Krowdix: simulador de redes sociales

Krowdix es un programa para el análisis de redes sociales. Estas redes se pueden crear y modificar de un modo sencillo, y están formadas por nodos (los usuarios de la red) y relaciones (conexiones entre esos nodos), pero además tienen otros elementos como aficiones (los intereses de los nodos), contenidos (cualquier cosa creada por un nodo en la red) y comentarios (de un nodo a un contenido). El programa es capaz de mostrar estas redes por pantalla de distintos modos, así como de generar informes que recojan sus propiedades. Pero además, gracias al uso de agentes inteligentes, Krowdix puede actuar como un simulador, mostrando la evolución de la red a partir de un instante dado. [ABSTRACT] Krowdix is a Social Network analyzer. Those networks can be created and modified easily, and they consist on nodes (network users) and relations (connections among nodes). They have other elements also, like likings (nodes interests), contents (anything created by a node) and comments (from nodes to contents). The program can show these networks on screen in different ways and generate reports with its properties. Furthermore, using intelligent agents, Krowdix can simulate the evolution of a network from a certain moment

Transfer learning in hierarchical dialogue topic classification with neural networks

Knowledge transfer between tasks can significantly improve the efficiency of machine learning algorithms. In supervised natural language understanding problems, this sort of improvement is critical since the availability of labelled data is usually scarce. In this paper we address the question of transfer learning between related topic classification tasks. A characteristic of our problem is that the tasks have a hierarchical relationship. Therefore, we introduce and validate how to implement the transfer exploiting this hierarchical structure. Our results for a real-world topic classification task show that the transfer can produce improvements in the behavior of the classifiers for some particular problems.The research presented in this paper is conducted as part of the project EM-PATHIC that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 769872

Algoritmos genéticos para la georeferenciación de imágenes con identificación automática de puntos de control terrestres

Gracias a la Visión por Computador podemos analizar sucesos que ocurren a gran escala,como el desplazamiento de glaciares, inundaciones, o vertidos de productos contaminantes en ríos y costas. El objetivo de este trabajo es proporcionar una técnica que sea capaz de georeferenciar fotografías oblicuas tomadas con un equipo de bajo coste. El proceso se realiza a partir de una serie de imágenes oblicuas y un modelo digital de elevación (DEM, Digital Elevation Model), analizando cada imagen por separado para obtener las coordenadas de los puntos más significativos comunes entre ellas para establecer la relación matemática a partir de la cual pueden georeferenciarse las mencionadas imágenes. El procedimiento que se plantea tiene como fundamento el diseño de un algoritmo genético con el que podremos ajustar tanto la posición teórica como la dirección hacia la que apuntaba el eje óptico del sistema correspondiente acoplado a la cámara. El objetivo consiste en obtener los valores reales que permitan el ajuste con el fin de lograr la buscada relación matemática entre la imagen y el DEM y lo más precisa posible. [ABSTRACT] Thanks to Computer Vision, we are able to analyze big scale events, as glacier movements, floods, or contaminant spills at rivers or shores. The aim of this paper is to provide with a technique capable of georeferencing oblique terrestrial photography with low cost equipment. All the process will be done with the input of some oblique pictures and a Digital Elevation Model (DEM), analyzing each one to obtain significant points to establish a relationship from where the pictures can be georeferenced. The process suggested it's based on the design of a Genetic Algorithm, which will be able to adjust both theoric position and aim of our optical system. The aim is to obtain the mathematical relationship between the image and the DEM as precise as possible

Analysis of the Sensitivity of the End-Of-Turn Detection Task to Errors Generated by the Automatic Speech Recognition Process

An End-Of-Turn Detection Module (EOTD-M) is an essential component of automatic Spoken Dialogue Systems. The capability of correctly detecting whether a user?s utterance has ended or not improves the accuracy in interpreting the meaning of the message and decreases the latency in the answer. Usually, in dialogue systems, an EOTD-M is coupled with an Automatic Speech Recognition Module (ASR-M) to transmit complete utterances to the Natural Language Understanding unit. Mistakes in the ASR-M transcription can have a strong effect on the performance of the EOTD-M. The actual extent of this effect depends on the particular combination of ASR M transcription errors and the sentence featurization techniques implemented as part of the EOTD-M. In this paper we investigate this important relationship for an EOTD-M based on semantic information and particular characteristics of the speakers (speech profiles). We introduce an Automatic Speech Recognition Simulator (ASR-SIM) that models different types of semantic mistakes in the ASR-M transcription as well as different speech profiles. We use the simulator to evaluate the sensitivity to ASR-M mistakes of a Long Short-Term Memory network classifier trained in EOTD with different featurization techniques. Our experiments reveal the different ways in which the performance of the model is influenced by the ASR-M errors. We corroborate that not only is the ASR-SIM useful to estimate the performance of an EOTD-M in customized noisy scenarios, but it can also be used to generate training datasets with the expected error rates of real working conditions, which leads to better performance.The research presented in this paper has been conducted as part of the project EMPATHIC that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 769872. Jose A. Lozano is partially supported by the Basque Government through the BERC 2018-2021 program, IT1244-19 and grant "Artificial Intelligence in BCAM number EXP. 2019/00432'' and by the Spanish Ministry of Science, Innovation and Universities: BCAM Severo Ochoa accreditation SEV-2017-0718, TIN2016-78365-R and PID2019-104966GB-I00. And R. Santana acknowledge support by the Spanish Ministry of Science, Innovation and Universities (Project TIN201678365-R and PID2019-104966GB-I00), and the Basque Government (IT1244-19 and ELKARTEK Programs

Analysis of the Sensitivity of the End-Of-Turn Detection Task to Errors Generated by the Automatic Speech Recognition Process

An End-Of-Turn Detection Module (EOTD-M) is an essential component of automatic Spoken Dialogue Systems. The capability of correctly detecting whether a user?s utterance has ended or not improves the accuracy in interpreting the meaning of the message and decreases the latency in the answer. Usually, in dialogue systems, an EOTD-M is coupled with an Automatic Speech Recognition Module (ASR-M) to transmit complete utterances to the Natural Language Understanding unit. Mistakes in the ASR-M transcription can have a strong effect on the performance of the EOTD-M. The actual extent of this effect depends on the particular combination of ASR M transcription errors and the sentence featurization techniques implemented as part of the EOTD-M. In this paper we investigate this important relationship for an EOTD-M based on semantic information and particular characteristics of the speakers (speech profiles). We introduce an Automatic Speech Recognition Simulator (ASR-SIM) that models different types of semantic mistakes in the ASR-M transcription as well as different speech profiles. We use the simulator to evaluate the sensitivity to ASR-M mistakes of a Long Short-Term Memory network classifier trained in EOTD with different featurization techniques. Our experiments reveal the different ways in which the performance of the model is influenced by the ASR-M errors. We corroborate that not only is the ASR-SIM useful to estimate the performance of an EOTD-M in customized noisy scenarios, but it can also be used to generate training datasets with the expected error rates of real working conditions, which leads to better performance.The research presented in this paper has been conducted as part of the project EMPATHIC that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 769872. Jose A. Lozano is partially supported by the Basque Government through the BERC 2018-2021 program, IT1244-19 and grant "Artificial Intelligence in BCAM number EXP. 2019/00432'' and by the Spanish Ministry of Science, Innovation and Universities: BCAM Severo Ochoa accreditation SEV-2017-0718, TIN2016-78365-R and PID2019-104966GB-I00. And R. Santana acknowledge support by the Spanish Ministry of Science, Innovation and Universities (Project TIN201678365-R and PID2019-104966GB-I00), and the Basque Government (IT1244-19 and ELKARTEK Programs