A Submodular Optimization Framework for Imbalanced Text Classification with Data Augmentation

In the domain of text classification, imbalanced datasets are a common occurrence. The skewed distribution of the labels of these datasets poses a great challenge to the performance of text classifiers. One popular way to mitigate this challenge is to augment underwhelmingly represented labels with synthesized items. The synthesized items are generated by data augmentation methods that can typically generate an unbounded number of items. To select the synthesized items that maximize the performance of text classifiers, we introduce a novel method that selects items that jointly maximize the likelihood of the items belonging to their respective labels and the diversity of the selected items. Our proposed method formulates the joint maximization as a monotone submodular objective function, whose solution can be approximated by a tractable and efficient greedy algorithm. We evaluated our method on multiple real-world datasets with different data augmentation techniques and text classifiers, and compared results with several baselines. The experimental results demonstrate the effectiveness and efficiency of our method

Inference of Traffic Regulations at Intersections Based on Trajectory Data

Zahlreiche moderne Lösungen im Bereich Autonomes Fahren greifen auf hochpräzises Kartenmaterial zurück. Neben anderen Informationen muss das Kartenmaterial solche über Verkehrsregeln enthalten. In dieser Arbeit wird eine Offline-Lösung für die Inferenz von Verkehrsregeln an Deutschen Kreuzungen entwickelt. Mithilfe dieser Lösung werden für jeden Fahrstreifen einer Kreuzung Klassifikationsentscheidungen für jede mögliche Zielrichtung, welche von diesem Fahrstreifen aus erreichbar ist, getroffen. Verkehrsregeln werden mithilfe von Hidden-Markov-Models repräsentiert und, basierend auf errechneten Likelihood-Werten, bestimmt. Die Modelle werden mithilfe künstlich erzeugter Trajektorien von Kreuzungsüberquerungen parametrisiert und evaluiert. Unter realen Umständen würden solche Daten opportunistisch und sensorgestützt von einer Fahrzeugflotte über einen längeren Zeitraum hinweg gesammelt werden. In einer Reihe von Experimenten wird eine geeignete Trajektorienrepräsentation festgelegt und der Klassifikationsansatz getestet und verfeinert. Die Klassifikationsperformanz des Ansatzes wird mithilfe eines Kreuzvalidierungsverfahren bestimmt. Mittlere F$_1$-Scores zur Quantifizierung der besten Ergebnisse unter den erzielten Testergebnissen variieren zwischen 0.809 und 0.832. Bezüglich der Verkehrsregeln, welche mithilfe von Vorfahrts- und Stoppschildern, sowie Lichtsignalanlagen kommuniziert werden, werden hohe Klassifikationsleistungen erreicht. Allerdings bestehen Schwierigkeiten bei der Klassifikation im Zusammenhang mit den Verkehrsregeln Vorfahrt achten und Rechts vor Links. Da die initial erzielten Ergebnisse vielversprechend sind, wird empfohlen diesen Ansatz in zukünftigen Arbeiten weiterzuentwickeln und zu verbessern

Retinal image quality assessment using deep convolutional neural networks

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Informática Médica)Diabetic Retinopathy (DR) and diabetic macular edema (DME) are the damages caused to the retina and are complications that can affect the diabetic population. Diabetic retinopathy (DR), is the most common disease due to the presence of exudates and has three levels of severity, such as mild, moderate and severe, depending on the exudates distribution in the retina. For screening of diabetic retinopathy or a population-based clinical study, a large number of digital fundus images are captured and to be possible to recognize the signs of DR and DME, it is necessary that the images have quality, because low-quality images may force the patient to return for a second examination, wasting time and possibly delaying treatment. These images are evaluated by trained human experts, which can be a time-consuming and expensive task due to the number of images that need to be examined. Therefore, this is a field that would be hugely benefited with the development of an automated eye fundus quality assessment and analysis systems. It can potentially facilitate health care in remote regions and in developing countries where reading skills are scarce. Deep Learning is a kind of Machine Learning method that involves learning multi-level representations that begin with raw data entry and gradually moves to more abstract levels through non-linear transformations. With enough training data and sufficiently deep architectures, neural networks, such as Convolutional Neural Networks (CNN), can learn very complex functions and discover complex structures in the data. Thus, Deep Learning emerges as a powerful tool for medical image analysis and evaluation of retinal image quality using computer-aided diagnosis. Therefore, the aim of this study is to automatically assess all the three quality parameters alone (focus, illumination and color), and then an overall quality of fundus images assessment, classifying the images into the classes “accept” or “reject with a Deep Learning approach using convolutional neural networks (CNN). For the overall classification, the following results were obtained: test accuracy=97.89%, SN=97.9%, AUC=0.98 and 1-score=97.91%.A retinopatia diabética (RD) e o edema macular diabético (EMD) são patologias da retina e são uma complicação que pode afetar a população diabética. A retinopatia diabética é a doença mais comum devido à presença de exsudatos e possui três níveis de gravidade, como leve, moderado e grave, dependendo da distribuição dos exsudatos na retina. Para triagem da retinopatia diabética ou estudo clínico de base populacional, um grande número de imagens digitais de fundo do olho são capturadas e para ser possível reconhecer os sinais da RD e EMD, é necessário que as imagens tenham qualidade, pois imagens de baixa qualidade podem forçar o paciente a retornar para um segundo exame, perdendo tempo e, possivelmente, retardando o tratamento. Essas imagens são avaliadas por especialistas humanos treinados, o que pode ser uma tarefa demorada e cara devido ao número de imagens que precisam de ser examinadas. Portanto, este é um campo que seria enormemente beneficiado com o desenvolvimento de sistemas automatizados de avaliação e análise da qualidade da imagem do fundo de olho. Pode potencialmente facilitar a assistência médica em regiões remotas e em países em desenvolvimento, onde as habilidades de leitura são escassas. Deep Learning é um tipo de método de Machine Learning que envolve a aprendizagem de representações em vários níveis que começam com a entrada de dados brutos e gradualmente se transformam para níveis mais abstratos através de transformações não lineares, para se obterem as previsões. Com dados de treino suficientes e arquiteturas suficientemente profundas, as redes neuronais, como as Convolutional Neural Networks (CNN), podem aprender funções muito complexas e descobrir estruturas complexas nos dados. Assim, o Deep Learning surge como uma ferramenta poderosa para analisar imagens médicas para avaliação da qualidade da retina, usando diagnóstico auxiliado por computador a partir do fundo do olho. Portanto, o objetivo deste estudo é avaliar automaticamente a qualidade geral das imagens do fundo, classificando as imagens em “aceites” ou “rejeitadas”, com base em três parâmetros principais, como o foco, a iluminação e cor com abordagem de Deep Learning usando convolutional neural networks (CNN). Para a classificação geral da qualidade das imagens, obtiveram-se os seguintes resultados: acurácia do teste = 97,89%, SN = 97,9%, AUC = 0,98 e 1-score=97.91%

Development of A Versatile Multichannel CWNIRS Instrument for Optical Brain-Computer Interface Applications

This thesis describes the design, development, and implementation of a versatile multichannel continuous-wave near-infrared spectroscopy (CWNIRS) instrument for brain-computer interface (BCI) applications. Specifically, it was of interest to assess what gains could be achieved by using a multichannel device compared to the single channel device implemented by Coyle in 2004. Moreover, the multichannel approach allows for the assessment of localisation of functional tasks in the cerebral cortex, and can identify lateralisation of haemodynamic responses to motor events. The approach taken to extend single channel to multichannel was based on a software-controlled interface. This interface allowed flexibility in the control of individual optodes including their synchronisation and modulation (AM, TDM, CDMA). Furthermore, an LED driver was developed for custom-made triple-wavelength LEDs. The system was commissioned using a series of experiments to verify the performance of individual components in the system. The system was then used to carry out a set of functional studies including motor imagery and cognitive tasks. The experimental protocols based on motor imagery and overt motor tasks were verified by comparison with fMRI. The multichannel approach identified stroke rehabilitation as a new application area for optical BCI. In addition, concentration changes in deoxyhaemoglobin were identified as being a more localised indicator of functional activity, which is important for effective BCI design. An assessment was made on the effect of the duration of the stimulus period on the haemodynamic signals. This demonstrated the possible benefits of using a shorter stimulus period to reduce the adverse affects of low blood pressure oscillations. i

Development of A Versatile Multichannel CWNIRS Instrument for Optical Brain-Computer Interface Applications

This thesis describes the design, development, and implementation of a versatile multichannel continuous-wave near-infrared spectroscopy (CWNIRS) instrument for brain-computer interface (BCI) applications. Specifically, it was of interest to assess what gains could be achieved by using a multichannel device compared to the single channel device implemented by Coyle in 2004. Moreover, the multichannel approach allows for the assessment of localisation of functional tasks in the cerebral cortex, and can identify lateralisation of haemodynamic responses to motor events. The approach taken to extend single channel to multichannel was based on a software-controlled interface. This interface allowed flexibility in the control of individual optodes including their synchronisation and modulation (AM, TDM, CDMA). Furthermore, an LED driver was developed for custom-made triple-wavelength LEDs. The system was commissioned using a series of experiments to verify the performance of individual components in the system. The system was then used to carry out a set of functional studies including motor imagery and cognitive tasks. The experimental protocols based on motor imagery and overt motor tasks were verified by comparison with fMRI. The multichannel approach identified stroke rehabilitation as a new application area for optical BCI. In addition, concentration changes in deoxyhaemoglobin were identified as being a more localised indicator of functional activity, which is important for effective BCI design. An assessment was made on the effect of the duration of the stimulus period on the haemodynamic signals. This demonstrated the possible benefits of using a shorter stimulus period to reduce the adverse affects of low blood pressure oscillations. i

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal