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Cecilio Ángulo

Universitat Politècnica de Catalunya

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Luis González Abril

Universidad de Sevilla

Núria Agell

Universitat Ramon Llull

Andreu Català

Universitat Politècnica de Catalunya

Marta Díaz Boladeras

Complejo Hospitalario Universitario de Toledo

Jordi Albó-Canals

Lighthouse Guild

Francisco J. Ruíz

Universitat Politècnica de Catalunya

Sérgio Escalera

Barcelona Supercomputing Center

Pere Ponsa

Universitat Politècnica de Catalunya

Diego Pardo

Rutherford Appleton Laboratory

Juan Antonio Ortega

Universidad de Sevilla

Cooperative Institutions

Universitat Politècnica de Catalunya

Universitat de Barcelona

Universitat Autònoma de Barcelona

Cidete (Spain)

Universitat Ramon Llull

FC Barcelona

Centre National de la Recherche Scientifique

Universidad de Sevilla

Tufts University

Pompeu Fabra University

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A post-processing strategy for SVM learning from unbalanced data

The European Symposium on Artificial Neural Networks (2011)

Haydemar Núñez Castro Luis González Abril Cecilio Ángulo

Standard learning algorithms may perform poorly when learning from unbalanced datasets. Based on the Fisher’s discriminant analysis, a post-processing strategy is introduced to deal datasets with significant imbalance in the data distribution. A new bias is defined, which reduces skew towards the minority class. Empirical results from experiments for a learned SVM model on twelve UCI datasets indicates that the proposed solution improves the original SVM, and they also improve those reported when using a z-SVM, in terms of g-mean and sensitivity.

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The role of somatosensory models in vocal autonomous exploration

Juan Manuel Acevedo Valle Cecilio Ángulo Clément Moulin-Frier Karla Andrea Trejo Ramírez

The present work focuses on two main objectives. Firstly, it highlights the relevance of studying the early stages of language development using machines as an approach to contribute to the future of speech recognizers and synthesizers, user interfaces, active learning techniques, and to the field of robotics and artificial intelligence in general. Secondly, this work introduces some results on the study of the role of somatosensory models in vocal autonomous exploration. In previous works, the roles of intrinsic motivations and motor constraints in early vocal development were studied showing that active learning techniques can be used by artificial agents endowed with a simulated vocal tract to autonomously learn how to produce intended sounds through the use of probabilistic models. This work studies the effects of modifying the somatosensory model, which is used to map motor commands to undesired articulatory configurations, over the intrinsically motivated active learning process. The somatosensory system is modeled as a Gaussian Mixture Model. Herein, some simulations were run varying the structure of the model in order to analyze differences in the results. The effects on the explored sensorimotor regions and the amount of undesired vocal configurations are studied. The simulations presented in this work show that the structure of the current somatosensory model is relevant to the learning process. However, it can be also concluded that in order to reliably characterize the effects of modifying the somatosensory model further simulations must be performed and clear measures for performance should be considered. // El trabajo presentado persigue dos objetivos principales: el primero de ellos es mostrar la necesidad de estudiar las etapas tempranas del desarrollo del lenguaje utilizando maquinas. Estos estudios contribuiran en el desarrollo futuro de sintetizadores y reconocedores de voz, interfaces de usuario e indirectamente al estudio de la inteligencia artificial; el segundo objetivo es presentar nuevos resultados en el estudio sobre el rol de los sistemas somatosensores en la exploracion vocal temprana. En trabajos preliminares fueron estudiados los roles de las motivaciones intrinsecas y las restricciones motoras en el desarrollo vocal temprano. De estos estudios se concluyo que las tecnicas de aprendizaje automatico activo pueden ser utilizadas en conjunto con agentes artificiales dotados con un tracto vocal simulado para aprender autonomamente como producir sonidos especificos. En el presente trabajo se estudian los efectos del cambio de los parametros que definen el modelo probabilistico del sistema somatosensorial, el cual mapea configuraciones motoras con configuraciones articulares indeseadas sobre el proceso de aprendizaje. El sistema somatosensorial es modelado utilizando “Gaussian Mixture Models”. A traves del resultado de una serie de simulaciones donde se modifica la estructura del modelo antes mencionado, se demuestra que la estructura del modelo somatosensorial es relevante para el proceso de aprendizaje. Sin embargo, los resultados tambien indican que para realizar una mejor caracterizacion de los efectos de la modificacion del modelo somatosensorial deben llevarse a cabo mas simulaciones, asi como tomar en consideracion nuevas medidas de calidad del aprendizaje.

Relevance

Vocal tract

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Autonomous Discovery of Motor Constraints in an Intrinsically Motivated Vocal Learner

IEEE Transactions on Cognitive and Developmental Systems (2017)

Juan M. Acevedo-Valle Cecilio Ángulo Clément Moulin-Frier

This paper introduces new results on the modeling of early vocal development using artificial intelligent cognitive architectures and a simulated vocal tract. The problem is addressed using intrinsically motivated learning algorithms for autonomous sensorimotor exploration, a kind of algorithm belonging to the active learning architectures family. The artificial agent is able to autonomously select goals to explore its own sensorimotor system in regions, where its competence to execute intended goals is improved. We propose to include a somatosensory system to provide a proprioceptive feedback signal to reinforce learning through the autonomous discovery of motor constraints. Constraints are represented by a somatosensory model which is unknown beforehand to the learner. Both the sensorimotor and somatosensory system are modeled using Gaussian mixture models. We argue that using an architecture which includes a somatosensory model would reduce redundancy in the sensorimotor model and drive the learning process more efficiently than algorithms taking into account only auditory feedback. The role of this proposed system is to predict whether an undesired collision within the vocal tract under a certain motor configuration is likely to occur. Thus, compromised motor configurations are rejected, guaranteeing that the agent is less prone to violate its own constraints.

10.1109/tcds.2017.2699578

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A self-organizing world: special issue of the 13th edition of the workshop on self-organizing maps and learning vector quantization, clustering and data visualization, WSOM + 2019

Neural Computing and Applications (2021)

Alfredo Vellido Cecilio Ángulo Karina Gibert

Self-organizing map

Learning vector quantization

10.1007/s00521-021-06307-w

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Emotional factors in robot-based assistive services for elderly

Martha Diaz-Boladeras Cecilio Ángulo Marcel Heerink

Emotional factors related to aging at home assistive technology are known to affect technology acceptance, effective use, and quality of life improvement. This paper is a survey on the affective dimension of robot-based systems conceived for helping elderly at home. The specificity of elders’ capabilities (e.g. sensory and cognitive), coping styles, aspirations, lifestyles, social rules and preferences are faced with available knowledge from the fields of social psychology, sociology and gerontology. In the case of social robots, convenient verbal and non-verbal communication and motion behavior (e.g. social distance, space formations) are to be designed according to generational and cultural rules. Moreover, robot behavior should be congruent with its role (i.e. helper, companion) and affordances.

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Social robot

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Aprendizaje con máquinas núcleo en entornos de multiclasificación

Cecilio Ángulo

La propiedad de generalización de una máquina de aprendizaje, es decir su capacidad para emitir una respuesta correcta ante una nueva entrada semejante a aquellas con las que ha sido entrenada, es la característica principal que se busca en los sistemas conexionistas supervisados y sirve de justificación en la elección de los principios inductivos y el tipo de estructuras de aprendizaje para elaborar el presente estudio. La regularización o penalización es uno de estos principios que favorecen a nivel teórico la generalización, sobre el cual se ha desarrollado un método de cálculo directo de la matriz de regularización cuando se utiliza como estabilizador un operador diferencial de segundo grado, precisamente aquel que minimiza el grado de convexidad de la función solución, evitando así y el proceso iterativo de cálculo de la matriz hessiana y fijando el tipo de núcleo a ser utilizado. Los nexos de unión entre la regularización y el principio de minimización del riesgo estructural así como las excelentes características teóricas mostradas por este ´ ultimo principio trabajando, por definición, sobre conjuntos finitos de datos y expandiendo su solución sobre un número pequeño de núcleos, han llevado a desplazar el foco de trabajo de numerosos investigadores hacia las máquinas de soporte vectorial, su materialización procedimental. En este contexto, se ha desarrollado una máquina que permite extender de forma natural el comportamiento binario de estas máquinas núcleo de margen máximo sobre problemas de clasificación hacia una solución ternaria m´asacorde con la estructura geométrica de los datos, en especial en las situaciones habituales de espacios de salida que poseen más de dos clases. El uso de la nueva arquitectura, bautizada K-SVCR, en problemas de multiclasificación resulta más adecuado que las reducciones estándares de problemas multiclase sobre máquinas biclasificadoras en estructuras en paralelo o arbóreas puesto que cada nodo de dicotomía considera todo el espacio de entrenamiento y se fuerza al hiperplano de separación a considerar la estructura geométrica de los patrones de entrenamiento. En especial, se demuestra la robustez del nuevo método ante fallos en las predicciones de algunos de sus nodos de trabajo cuando se considera un tipo especial de combinación de estas respuestas. La nueva arquitectura de multiclasificación ha sido modificada con posterioridad para ser implementada sobre un problema de clasificación con características independientes, la ordenación o problema de aprendizaje de preferencias. Sus prestaciones son evaluadas sobre una aplicación financiera en la determinación de riesgos crediticios. Finalmente, una aplicación de categorización o discriminación de escenarios de depuración donde incide el efecto de la temporalidad sirve también como ejemplo de funcionamiento. The property of generalization of a learning machine, i.e. its capacity to emit a correct answer on a new similar input to those with wich it has been trained, is the basic behavior looked for in the supervised connexionists systems and it serves as justification in the selection of the inductive principles and the type of learning structures to ellaborate the present study. The penalty is one of these principles that favor at theoretical level the generalization, on which a method of direct calculation of the regularization matrix when a second degree differential operator is used like stabilizer, indeed that diminishing the convexity degree of the solution function, avoiding therefore the iterative process of calculation of the Hessian matrix, has been developed and fixing the type of kernel to be used. Links between regularization and the structural risk minimization principle as well as the excellent theoretical characteristics shown by this last principle working, by definition, on finite data sets and expanding their solution on a small number of kernels, have taken to move the center of study of numerous investigators towards the support vector machines, their procedural materialization. In this context, a machine that allows to extend of natural form the binary behavior of these maximum margin ker-nel machines on classification problems towards an agreed ternary solution with the geometric structure of the data has been developed, in special in the habitual situations of output spaces having more than two classes. The use of the new architecture, named K-SVCR, in multiclassification problems is more suitable than the standard reductions from multiclass problems on biclass machines in tree or parallel structures, since each di-chotomie node considers all the training space and force to the hyperplane of separation to consider the geometric structure of the training patterns. In special, the robustness of the new method is demostrated on failures in the predictions of some of its working nodes when a special type of combination of these answers is considered. The new architecture of multiclassification has been modified later to be implemented on a classification problem with independent characteristics, the ordenation or learning of preferences problem. Their benefits are evaluated on a financial application in the determination of credit risks. Finally, an application of categorization in waste water plant scenes, where the temporality affects, also serves like operation example.

10.5821/dissertation-2117-93490

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SVM-based learning method for improving colour adjustment in automotive basecoat manufacturing

The European Symposium on Artificial Neural Networks (2009)

Francisco Javier Ruiz Vegas Núria Agell Cecilio Ángulo

A new iterative method based on Support Vector Machines to perform automated colour adjustment processing in the automotive in- dustry is proposed in this paper. The iterative methodology relies on a SVM trained with patterns provided by expert colourists and an ac- tions' generator module. The SVM algorithm enables selecting the most adequate action in each step of an iterated feed-forward loop until the final state satisfies colourimetric bounding conditions. Both encouraging results obtained and the significant reduction of non-conformance costs, justify further industrial efforts to develop an automated software tool in this and similar industrial processes.

Bounding overwatch

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Pain and anxiety treatment based on social robot interaction with children to improve patient experience. Ongoing research

Cecilio Ángulo Carles Garriga Berga C. Luaces Cubells Jaume Perez Payarols Jordi Albó-Canals

A major focus for children’s quality of life programs in hospitals is improving their experiences during procedures. In anticipation of treatment, children may become anxious and during procedures pain appears. The aim of this article is to introduce a proposal to design pioneering techniques based on the use of social robots to improve the patient experience by eliminating or minimizing pain and anxiety. According to this proposed challenge, this research aims to design and develop specific human-social robot interaction with pet robots. Robot interactive behavior will be designed based on modular skills using soft-computing paradigms.

Anticipation (artificial intelligence)

Social robot

Human–robot interaction

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Citations (5)

Completing Ambiguous Plans in Cognitive Robotics with Delayed Information

Frontiers in artificial intelligence and applications (2016)

Kothakota Sai Kishor Cecilio Ángulo

Robots equipped with a set of simple action skills should complete complex tasks, defined as the concatenation of a number of those basic abilities. Traditionally, planners have been used to decide skills to be activated, as well as in which sequence, like state machines. Recently, cognitive architectures like SOAR have been proposed to act as the reasoner by selecting which competence the robot should perform, addressing it towards the goal. However, they have been unidirectionally integrated: once the plan is completely designed by the cognitive architecture, it is sent to the robot, but no feedback is provided to the reasoner. Instead, our proposal allows to establish bi-directional communication between the reasoner and the robot. In this form, the reasoner can develop incomplete plans under the assumption that a part of the information to complete the plan for achieving the goal will arrive delayed from the robot's environment as well as the user. Our work develops this bi-directional communication between the SOAR cognitive architecture and the ROS (Robot Operating System) environment, usual in mobile robotics. The proposed architecture has been tested on a UAV (Unmanned Aerial Vehicle) Parrot AR.Drone 2.0, which acts as mobile robot, in a searching task.

Cognitive robotics

10.3233/978-1-61499-696-5-19

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Cascade classifiers based robust pedestrian detection

Wilbert G. Aguilar Marco A. Luna Julio F. Moya Vanessa Abad Hugo Ruiz

This paper proposes a dataset and algorithms for pedestrian detection in UAVs. The method proposed is a HAARLBP based cascade classifier combined with saliency maps for improving the performance of the detector. In addition we introduce a dataset with images from surveillance cameras at different angles and altitudes emulating a UAV. We validate our dataset by the implementation of HOG algorithm and compared it with other approaches from the literature. The results show that HAAR-LBP algorithm has better performance than HAAR like features; our dataset is better for pedestrian detection using UAVs and the use of saliency maps improves the performance of cascade classifiers.

Haar-like features

Pedestrian detection

Cascading classifiers

10.1109/itslatam.2017.8060000

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