Parameterless-Growing-SOM and Its Application to a Voice Instruction Learning System

An improved self-organizing map (SOM), parameterless-growing-SOM (PL-G-SOM), is proposed in this paper. To overcome problems existed in traditional SOM (Kohonen, 1982), kinds of structure-growing-SOMs or parameter-adjusting-SOMs have been invented and usually separately. Here, we combine the idea of growing SOMs (Bauer and Villmann, 1997; Dittenbach et al. 2000) and a parameterless SOM (Berglund and Sitte, 2006) together to be a novel SOM named PL-G-SOM to realize additional learning, optimal neighborhood preservation, and automatic tuning of parameters. The improved SOM is applied to construct a voice instruction learning system for partner robots adopting a simple reinforcement learning algorithm. User's instructions of voices are classified by the PL-G-SOM at first, then robots choose an expected action according to a stochastic policy. The policy is adjusted by the reward/punishment given by the user of the robot. A feeling map is also designed to express learning degrees of voice instructions. Learning and additional learning experiments used instructions in multiple languages including Japanese, English, Chinese, and Malaysian confirmed the effectiveness of our proposed system

Análisis Interactivo de Datos: Mapas Autoorganizados

Los fenómenos físicos, así como los procesos industriales, producen volúmenes cada vez más cuantiosos de datos, a menudo de difícil tratamiento. Se hace preceptiva la generación de sistemas y procedimientos que permitan extraer, en una primera etapa visual de análisis, la información subyacente a estos datos, orientando así los cálculos y estudios posteriores que puedan aplicarse. El análisis visual será reforzado si se complementa con elementos interactivos que permitan al usuario dirigirse hacia los focos de su interés. Dentro de las técnicas de visualización de datos para los fines expuestos destacan los Mapas Autoorganizados (también llamados SOM, acrónimo de Self-Organizing Maps), un tipo de redes neuronales visuales cuya efectividad será potenciada si son dotados de interactividad. Las aportaciones destacadas del presente trabajo son: 1. Desarrollo de una herramienta de código abierto de ayuda en entornos tecnológicos, académicos e industriales, que incorpora entrenamiento de Mapas Autoorganizados y sistema interactivo de visualización de resultados. Dicha herramienta constituye un prototipo de código abierto, fácil de modificar y escalar, y compatible con los sistemas operativos de uso más habitual en el mercado. Se realiza el entrenamiento de diferentes modelos de Mapas Autoorganizados: GSOM (Growing SOM), GHSOM (Growing Hierarchical SOM) y una nueva propuesta: GCHSOM. Los entrenamientos permiten diversidad de parámetros y se realizan por lotes, almacenando los resultados que presenten mejores mediciones en sus medidas de calidad (error de cuantificación, error topográfico e índice de Kaski y Lagus). 2. Presentación de los Mapas Autoorganizados como herramienta de visualización de datos para su empleo en fases iniciales de análisis de información. Sus propiedades de cuantificación de datos y proyección de las relaciones existentes entre éstos en espacios de bajas dimensiones los convierten en útiles muy eficaces para el análisis visual de información. 3. Mejora de los Mapas Autoorganizados mediante la adición de interactividad, como respuesta a las necesidades actuales de análisis visual de datos. Entre otras opciones, se destaca la realización de una selección de distintos modos de color (secuencial, escala de grises, bipolares secuenciales, escalas de pseudocolores) que por sus características facilitarán en algunos casos, la discriminación, y en otros, la cuantificación. Además se realizan agrupamientos de la información que facilitan su comprensión. Las opciones interactivas de tratamiento de los datos facilitan la discriminación de la información, permitiendo así la visualización de muchas capas. 4. Optimizaciones en el proceso de entrenamiento de grupos de Mapas Autoorganizados para un mismo conjunto de datos para, posteriormente, seleccionar de entre éstos al que reúna mejores condiciones. Se realizan una serie de mejoras sobre el algoritmo base de entrenamiento (paralelismo, cacheado de cálculos) que facilitan la realización de experimentos. 5. Valoración de modelos de entrenamiento y propuesta de variantes en el ámbito de visualización de datos. Concretamente se propone una variante de Mapa Autoorganizado bautizada como GCHSOM (Growing Cluster Hierarchical SOM) que consiste en una estructura de Mapas Autoorganizados de tipo GSOM. Esta variante permite que los datos a analizar se muestren gráficamente en una primera instancia con alto nivel de detalle, permitiendo además al investigador realizar nuevas consultas visuales siguiendo una estructura jerárquica que le guiará permitiendo el acceso a diferentes subconjuntos o contextos de los datos, proporcionando nuevos matices sobre cada uno de estos contextos.Physical phenomena, as well as industrial processes, produce increasingly large volumes of data, often of dificult treatment. It makes mandatory the generation of systems and procedures that allow to extract, in the first analysis stage, the underlying information to this data, facing this way the calculations and later studies that could be applied. Visual analysis will be strengthened if it is supplemented with interactive elements that allow the user to move towards the foci of interest. Among the techniques of visualization of data to the exposed ends are the Self-Organizing Maps (SOM), a kind of neural network whose effectiveness will be enhanced if they are endowed with interactivity. The contributions of this work are: 1. Development of an open source tool to aid in technological, academic and industrial environments, which incorporates training of Self-Organizing Maps and an interactive display of visualization of results. It is worth developing as it is difficult to find free software that can be ported to different operating systems. As it is open source, it can be modified to accomplish different purposes. Also, as its interface is made on the Processing framework, it is near-direct portable to Linux, Mac OS X and Windows, and makes use of Processing powerful graphics library. This software is of particular interest as it allows navigation through structures formed by different neural networks, compared to flat representations of the same. It can train several SOM models: GSOM (Growing SOM), GHSOM (Growing Hierarchical SOM) and a new proposal: GCHSOM. It allows diversity of training parameters and can perform batch, storing the resulting nets that present better measures in their measures of quality (quantization error, topographic error, and Kaski and Lagus rate). The users can choose the training options via the client interface, and select the data to be treated. Then they choose the number of times to repeat the experiment. According to those inputs, this piece of code provides the best SOM networks based on three qualitycriteria: a) quantization error b) topographical error. c) Kaski and Lagus Index 2. Presentation of Self-Organizing Maps as a data visualization tool for use in early stages of analysis. Its properties of data quantification and projection of the relationships between data on low-dimensional spaces make them very effective useful for visual analysis. 3. Improvement of Self-Organizing Maps by adding interactivity, in response to the current needs of visual data analysis. The user is offered the following options: a) Saving and loading the different experiments that have been conducted. b) Selecting the network that complies with the quality criteria for each training. c) Request new clusters of data for the calculated network. d) Select the color map used (sequential, grayscale, sequential bipolar scales, pseudo colors) whose characteristics provide in some cases, discrimination, and other, quantification. e) Manipulate the results (hiding on, pan, and zoom). f) Different partial selection modes for the display of summary information. g) The ability to re-train with subsets of information results. In short, the graphic interface serves not only as a bridge between the user and the training libraries, but also provides a number of functions that facilitate interactive data analysis and navigation through the most complex hierarchical structures. 4. Optimizations in the process of batch training Self-Organizing Maps groups for the same set of data to select from among them the ones which meet best conditions. A number of improvements on the training base (parallelism, caching calculations) that facilitate algorithm performing experiments are implemented. 5. Assessment of training models and variants proposed in the field of data visualization. A new proposal has been made, variant from GHSOM: GCHSOM (Hierarchical Cluster Growing SOM) consisting of a hierarchical structure of GSOM. This option allows data to be analyzed is graphically displayed at first sight with high level of detail, also allowing the researchers to make new visual queries following a hierarchical structure that will guide them, allowing the access to different subsets of data, providing new nuances of each of these contexts

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Standardized development of computer software. Part 2: Standards

This monograph contains standards for software development and engineering. The book sets forth rules for design, specification, coding, testing, documentation, and quality assurance audits of software; it also contains detailed outlines for the documentation to be produced

Studies related to the process of program development

The submitted work consists of a collection of publications arising from research carried out at Rhodes University (1970-1980) and at Heriot-Watt University (1980-1992). The theme of this research is the process of program development, i.e. the process of creating a computer program to solve some particular problem. The papers presented cover a number of different topics which relate to this process, viz. (a) Programming methodology programming. (b) Properties of programming languages. aspects of structured. (c) Formal specification of programming languages. (d) Compiler techniques. (e) Declarative programming languages. (f) Program development aids. (g) Automatic program generation. (h) Databases. (i) Algorithms and applications