Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a "divide-and-conquer" effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

Texture analysis of high resolution MRI allows discrimination between febrile and afebrile initial precipitating injury in mesial temporal sclerosis

A computational pipeline combining texture analysis and pattern classification algorithms was developed for investigating associations between high-resolution MRI features and histological data. This methodology was tested in the study of dentate gyrus images of sclerotic hippocampi resected from refractory epilepsy patients. Images were acquired using a simple surface coil in a 3.0T MRI scanner. All specimens were subsequently submitted to histological semiquantitative evaluation. The computational pipeline was applied for classifying pixels according to: a) dentate gyrus histological parameters and b) patients' febrile or afebrile initial precipitating insult history. The pipeline results for febrile and afebrile patients achieved 70% classification accuracy, with 78% sensitivity and 80% specificity [area under the reader observer characteristics (ROC) curve: 0.89]. The analysis of the histological data alone was not sufficient to achieve significant power to separate febrile and afebrile groups. Interesting enough, the results from our approach did not show significant correlation with histological parameters (which per se were not enough to classify patient groups). These results showed the potential of adding computational texture analysis together with classification methods for detecting subtle MRI signal differences, a method sufficient to provide good clinical classification. A wide range of applications of this pipeline can also be used in other areas of medical imaging. Magn Reson Med, 2012. (c) 2012 Wiley Periodicals, Inc.

Expanding the horizontal capabilities of CRT monitors using artificial inter-pixel steps for neuroscience experiments

This article describes the development of a visual stimulus generator to be used in neuroscience experiments with invertebrates such as flies. The experiment consists in the visualization of a fixed image that is displaced horizontally according to the stimulus data. The system is capable of displaying 640 x 480 pixels with 256 intensity levels at 200 frames per second (FPS) on conventional raster monitors. To double the possible horizontal positioning possibilities from 640 to 1280, a novel technique is presented introducing artificial inter-pixel steps. The implementation consists in using two video frame buffers containing each a distinct view of the desired image pattern. This implementation generates a visual effect capable of doubling the horizontal positioning capabilities of the visual stimulus generator allowing more precise and movements more contiguous. (C) 2011 Elsevier Inc. All rights reserved.

Rare element concentrations measured on filtered seawater of water bottle samples during POLARSTRERN cruise ANT-XXIV/3 (GIPY5)

The concentrations of rare earth elements (REEs) in seawater display systematic variations related to weathering inputs, particle scavenging and water mass histories. Here we investigate the REE concentrations of water column profiles in the Atlantic sector of the Southern Ocean, a key region of the global circulation and primary production. The data reveal a pronounced contrast between the vertical profiles in the Antarctic Circumpolar Current (ACC) and those to the south of the ACC in the Weddell Gyre (WG). The ACC profiles exhibit the typical increase of REE concentrations with water depth and a change in the shape of the profiles from near linear for the light REEs to more convex for the heavy REEs. In contrast, the WG profiles exhibit high REE concentrations throughout the water column with only the near surface samples showing slightly reduced concentrations indicative of particle scavenging. Seawater normalised REE patterns reveal the strong remineralisation signal in the ACC with the light REEs preferentially removed in surface waters and the mirror image pattern of their preferential release in deep waters. In the WG the patterns are relatively homogenous reflecting the prevalence of well-mixed Lower Circumpolar Deep Water (LCDW) that follows shoaling isopycnals in the region. In the WG particle scavenging of REEs is comparatively small and limited to the summer months by light limitation and winter sea ice cover. Considering the surface water depletion compared to LCDW and that the surface waters of the WG are replaced every few years, the removal rate is estimated to be on the order of 1 nmol/m3/yr for La and Nd. The negative cerium anomalies observed in deep waters are some of the strongest found globally with only the deepest waters in parts of the Pacific having stronger anomalies. These deep waters have been isolated from fresh continental REE inputs during their long journey through the abyssal Indo-Pacific ocean and suggests that the high REE concentrations found in the ACC and WG reflect contributions from old deep waters.

Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The focus of this chapter is to study feature extraction and pattern classification methods from two medical areas, Stabilometry and Electroencephalography (EEG). Stabilometry is the branch of medicine responsible for examining balance in human beings. Balance and dizziness disorders are probably two of the most common illnesses that physicians have to deal with. In Stabilometry, the key nuggets of information in a time series signal are concentrated within definite time periods are known as events. In this chapter, two feature extraction schemes have been developed to identify and characterise the events in Stabilometry and EEG signals. Based on these extracted features, an Adaptive Fuzzy Inference Neural network has been applied for classification of Stabilometry and EEG signals.

Sistema de autenticación accesible para el hogar digital

El Hogar Digital Accesible (HDA) de la ETSIST nace con el propósito de acercar las nuevas Tecnologías de la Información a las personas que precisan de necesidades concretas de accesibilidad y usabilidad, dotándoles de herramientas que les permitan aumentar su calidad de vida, confort, seguridad y autonomía. El entorno del HDA consta de elementos de control para puertas, persianas, iluminación, agua o gas, sensores de temperatura, incendios, gas, sistemas de climatización, sistemas de entretenimiento y sistemas de seguridad tales como detectores de presencia y alarmas. Todo ello apoyado sobre una arquitectura de red que proporciona una pasarela residencial y un acceso a banda ancha. El objetivo principal de este PFG ha sido el desarrollo de un sistema de autenticación para el Hogar Digital Accesible de bajo coste. La idea de integrar un sistema de autenticación en el HDA, surge de la necesidad de proteger de accesos no deseados determinados servicios disponibles dentro de un ámbito privado. Algunos de estos servicios pueden ser tales como el acceso a la lectura de los mensajes disponibles en el contestador automático, el uso de equipos multimedia, la desconexión de alarmas de seguridad o simplemente la configuración de ambientes según el usuario que esté autenticado (intensidad de luz, temperatura de la sala, etc.). En el desarrollo han primado los principios de accesibilidad, usabilidad y seguridad necesarios para la creación de un entorno no invasivo, que permitiera acreditar la identidad del usuario frente al sistema HDA. Se ha planteado como posible solución, un sistema basado en el reconocimiento de un trazo realizado por el usuario. Este trazo se usará como clave de cara a validar a los usuarios. El usuario deberá repetir el trazado que registró en el sistema para autenticarse. Durante la ejecución del presente PFG, se justificará la elección de este mecanismo de autenticación frente a otras alternativas disponibles en el mercado. Para probar la aplicación, se ha podido contar con dos periféricos de distintas gamas, el uDraw creado para la PS3 que se compone de una tableta digitalizadora y un lápiz que permite recoger los trazos realizados por el usuario de forma inalámbrica y la tableta digitalizadora Bamboo de Wacom. La herramienta desarrollada permite a su vez, la posibilidad de ser usada por otro tipo de dispositivos como es el caso del reloj con acelerómetro de 3 ejes de Texas Instruments Chronos eZ430 capaz de trasladar los movimientos del usuario al puntero de un ratón. El PFG se encuentra dividido en tres grandes bloques de flujo de trabajo. El primero se centra en el análisis del sistema y las tecnologías que lo componen, incluyendo los distintos algoritmos disponibles para realizar la autenticación basada en reconocimiento de patrones aplicados a imágenes que mejor se adaptan a las necesidades del usuario. En el segundo bloque se recoge una versión de prueba basada en el análisis y el diseño UML realizado previamente, sobre la que se efectuaron pruebas de concepto y se comprobó la viabilidad del proyecto. El último bloque incluye la verificación y validación del sistema mediante pruebas que certifican que se han alcanzado los niveles de calidad necesarios para la consecución de los objetivos planteados, generando finalmente la documentación necesaria. Como resultado del trabajo realizado, se ha obtenido un sistema que plantea una arquitectura fácilmente ampliable lograda a través del uso de técnicas como la introspección, que permiten separar la lógica de la capa de negocio del código que la implementa, pudiendo de forma simple e intuitiva sustituir código mediante ficheros de configuración, lo que hace que el sistema sea flexible y escalable. Tras la realización del PFG, se puede concluir que el producto final obtenido ha respondido de forma satisfactoria alcanzando los niveles de calidad requeridos, siendo capaz de proporcionar un sistema de autenticación alternativo a los convencionales, manteniendo unas cotas de seguridad elevadas y haciendo de la accesibilidad y el precio sus características más reseñables. ABSTRACT. Accessible Digital Home (HDA) of the ETSIST was created with the aim of bringing the latest information and communications technologies closer to the people who has special needs of accessibility and usability increasing their quality of life, comfort, security and autonomy. The HDA environment has different control elements for doors, blinds, lighting, water or gas, temperature sensors, fire protection systems, gas flashover, air conditioning systems, entertainments systems and security systems such as intruders detectors and alarms. Everything supported by an architecture net which provides a broadband residential services gateway. The main goal of this PFG was the development of a low-cost authentication system for the Accessible Digital Home. The idea of integrating an authentication system on the HDA, stems from the need to safeguard certain private key network resources from unauthorized access. Some of said resources are the access to the answering machine messages, the use of multimedia devices, the alarms deactivation or the parameter settings for each environment as programmed by the authenticated user (light intensity, room temperature, etc.). During the development priority was given to concepts like accessibility, usability and security. All of them necessary to create a non invasive environment that allows the users to certify their identity. A system based on stroke pattern recognition, was considered as a possible solution. This stroke is used as a key to validate users. The user must repeat the stroke that was saved on the system to validate access. The selection of this authentication mechanism among the others available options will be justified during this PFG. Two peripherals with different ranges were used to test the application. One of them was uDraw design for the PS3. It is wireless and is formed by a pen and a drawing tablet that allow us to register the different strokes drawn by the user. The other one was the Wacom Bamboo tablet, that supports the same functionality but with better accuracy. The developed tool allows another kind of peripherals like the 3-axes accelerometer digital wristwatch Texas Instruments Chronos eZ430 capable of transfering user movements to the mouse cursor. The PFG is divided by three big blocks that represent different workflows. The first block is focused on the system analysis and the technologies related to it, including algorithms for image pattern recognition that fits the user's needs. The second block describes how the beta version was developed based on the UML analysis and design previously done. It was tested and the viability of the project was verified. The last block contains the system verification and validation. These processes certify that the requirements have been fulfilled as well as the quality levels needed to reach the planned goals. Finally all the documentation has been produced. As a result of the work, an expandable system has been created, due to the introspection that provides the opportunity to separate the business logic from the code that implements it. With this technique, the code could be replaced throughout configuration files which makes the system flexible and highly scalable. Once the PFG has finished, it must therefore be concluded that the final product has been a success and high levels of quality have been achieved. This authentication tool gives us a low-cost alternative to the conventional ones. The new authentication system remains security levels reasonably high giving particular emphasis to the accessibility and the price.

Sistema de seguridad basado en la clasificación de eventos sonoros

La teoría de reconocimiento y clasificación de patrones y el aprendizaje automático son actualmente áreas de conocimiento en constante desarrollo y con aplicaciones prácticas en múltiples ámbitos de la industria. El propósito de este Proyecto de Fin de Grado es el estudio de las mismas así como la implementación de un sistema software que dé solución a un problema de clasificación de ruido impulsivo, concretamente mediante el desarrollo de un sistema de seguridad basado en la clasificación de eventos sonoros en tiempo real. La solución será integral, comprendiendo todas las fases del proceso, desde la captación de sonido hasta el etiquetado de los eventos registrados, pasando por el procesado digital de señal y la extracción de características. Para su desarrollo se han diferenciado dos partes fundamentales; una primera que comprende la interfaz de usuario y el procesado de la señal de audio donde se desarrollan las labores de monitorización y detección de ruido impulsivo y otra segunda centrada únicamente en la clasificación de los eventos sonoros detectados, definiendo una arquitectura de doble clasificador donde se determina si los eventos detectados son falsas alarmas o amenazas, etiquetándolos como de un tipo concreto en este segundo caso. Los resultados han sido satisfactorios, mostrando una fiabilidad global en el proceso de entorno al 90% a pesar de algunas limitaciones a la hora de construir la base de datos de archivos de audio, lo que prueba que un dispositivo de seguridad basado en el análisis de ruido ambiente podría incluirse en un sistema integral de alarma doméstico aumentando la protección del hogar. ABSTRACT. Pattern classification and machine learning are currently expertise areas under continuous development and also with extensive applications in many business sectors. The aim of this Final Degree Project is to study them as well as the implementation of software to carry on impulsive noise classification tasks, particularly through the development of a security system based on sound events classification. The solution will go over all process stages, from capturing sound to the labelling of the events recorded, without forgetting digital signal processing and feature extraction, everything in real time. In the development of the Project a distinction has been made between two main parts. The first one comprises the user’s interface and the audio signal processing module, where monitoring and impulsive noise detection tasks take place. The second one is focussed in sound events classification tasks, defining a double classifier architecture where it is determined whether detected events are false alarms or threats, labelling them from a concrete category in the latter case. The obtained results have been satisfactory, with an overall reliability of 90% despite some limitations when building the audio files database. This proves that a safety device based on the analysis of environmental noise could be included in a full alarm system increasing home protection standards.

Aplicação de técnicas inteligentes com análise no domínio do tempo para reconhecimento de defeitos em motores de indução trifásicos

Os motores de indução trifásicos são os principais elementos de conversão de energia elétrica em mecânica motriz aplicados em vários setores produtivos. Identificar um defeito no motor em operação pode fornecer, antes que ele falhe, maior segurança no processo de tomada de decisão sobre a manutenção da máquina, redução de custos e aumento de disponibilidade. Nesta tese são apresentas inicialmente uma revisão bibliográfica e a metodologia geral para a reprodução dos defeitos nos motores e a aplicação da técnica de discretização dos sinais de correntes e tensões no domínio do tempo. É também desenvolvido um estudo comparativo entre métodos de classificação de padrões para a identificação de defeitos nestas máquinas, tais como: Naive Bayes, k-Nearest Neighbor, Support Vector Machine (Sequential Minimal Optimization), Rede Neural Artificial (Perceptron Multicamadas), Repeated Incremental Pruning to Produce Error Reduction e C4.5 Decision Tree. Também aplicou-se o conceito de Sistemas Multiagentes (SMA) para suportar a utilização de múltiplos métodos concorrentes de forma distribuída para reconhecimento de padrões de defeitos em rolamentos defeituosos, quebras nas barras da gaiola de esquilo do rotor e curto-circuito entre as bobinas do enrolamento do estator de motores de indução trifásicos. Complementarmente, algumas estratégias para a definição da severidade dos defeitos supracitados em motores foram exploradas, fazendo inclusive uma averiguação da influência do desequilíbrio de tensão na alimentação da máquina para a determinação destas anomalias. Os dados experimentais foram adquiridos por meio de uma bancada experimental em laboratório com motores de potência de 1 e 2 cv acionados diretamente na rede elétrica, operando em várias condições de desequilíbrio das tensões e variações da carga mecânica aplicada ao eixo do motor.

Análise de grandezas cinemáticas e dinâmicas inerentes à hemiparesia através da descoberta de conhecimento em bases de dados

Em virtude de uma elevada expectativa de vida mundial, faz-se crescente a probabilidade de ocorrer acidentes naturais e traumas físicos no cotidiano, o que ocasiona um aumento na demanda por reabilitação. A terapia física, sob o paradigma da reabilitação robótica com serious games, oferece maior motivação e engajamento do paciente ao tratamento, cujo emprego foi recomendado pela American Heart Association (AHA), apontando a mais alta avaliação (Level A) para pacientes internados e ambulatoriais. No entanto, o potencial de análise dos dados coletados pelos dispositivos robóticos envolvidos é pouco explorado, deixando de extrair informações que podem ser de grande valia para os tratamentos. O foco deste trabalho consiste na aplicação de técnicas para descoberta de conhecimento, classificando o desempenho de pacientes diagnosticados com hemiparesia crônica. Os pacientes foram inseridos em um ambiente de reabilitação robótica, fazendo uso do InMotion ARM, um dispositivo robótico para reabilitação de membros superiores e coleta dos dados de desempenho. Foi aplicado sobre os dados um roteiro para descoberta de conhecimento em bases de dados, desempenhando pré-processamento, transformação (extração de características) e então a mineração de dados a partir de algoritmos de aprendizado de máquina. A estratégia do presente trabalho culminou em uma classificação de padrões com a capacidade de distinguir lados hemiparéticos sob uma precisão de 94%, havendo oito atributos alimentando a entrada do mecanismo obtido. Interpretando esta coleção de atributos, foi observado que dados de força são mais significativos, os quais abrangem metade da composição de uma amostra.

Patterns in complex systems modeling

The design, development, and use of complex systems models raises a unique class of challenges and potential pitfalls, many of which are commonly recurring problems. Over time, researchers gain experience in this form of modeling, choosing algorithms, techniques, and frameworks that improve the quality, confidence level, and speed of development of their models. This increasing collective experience of complex systems modellers is a resource that should be captured. Fields such as software engineering and architecture have benefited from the development of generic solutions to recurring problems, called patterns. Using pattern development techniques from these fields, insights from communities such as learning and information processing, data mining, bioinformatics, and agent-based modeling can be identified and captured. Collections of such 'pattern languages' would allow knowledge gained through experience to be readily accessible to less-experienced practitioners and to other domains. This paper proposes a methodology for capturing the wisdom of computational modelers by introducing example visualization patterns, and a pattern classification system for analyzing the relationship between micro and macro behaviour in complex systems models. We anticipate that a new field of complex systems patterns will provide an invaluable resource for both practicing and future generations of modelers.

ESTUDO COMPARATIVO ENTRE A ANÁLISE FACIAL SUBJETIVA E ANÁLISE CEFALOMÉTRICA DE TECIDOS MOLES NO DIAGNÓSTICO ORTODÔNTICO

O propósito deste estudo foi avaliar a concordância entre a Análise facial subjetiva, proposta por CAPELOZZA FILHO e a Análise Cefalométrica de Tecidos Moles, de ARNETT; McLAUGHLIN. Fotografias de frente e de perfil e telerradiografias em norma lateral padronizadas, de 50 indivíduos, com média de idade de 24 anos e 1 mês foram utilizadas para a avaliação. Verificou-se também nos indivíduos classificados como Padrão I, a correspondência dos valores médios e dos desvios-padrão das medidas obtidas, com os valores normativos da Análise Cefalométrica dos Tecidos Moles para os indivíd uos com harmonia facial. Constatou-se em indivíduos do Padrão I que os lábios sempre se encontram à frente da Linha Vertical Verdadeira, e que apesar de grandes variações do ponto pogônio, ainda é mantido o equilíbrio facial. Os resultados demonstraram que a Análise facial subjetiva eficiente na classificação do padrão facial.

Implementação de uma arquitetura fuzzy neural em hardware com treinamento online

Computational Intelligence Methods have been expanding to industrial applications motivated by their ability to solve problems in engineering. Therefore, the embedded systems follow the same idea of using computational intelligence tools embedded on machines. There are several works in the area of embedded systems and intelligent systems. However, there are a few papers that have joined both areas. The aim of this study was to implement an adaptive fuzzy neural hardware with online training embedded on Field Programmable Gate Array – FPGA. The system adaptation can occur during the execution of a given application, aiming online performance improvement. The proposed system architecture is modular, allowing different configurations of fuzzy neural network topologies with online training. The proposed system was applied to: mathematical function interpolation, pattern classification and selfcompensation of industrial sensors. The proposed system achieves satisfactory performance in both tasks. The experiments results shows the advantages and disadvantages of online training in hardware when performed in parallel and sequentially ways. The sequentially training method provides economy in FPGA area, however, increases the complexity of architecture actions. The parallel training method achieves high performance and reduced processing time, the pipeline technique is used to increase the proposed architecture performance. The study development was based on available tools for FPGA circuits.

Novas abordagens para configurações automáticas dos parâmetros de controle em comitês de classificadores

Significant advances have emerged in research related to the topic of Classifier Committees. The models that receive the most attention in the literature are those of the static nature, also known as ensembles. The algorithms that are part of this class, we highlight the methods that using techniques of resampling of the training data: Bagging, Boosting and Multiboosting. The choice of the architecture and base components to be recruited is not a trivial task and has motivated new proposals in an attempt to build such models automatically, and many of them are based on optimization methods. Many of these contributions have not shown satisfactory results when applied to more complex problems with different nature. In contrast, the thesis presented here, proposes three new hybrid approaches for automatic construction for ensembles: Increment of Diversity, Adaptive-fitness Function and Meta-learning for the development of systems for automatic configuration of parameters for models of ensemble. In the first one approach, we propose a solution that combines different diversity techniques in a single conceptual framework, in attempt to achieve higher levels of diversity in ensembles, and with it, the better the performance of such systems. In the second one approach, using a genetic algorithm for automatic design of ensembles. The contribution is to combine the techniques of filter and wrapper adaptively to evolve a better distribution of the feature space to be presented for the components of ensemble. Finally, the last one approach, which proposes new techniques for recommendation of architecture and based components on ensemble, by techniques of traditional meta-learning and multi-label meta-learning. In general, the results are encouraging and corroborate with the thesis that hybrid tools are a powerful solution in building effective ensembles for pattern classification problems.

Decoding Spontaneous Emotional States in the Human Brain.

Pattern classification of human brain activity provides unique insight into the neural underpinnings of diverse mental states. These multivariate tools have recently been used within the field of affective neuroscience to classify distributed patterns of brain activation evoked during emotion induction procedures. Here we assess whether neural models developed to discriminate among distinct emotion categories exhibit predictive validity in the absence of exteroceptive emotional stimulation. In two experiments, we show that spontaneous fluctuations in human resting-state brain activity can be decoded into categories of experience delineating unique emotional states that exhibit spatiotemporal coherence, covary with individual differences in mood and personality traits, and predict on-line, self-reported feelings. These findings validate objective, brain-based models of emotion and show how emotional states dynamically emerge from the activity of separable neural systems.

Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder

Improved clinical care for Bipolar Disorder (BD) relies on the identification of diagnostic markers that can reliably detect disease-related signals in clinically heterogeneous populations. At the very least, diagnostic markers should be able to differentiate patients with BD from healthy individuals and from individuals at familial risk for BD who either remain well or develop other psychopathology, most commonly Major Depressive Disorder (MDD). These issues are particularly pertinent to the development of translational applications of neuroimaging as they represent challenges for which clinical observation alone is insufficient. We therefore applied pattern classification to task-based functional magnetic resonance imaging (fMRI) data of the n-back working memory task, to test their predictive value in differentiating patients with BD (n=30) from healthy individuals (n=30) and from patients' relatives who were either diagnosed with MDD (n=30) or were free of any personal lifetime history of psychopathology (n=30). Diagnostic stability in these groups was confirmed with 4-year prospective follow-up. Task-based activation patterns from the fMRI data were analyzed with Gaussian Process Classifiers (GPC), a machine learning approach to detecting multivariate patterns in neuroimaging datasets. Consistent significant classification results were only obtained using data from the 3-back versus 0-back contrast. Using contrast, patients with BD were correctly classified compared to unrelated healthy individuals with an accuracy of 83.5%, sensitivity of 84.6% and specificity of 92.3%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their relatives with MDD, were respectively 73.1%, 53.9% and 94.5%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their healthy relatives were respectively 81.8%, 72.7% and 90.9%. We show that significant individual classification can be achieved using whole brain pattern analysis of task-based working memory fMRI data. The high accuracy and specificity achieved by all three classifiers suggest that multivariate pattern recognition analyses can aid clinicians in the clinical care of BD in situations of true clinical uncertainty regarding the diagnosis and prognosis.