024, ED431C 2018/29, ED431F2018/02 and ED431C 2021/30) and the European Union (European Regional Development Fund - ERDF). Finally, it has also been funded by the Ministerio de Universidades of Spain in the FPU 2017 program (FPU17/04154)S

Pautes per avaluar projectes de recerca i innovació en salut que utilitzin tecnologies emergents i dades personals

Los intereses de la ciencia, la tecnología y la sociedad no deben prevalecer sobre los del individuo. Asegurar este principio es una de las tareas de los comités de ética de la investigación (CEI), unos órganos colegiados interdisciplinarios establecidos por la ley que analizan la validez científica de las investigaciones y su valor social, y que ponderan los derechos e intereses en juego. En los últimos años se ha visto como la labor de los CEI se ampliaba: ya no solo evalúan ensayos clínicos con medicamentos y productos sanitarios, sino que también analizan proyectos en los que se aplican tecnologías emergentes como la inteligencia artificial, los datos masivos, la biometría o la realidad virtual, entre otras. En este contexto, el Grupo de Opinión del Observatorio de Bioética y Derecho - Cátedra UNESCO de Bioética de la Universidad de Barcelona (OBD) ha publicado el documento Pautas para evaluar proyectos de investigación e innovación en salud que utilicen tecnologías emergentes y datos personales, donde se tratan los retos, las cuestiones no resueltas y los problemas que suscitan los proyectos de investigación e innovación en salud

The cognitive and psychiatric subacute impairment in severe Covid-19.

Neurologic impairment persisting months after acute severe SARS-CoV-2 infection has been described because of several pathogenic mechanisms, including persistent systemic inflammation. The objective of this study is to analyze the selective involvement of the different cognitive domains and the existence of related biomarkers. Cross-sectional multicentric study of patients who survived severe infection with SARS-CoV-2 consecutively recruited between 90 and 120 days after hospital discharge. All patients underwent an exhaustive study of cognitive functions as well as plasma determination of pro-inflammatory, neurotrophic factors and light-chain neurofilaments. A principal component analysis extracted the main independent characteristics of the syndrome. 152 patients were recruited. The results of our study preferential involvement of episodic and working memory, executive functions, and attention and relatively less affectation of other cortical functions. In addition, anxiety and depression pictures are constant in our cohort. Several plasma chemokines concentrations were elevated compared with both, a non-SARS-Cov2 infected cohort of neurological outpatients or a control healthy general population. Severe Covid-19 patients can develop an amnesic and dysexecutive syndrome with neuropsychiatric manifestations. We do not know if the deficits detected can persist in the long term and if this can trigger or accelerate the onset of neurodegenerative diseases