A new linear genetic programming approach based on straight line programs: Some theoretical and experimental aspects

Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described, new recombination operators for GP related to slp's are introduced and a study of the Vapnik-Chervonenkis dimension of families of slp's is done. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representation

Una metodología centrada en el usuario para el desarrollo de sistemas inteligentes basados en modelos de aprendizaje profundo

Esta propuesta doctoral está orientada al análisis de los sistemas inteligentes que utilizan modelos de aprendizaje profundo como núcleo de pensamiento. Dichos sistemas, por su complejidad, son difíciles de entrenar y de mejorar continuamente, además de que suelen ser una caja negra, por lo que sus resultados son poco explicables. En la tesis se está estudiando cómo orientar estos sistemas inteligentes a los usuarios, de tal forma que puedan aprovechar mejor los modelos desarrollados para sus problemas concretos, adaptando el sistema a voluntad. Además, también se mejorará el entendimiento que tienen de los mismos mediante técnicas de explicabilidad de modelos Deep Learning, lo cual repercutirá de forma positiva en la usabilidad general del sistema. Se pretende con esto obtener una metodología para el desarrollo de este tipo de sistemas, la cual pueda ser utilizada en aplicaciones reales y trabajos futuros.Esta tesis está financiada por la Universidad de Cantabria, el Gobierno de Cantabria y el Banco Santander a través de la beca de doctorado industrial DI27, concedida a Santos Bringas en la convocatoria del Programa de Doctorados Industriales 2020

A Framework for Identifying Sequences of Interactions That Cause Usability Problems in Collaborative Systems

Collaborative systems support shared spaces, where groups of users exchange interactions. In order to ensure the usability of these systems, an intuitive interactions´ organization and that each user has awareness information to know the activity of others are necessary. Usability laboratories allow evaluators to verify these requirements. However, laboratory usability evaluations can be problematic for reproducing mobile and ubiquitous contexts, as they restrict the place and time in which the user interacts with the system. This paper presents a framework for building software support that it collects human?machine interactions in mobile and ubiquitous contexts and outputs an assessment of the system´s usability. This framework is constructed through learning that is based on neural networks, identifying sequences of interactions related to usability problems when users carry out collaborative activities. The paper includes a case study that puts the framework into action during the development process of a smartphone application that supports collaborative sport betting.This research and the APC was funded by the University of Cantabria and the Government of Cantabria through the industrial doctorate grant DI27, given to Santos Bringas. Alicia Nieto-Reyes was supported by a Spanish Ministerio de Ciencia, Innovación y Universidades grant MTM2017-86061-C2-2-P

Discovering user's trends and routines from location based social networks

ABSTRACT: Location data is a powerful source of information to discover user's trends and routines. A suitable identification of the user context can be exploited to provide automatically services adapted to the user preferences. In this paper, we define a Dynamic Bayesian Network model and propose a method that processes location annotated data in order to train the model. Finally, our model enables us to predict future location contexts from the user patterns. A case study evaluates the proposal using real-world data of a location-based social network.This research was funded by Fondo Europeo de Desarrollo Regional (FEDER) and Sociedad para el Desarrollo Regional de Cantabria (SODERCAN) grant number TI16-IN-007 (within the program “I+C=+C 2016- PROYECTOS DE I+D EN EL ÁMBITO DE LAS TIC, LÍNEA SMART”), and by Ministerio de Ciencia e Innovación (MICINN), Spain grant number MTM2014-55262-P (project PAC::LFO)

A Convolutional Neural Network-Based Method for Human Movement Patterns Classification in Alzheimer?s Disease

Alzheimer’s disease (AD) constitutes a neurodegenerative pathology that presents mobility disorders as one of its earliest symptoms. Current smartphones integrate accelerometers that can be used to collect mobility data of Alzheimer’s patients. This paper describes a method that processes these accelerometer data and a convolutional neural network (CNN) that classifies the stage of the disease according to the mobility patterns of the patient. The method is applied in a case study with 35 Alzheimer’s patients, in which a classification success rate of 91% was obtaine

The role of keeping "semantic blocks" invariant: effects in linear genetic programming performance

This paper is focused on two different approaches (previously proposed by the authors) that perform better than Genetic Programming in typical symbolic regression problems: straight-line program genetic programming (SLP-GP) and evolution with attribute grammars (AGE). Both approaches have different characteristics. One of themost important is that SLP-GP keeps semantic blocks invariant (the crossover operator always exchanges complete subexpressions). In this paper we compare both methods and study the possible effect on their performance of keeping these blocks invariant.This work was partially supported by the R&D program of the Community of Madrid (S2009/TIC-1650, project “e-Madrid”) as well as by the Spanish Ministry of Science and Innovation (TIN2007-67466-C02-02). The authors thank Dr. Manuel Alfonseca for his help to prepare this document

Activity in the field of Human-Computer Interaction of a work team integrated in the MCFLAI research group

Se presenta la actividad en el ámbito de la Interacción Persona-Ordenador de un equipo de trabajo integrado en el grupo de investigación MCFLAI (Mathematics & Computation: Foundations, Learning, Artificial Intelligence) de la Universidad de CantabriaThe activity in the field of Human-Computer Interaction of a work team integrated in the research group MCFLAI (Mathematics & Computation: Foundations, Learning, Artificial Intelligence) of the University of Cantabria is presented