Adaptive Real-Time Method for Anomaly Detection Using Machine Learning

[Abstract] Anomaly detection is a sub-area of machine learning that deals with the development of methods to distinguish among normal and anomalous data. Due to the frequent use of anomaly-detection systems in monitoring and the lack of methods capable of learning in real time, this research presents a new method that provides such online adaptability. The method bases its operation on the properties of scaled convex hulls. It begins building a convex hull, using a minimum set of data, that is adapted and subdivided along time to accurately fit the boundary of the normal class data. The model has online learning ability and its execution can be carried out in a distributed and parallel way, all of them interesting advantages when dealing with big datasets. The method has been compared to other state-of-the-art algorithms demonstrating its effectiveness.This work has been supported by Spanish Government’s Secretaría de Estado de Investigación (Grant TIN2015-65069-C2-1-R), Xunta de Galicia (Grants ED431C 2018/34 and ED431G/01) and EU FEDER funds.Xunta de Galicia; ED431C 2018/34Xunta de Galicia; ED431G/0

A Machine Learning Solution for Distributed Environments and Edge Computing

[Abstract] In a society in which information is a cornerstone the exploding of data is crucial. Thinking of the Internet of Things, we need systems able to learn from massive data and, at the same time, being inexpensive and of reduced size. Moreover, they should operate in a distributed manner making use of edge computing capabilities while preserving local data privacy. The aim of this work is to provide a solution offering all these features by implementing the algorithm LANN-DSVD over a cluster of Raspberry Pi devices. In this system, every node first learns locally a one-layer neural network. Later on, they share the weights of these local networks to combine them into a global net that is finally used at every node. Results demonstrate the benefits of the proposed system.This research was funded by the Spanish Secretaría de Estado de Universidades e I+D+i (Grant TIN2015-65069-C2-1-R), Xunta de Galicia (Grants ED431C2018/34, ED341D R2016/045) and FEDER funds.Xunta de Galicia; ED431C2018/34Xunta de Galicia; ED341D R2016/04

Fast deep autoencoder for federated learning

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract]: This paper presents a novel, fast and privacy preserving implementation of deep autoencoders. DAEF (Deep AutoEncoder for Federated learning), unlike traditional neural networks, trains a deep autoencoder network in a non-iterative way, which drastically reduces training time. Training can be performed incrementally, in parallel and distributed and, thanks to its mathematical formulation, the information to be exchanged does not endanger the privacy of the training data. The method has been evaluated and compared with other state-of-the-art autoencoders, showing interesting results in terms of accuracy, speed and use of available resources. This makes DAEF a valid method for edge computing and federated learning, in addition to other classic machine learning scenarios.This work was supported in part by grant Machine Learning on the Edge - Ayudas Fundación BBVA a Equipos de Investigación Científica 2019; the Spanish National Plan for Scientific and Technical Research and Innovation (PID2019-109238GB-C22 and TED2021-130599A-I00); the Xunta de Galicia (ED431C 2022/44) and ERDF funds. CITIC is funded by Xunta de Galicia and ERDF funds. Funding for open access charge: Universidade da Coruña/CISUG.Xunta de Galicia; ED431C 2022/4

Experiencias para la mejora del proceso de aprendizaje y la motivación de los estudiantes basadas en proyectos

[Resumen] El presente artículo presenta una experiencia de aplicación de Aprendizaje Basado en Proyectos (ABP) a nivel de educación superior, con el objetivo de incrementar la motivación de los alumnos y mejorar su autoaprendizaje en una asignatura generalista de primer curso del Grado en Ingeniería Eléctrica de la Universidade da Coruña. En concreto, se planteó la incorporación de una nueva herramienta tecnológica: robots. La idea general es que los alumnos tengan que programar un dispositivo que actúa en el mundo real, es decir, fuera del entorno digital del ordenador, y que sea cercano a la especialidad de su titulación. Además de mostrar los resultados concretos obtenidos en esta experiencia durante dos cursos académicos, se analizan las repercusiones didácticas de una forma más global, y se plantean conclusiones y recomendaciones para otras asignaturas similares de otras titulaciones que pudiesen aplicar metodología ABP.[Abstract] This article presents an experience of applying Project Based Learning (PBL) at a higher education level, with the aim of increasing student’s motivation and improving their self-learning. PBL is applied in a generalist first year subject of the Degree in Electrical Engineering of the University of A Coruña. Specifically, the incorporation of a new technological tool, robots, was proposed with the aim of achieving a higher level of student motivation. The general idea is that students have to program a device that acts in the real world, that is, outside the digital environment of the computer, and that is close to the specialty of their degree. In addition to showing the specific results obtained in this experience during 2 academic years, the didactic repercussions are analyzed in a more global way, and conclusions and recommendations are proposed for other similar subjects of other degrees that could apply ABP methodology.https://doi.org/10.17979/spudc.978849749775

Machine learning techniques to predict different levels of hospital care of CoVid-19

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] In this study, we analyze the capability of several state of the art machine learning methods to predict whether patients diagnosed with CoVid-19 (CoronaVirus disease 2019) will need different levels of hospital care assistance (regular hospital admission or intensive care unit admission), during the course of their illness, using only demographic and clinical data. For this research, a data set of 10,454 patients from 14 hospitals in Galicia (Spain) was used. Each patient is characterized by 833 variables, two of which are age and gender and the other are records of diseases or conditions in their medical history. In addition, for each patient, his/her history of hospital or intensive care unit (ICU) admissions due to CoVid-19 is available. This clinical history will serve to label each patient and thus being able to assess the predictions of the model. Our aim is to identify which model delivers the best accuracies for both hospital and ICU admissions only using demographic variables and some structured clinical data, as well as identifying which of those are more relevant in both cases. The results obtained in the experimental study show that the best models are those based on oversampling as a preprocessing phase to balance the distribution of classes. Using these models and all the available features, we achieved an area under the curve (AUC) of 76.1% and 80.4% for predicting the need of hospital and ICU admissions, respectively. Furthermore, feature selection and oversampling techniques were applied and it has been experimentally verified that the relevant variables for the classification are age and gender, since only using these two features the performance of the models is not degraded for the two mentioned prediction problems.This research has been supported by GAIN (Galician Innovation Agency) and the Regional Ministry of Economy, Employment and Industry, Xunta de Galicia grant COV20/00604 through the ERDF Funds. Also, it has been possible thanks to the support of the Xunta de Galicia (Dirección Xeral de Saúde Pública) by providing the anonymous patient data. Also, it has been supported by the Xunta de Galicia (Grant ED431C 2018/34 and IN845D 2020/26 of the Axencia Galega de Innovación) with European Union ERDF funds. CITIC, as Research Center accredited by Galician University System, is funded by Consellería de Cultura, Educación e Universidades from Xunta de Galicia, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by Secretaría Xeral de Universidades (Grant ED431G 2019/01). Finally, we would also like to thank Prof. Ricardo Cao, as Chairman of the Committee of Experts for Mathematical Action against Coronavirus, for his kind request to collaborate in this projectXunta de Galicia; COV20/00604Xunta de Galicia; ED431C 2018/34Xunta de Galicia; IN845D 2020/26Xunta de Galicia; ED431G 2019/0

Qualitative spatial representation in agent-based models

One of the advantages of agent-based models as simulations of social systems is the ease with which it is possible to spatially embed the agents and their interactions. Spatially explicit representations in agent-based models most typically take the form of raster-based representations in which the space is represented as a grid of squares. More recently, vectorbased representations have been used, usually importing data for the polygons from geographical information systems (GIS). However, for some models, what matters about the space for the purposes of simulation is less the quantitative spatial relationships among entities (e.g. area, distance or direction) than the qualitative relations these quantitative data are used to determine: neighbourhood, and accessibility (which is a general term covering movement and sensing from one region to another). This paper gives consideration to the use of qualitative spatial representations in agent-based modelling, using a model of everyday pro-environmental behaviour in the workplace as an example

Estudio, implementación y análisis de nuevos algoritmos de aprendizaje y nuevas medidas de tolerancia al ruido para redes funcionales y neuronales

[Resumen] Esta tesis doctoral está organizada en dos partes, En la primera parte de la memoria se presentan nuevos algoritmos para el aprendizaje de redes de neuronas artificiales con alimentación hacia delante. En primer lugar se presenta un nuevo método para el aprendizaje de redes de una capa que permite el entrenamiento de este tipo de sistemas empleando un sistema de ecuaciones lineales. El método propuesto obtiene siempre el óptimo global de la función de error y, además, presenta una mayor velocidad de convergencia que los métodos iterativos empleados actualmente para este tipo de sistemas. Posteriormente, el método es mejorado permitiendo el aprendizaje de las funciones neuronales que, como se muestra en los experimentos realizados, permite mejorar el rendimiento de la red de neuronas. A continuación, se proponen tres nuevos algoritmos para la inicialización y el aprendizaje de redes de neuronas multicapa. Los dos primeros métodos están basados en el uso de mínimos cuadrados para obtener de forma óptima los pesos de todas o alguna de las capas de la red. En concreto, el primero de ellos está basado en la retropropagación de la salida deseada desde la capa de salida de la red hacia la de entrada y obtener, capa a capa, los pesos óptimos para cada una de ellas. El segundo método se basa en un método híbrido que utiliza una regla de optimización estándar para las primeras capas de la red y un sistema de ecuaciones lineales para la última cpa. Este método permite mejorar, significativamente, la velocidad de convergencia de los métodos actuales. Finalmente, el último método propuesto emplea una aproximación basada en modelos locales que consiste en la división de un problema complejo en varios subproblemas más fáciles de resolver

FedHEONN: Federated and homomorphically encrypted learning method for one-layer neural networks

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract]: Federated learning (FL) is a distributed approach to developing collaborative learning models from decentralized data. This is relevant to many real applications, such as in the field of the Internet of Things, since the models can be used in edge computing devices. FL approaches are motivated by and designed to protect privacy, a highly relevant issue given current data protection regulations. Although FL methods are privacy-preserving by design, recently published papers show that privacy leaks do occur, caused by attacks designed to extract private data from information interchanged during learning. In this work, we present an FL method based on a neural network without hidden layers that incorporates homomorphic encryption (HE) to enhance robustness against the above-mentioned attacks. Unlike traditional FL methods that require multiple rounds of training for convergence, our method obtains the collaborative global model in a single training round, yielding an effective and efficient model that simplifies management of the FL training process. In addition, since our method includes HE, it is also robust against model inversion attacks. In experiments with big data sets and a large number of clients in a federated scenario, we demonstrate that use of HE does not affect the accuracy of the model, whose results are competitive with state-of-the-art machine learning models. We also show that behavior in terms of accuracy is the same for identically and non-identically distributed data scenarios.This work has been supported by the grant Machine Learning on the Edge - Ayudas Fundación BBVA a Equipos de Investigación Científica 2019; the National Plan for Scientific and Technical Research and Innovation of the Spanish Government (Grants PID2019-109238GB-C2 and PID2021-128045OA-I00); and by the Xunta de Galicia (ED431C 2022/44) with the European Union ERDF funds. CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01). Funding for open access charge: Universidade da Coruña/CISUG.Xunta de Galicia; ED431C 2022/44Xunta de Galicia; ED431G 2019/0

A One-Class Classification method based on Expanded Non-Convex Hulls

[Abstract]: This paper presents an intuitive, robust and efficient One-Class Classification algorithm. The method developed is called OCENCH (One-class Classification via Expanded Non-Convex Hulls) and bases its operation on the construction of subdivisible and expandable non-convex hulls to represent the target class. The method begins by reducing the dimensionality of the data to two-dimensional spaces using random projections. After that, an iterative process based on Delaunay triangulations is applied to these spaces to obtain simple polygons that characterizes the non-convex shape of the normal class data. In addition, the method subdivides the non-convex hulls to represent separate regions in space if necessary. The method has been evaluated and compared to several main algorithms of the field using real data sets. In contrast to other methods, OCENCH can deal with non-convex and disjointed shapes. Finally, its execution can be carried out in a parallel way, which is interesting to reduce the execution time

Aplicación de un robot comercial de bajo coste en tareas de seguimiento de objetos

The computer vision plays a key role in the field of mobile robotics, given the large amount of information about the environment that can be extracted from an image. The aim of the work developed is to get a robot autonomous behavior of a low cost commercial called Rovio on the task of following a moving object independently. To achieve this, it has been necessary to develop a set of functions, ones for robot management and others to carry out communication and data transfer with it. Tests are performed to determine the stability of the proposed and implemented to measure the precision obtainedLa visión computacional desempeña un papel fundamental en el campo de la robótica móvil, dada la gran cantidad de información sobre el entorno que puede ser extraída de una imagen. El objetivo del trabajo desarrollado es conseguir un comportamiento autónomo de un robot comercial de bajo coste denominado Rovio, en la tarea de seguir un objeto móvil de forma autónoma. Para lograrlo, ha sido necesario desarrollar un conjunto de funciones, unas de manejo del robot y otras para llevar a cabo la comunicación y transferencia de datos con él. Se realizan pruebas para determinar la estabilidad de la propuesta implementada y para poder medir la precisión que se obtien