Un algoritmo en seudocodigo para el chequeo de la subsumicion en alc

En el presente artículo se describe un evaluador de satisfactibilidad para el chequeo de la subsumición en un lenguaje de atributos de conceptos (Attribute Language Concept, ALC). Los lenguajes de conceptos basados en las lógicas descriptivas (Description Logics, DLs) ofrecen servicios de razonamiento que permiten hacer clasificación y recuperación de la información dentro de la base de conocimiento. Los procesos de razonamiento de subsumición y de satisfactibilidad son equivalentes y se especifican por medio del Cálculo de Predicados de Primer Orden (First Order Predicate Calculus, FOPC) y el cálculo Tableaux. FOPC permite asociar cada expresión C de conceptos a una fórmula f c (x) de la lógica de predicados, de tal forma que un modelo de una fórmula f c (x) es un modelo del concepto C y viceversa. El cálculo Tableaux de primer orden siempre termina para las fórmulas asociadas a conceptos en el FOPC. El cálculo de terminación planteado permite una interpretación si la fórmula es satisfactible o se produce una contradicción si la fórmula es insatisfactible. Se plantea un algoritmo en seudocódigo para el chequeo de la subsumición.

Detección de modos de transporte usando datos GPS

The use of mobile devices and GPS technology allow the implementation of systems to analyze the context and typical transport activities of a user, through the analysis of the location data and acceleration sensors. This research includes the processing of data obtained via GPS. This processing is intended to detect the mode of transport of a user in segments of predefined paths. For classification, velocity profiles that identify modes of transport in each segment are used. The software implements a Java programming language and the use of Matlab for analysis and data filters. The software system is developed into two components; the first comprises the filter and transformation of data. These data are plotted from decimal coordinates to cartesian coordinates. The second presents the classification for the detection of transport modes with cartesian coordinates. It also contains the analysis of states of kinematic movements. The tests are performed through a dataset taken from the GeoLife project of Microsoft Asia. The obtained results show a coherent detection on the means of transport that the different users use. These users are compared from predefined speed profiles.Keywords: Transportation mode detection, GPS, multimodal transport.El uso de dispositivos móviles y el aprovechamiento de la tecnología GPS, permiten la implementación de sistemas para analizar el contexto y actividades típicas de transporte de un usuario, a través del análisis de los datos de localización y sensores de aceleración. Este trabajo de investigación comprende el procesamiento de datos obtenidos vía GPS. Con este procesamiento se pretende detectar el modo de transporte de un usuario en segmentos de recorridos predefinidos. Para la clasificación de éstos, se usan perfiles de velocidad que identifican los modos de transporte en cada uno de los segmentos, mediante un sistema software en lenguaje de programación Java y la utilización de Matlab para el análisis y filtros de datos. El sistema software se desarrolla en dos componentes, el primero comprende el filtro y transformación de datos. Estos datos se representan en coordenadas decimales a coordenadas cartesianas. El segundo presenta la clasificación, para la detección de modos de transportes con las coordenadas cartesianas. También contiene el análisis de estados de movimientos cinemáticos. Las pruebas se realizan a través de un dataset tomado del proyecto GeoLife de Microsoft Asia. Los resultados obtenidos muestran una detección coherente sobre los medios de transporte que usan los diferentes usuarios. Estos usuarios se comparan a partir de perfiles de velocidad predefinidos.Palabras Clave: Detección de modos de transporte, GPS, transporte multimodal

Advances in knowledge and computational modeling of the autistic brain: A literature review

El estudio del funcionamiento del cerebro permite, no sólo el descubrimiento de sus principios, sino también en la construcción de máquinas que lo emulen cada vez más inteligentes. En ese sentido, las neurociencias están aportando importantes conocimientos sobre cómo los diferentes elementos del cerebro interactúan en el procesamiento de información, para dar origen a funciones cognitivas de alto nivel (aprendizaje, conciencia, qualía, etc.), que caracterizan la conducta humana. Por otra parte, existen cerebros que viene con una maquinaria neuronal distinta caracterizados por sus capacidades cognitivas extraordinarias, comúnmente conocidos como autistas. A partir de estos dos hechos se planteó el siguiente interrogante. ¿Qué tanto se sabe sobre el autismo y como se ha avanzado en su modelado a nivel computacional?. Este artículo da una respuesta particular a modo de síntesis teórica del fenómeno autista y avances que a nivel computacional se han logrado en cuanto a simulación, emulación y desarrollo de herramientas de apoyo relacionados con este complejo fenómeno. Lo anterior con base en más de 50 estudios tomados de bases de datos científicas, tales como: Nature, Scopus, ACM, IEEE, Google scholar, entre otras.The study of the functioning of the brain allows, not only the discovery of its principles, but also in the construc-tion of machines that emulate getting smarter. In that sense, neurosciences are providing important insights into how different elements of the brain interact in information processing to give rise to high-level cognitive func-tions (learning, awareness, quality, etc.) that characterize human behavior. On the other hand, there are brains that come with distinct neuronal machinery characterized by their extraordinary cognitive abilities, commonly known as autistic. From these two facts the following question arises. How much is known about autism and how it has advanced in its modeling at the computational level?. This article gives a particular answer as a theoretical synthesis of the autistic phenomenon and advances that at computational level have been achieved in relation to simulation, emulation and development of support tools related to this complex phenomenon. The above based on more than 50 studies taken from scientific databases, such as: Nature, Scopus, ACM, IEEE, Google Scholar, among others

Learning algorithm for the recursive pattern recognition model

In this work, we incorporate a learning algorithm to the recursive pattern recognition model, based on the systematic functioning of the human neocortex presented in previous works. This algorithm has two mechanisms: the first, called Aprendizaje_nuevo, is used to learn new patterns and creates a new pattern recognition module in the model. The other, called Aprendizaje_por_refuerzo, is used to reinforce a pattern and adapts the module that represents the pattern to the changes in it. The algorithm is tested in various contexts (text and images) to analyze its capacities of learning and of recognition of the model

A recursive pattern recognition algorithm

En este trabajo, proponemos un algoritmo de reconocimiento de patrones basado en el funcionamiento sistemático del neocórtex humano, en procesos de reconocimiento de patrones. El algoritmo explota la idea de recursividad, basada en la jerarquía neocortical y en la desagregación/integración del patrón en el proceso de reconocimiento. El algoritmo propuesto es probado para analizar sus capacidades de reconocimiento de patrones.This work presents a pattern recognition algorithm based on the systematic functioning of the human neocortex, in processes of pattern recognition. The algorithm exploits the idea of recursivity, based on the neocortical hierarchy and the decomposition/integration in the recognition process. The proposed algorithm is tested to analyze its capabilities of pattern recognition

GeoMotor: Design with nature. Recognition of geometries using a convolutional neural-network approach (CNN)

This article conceptualizes a solid regular called GeoMotor capable of moving and directing the sediments of a mountain river and changing its geography. The GeoMotor manages to manipulate the directional growth of sediments in an artificial environment, unveiling emerging architectural structures. For this, an analog simulation of the mountain river flow was performed and provide data to understand the phenomenon. Subsequently, this data was used to train a neural network that recognizes the emerging architectural patterns. As future work, it is planned to improve the models to offer functionalities beyond the orthodox practices of traditional architectonic model

A recursive patterns matching model for the dynamic pattern recognition problem

This paper defines a new recursive pattern matching model based on the theory of the systemic functioning of the human brain, called pattern recognition theory of mind, in the context of the dynamic pattern recognition problem. Dynamic patterns are characterized by having properties that change in intervals of time, such as a pedestrian walking or a car running (the negation of a dynamic pattern is a static pattern). Novel contributions of this paper include: (1) Formally develop the concepts of dynamic and static pattern, (2) design a recursive pattern matching model, which exploits the idea of recursivity and time series in the recognition process, and the unbundling/integration of pattern to recognize, and (3) develop strategies of pattern matching from two major orientations: recognition of dynamic patterns oriented by characteristic, or oriented by perception. The model is instantiated in several cases, to analyze its performance

Analysis of the emotions in a multi-robot system in emergent contexts

In this paper, we propose an emotional model for robots in a multi-robot system, in order to allow emerging behaviors. The emotional model uses four universal emotions: anger, disgust, sadness, and joy, assigned to each robot based on the level of satisfaction of its basic needs. These four universal emotions lie on a spectrum where depending where the emotion of the robot lies, can affect its behavior and of its neighboring robots. The more negative the emotion is, the more individualistic it becomes in its decisions (anger, sadness or disgust). The more positive the robot is in its emotion, the more it will consider the group and global goals (joy). Each robot is able to recognize another robot′s emotion in the system based on their current state, using the AR2P (AR2P for its acronym in Spanish: Algoritmo Recursivo de Reconocimiento de Patrones) recognition algorithm. In this way, it can use this information of the emotions to decide with whom collaborate. Specifically, the paper addresses emotions’ influence on the behavior of the system, at the individual and collective levels, and the emotions’ effects on the emergent behaviors of the multi-robot system. The paper explores the emerging behavior in two multi-robot scenarios; nectar harvesting and object transportation. The results show that the emotions are important to the emergent behavior in a multi-robot system

A recursive pattern recognition approach to selection web services in cloud environment

There are optimization problems in the Cloud for the selection of web services, due to the large number of services available by different cloud providers and the diversity of quality of service parameters of each of them. This work proposes the adaptation of a pattern recognition model based on the systematic functioning of the brain called Ar2p for the selection of web services in composition activities in Cloud environments. The web serice are represented as patterns to be recognized by Ar2p, which determines the necessary and sufficient web services that constitute the composition of services that meet its functional and non-functional requirements. The services composition and activity selection have been formalized through a logical-mathematical model of web service recognition mechanisms in two steps, one that describes the syntactic search of the service and the second, which offers filtering through quality of service parameters. An adaptive implementation of the final model allows its recognition modules to be provided with any desired optimization strategy

Transportation Mode Detection using GPS Data

El uso de dispositivos móviles y el aprovechamiento de la tecnología GPS, permiten la implementación de sistemas para analizar el contexto y actividades típicas de transporte de un usuario, a través del análisis de los datos de localización y sensores de aceleración. Este trabajo de investigación comprende el procesamiento de datos obtenidos vía GPS. Con este procesamiento se pretende detectar el modo de transporte de un usuario en segmentos de recorridos predefinidos. Para la clasificación de éstos, se usan perfiles de velocidad que identifican los modos de transporte en cada uno de los segmentos, mediante un sistema software en lenguaje de programación Java y la utilización de Matlab para el análisis y filtros de datos. El sistema software se desarrolla en dos componentes, el primero comprende el filtro y transformación de datos. Estos datos se representan en coordenadas decimales a coordenadas cartesianas. El segundo presenta la clasificación, para la detección de modos de transportes con las coordenadas cartesianas. También contiene el análisis de estados de movimientos cinemáticos. Las pruebas se realizan a través de un dataset tomado del proyecto GeoLife de Microsoft Asia. Los resultados obtenidos muestran una detección coherente sobre los medios de transporte que usan los diferentes usuarios. Estos usuarios se comparan a partir de perfiles de velocidad predefinidos.The use of mobile devices and GPS technology allow the implementation of systems to analyze the context and typical transport activities of a user, through the analysis of the location data and acceleration sensors. This research includes the processing of data obtained via GPS. This processing is intended to detect the mode of transport of a user in segments of predefined paths. For classification, velocity profiles that identify modes of transport in each segment are used. The software implements a Java programming language and the use of Matlab for analysis and data filters. The software system is developed into two components; the first comprises the filter and transformation of data. These data are plotted from decimal coordinates to cartesian coordinates. The second presents the classification for the detection of transport modes with cartesian coordinates. It also contains the analysis of states of kinematic movements. The tests are performed through a dataset taken from the GeoLife project of Microsoft Asia. The obtained results show a coherent detection on the means of transport that the different users use. These users are compared from predefined speed profiles