41 research outputs found

    Study to determine potential flight applications and human factors design guidelines for voice recognition and synthesis systems

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    A study was conducted to determine potential commercial aircraft flight deck applications and implementation guidelines for voice recognition and synthesis. At first, a survey of voice recognition and synthesis technology was undertaken to develop a working knowledge base. Then, numerous potential aircraft and simulator flight deck voice applications were identified and each proposed application was rated on a number of criteria in order to achieve an overall payoff rating. The potential voice recognition applications fell into five general categories: programming, interrogation, data entry, switch and mode selection, and continuous/time-critical action control. The ratings of the first three categories showed the most promise of being beneficial to flight deck operations. Possible applications of voice synthesis systems were categorized as automatic or pilot selectable and many were rated as being potentially beneficial. In addition, voice system implementation guidelines and pertinent performance criteria are proposed. Finally, the findings of this study are compared with those made in a recent NASA study of a 1995 transport concept

    Proceedings, MSVSCC 2013

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    Proceedings of the 7th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 11, 2013 at VMASC in Suffolk, Virginia

    Discovering lesser known molecular players and mechanistic patterns in Alzheimer's disease using an integrative disease modelling approach

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    Convergence of exponentially advancing technologies is driving medical research with life changing discoveries. On the contrary, repeated failures of high-profile drugs to battle Alzheimer's disease (AD) has made it one of the least successful therapeutic area. This failure pattern has provoked researchers to grapple with their beliefs about Alzheimer's aetiology. Thus, growing realisation that Amyloid-β and tau are not 'the' but rather 'one of the' factors necessitates the reassessment of pre-existing data to add new perspectives. To enable a holistic view of the disease, integrative modelling approaches are emerging as a powerful technique. Combining data at different scales and modes could considerably increase the predictive power of the integrative model by filling biological knowledge gaps. However, the reliability of the derived hypotheses largely depends on the completeness, quality, consistency, and context-specificity of the data. Thus, there is a need for agile methods and approaches that efficiently interrogate and utilise existing public data. This thesis presents the development of novel approaches and methods that address intrinsic issues of data integration and analysis in AD research. It aims to prioritise lesser-known AD candidates using highly curated and precise knowledge derived from integrated data. Here much of the emphasis is put on quality, reliability, and context-specificity. This thesis work showcases the benefit of integrating well-curated and disease-specific heterogeneous data in a semantic web-based framework for mining actionable knowledge. Furthermore, it introduces to the challenges encountered while harvesting information from literature and transcriptomic resources. State-of-the-art text-mining methodology is developed to extract miRNAs and its regulatory role in diseases and genes from the biomedical literature. To enable meta-analysis of biologically related transcriptomic data, a highly-curated metadata database has been developed, which explicates annotations specific to human and animal models. Finally, to corroborate common mechanistic patterns — embedded with novel candidates — across large-scale AD transcriptomic data, a new approach to generate gene regulatory networks has been developed. The work presented here has demonstrated its capability in identifying testable mechanistic hypotheses containing previously unknown or emerging knowledge from public data in two major publicly funded projects for Alzheimer's, Parkinson's and Epilepsy diseases

    An uncertainty prediction approach for active learning - application to earth observation

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    Mapping land cover and land usage dynamics are crucial in remote sensing since farmers are encouraged to either intensify or extend crop use due to the ongoing rise in the world’s population. A major issue in this area is interpreting and classifying a scene captured in high-resolution satellite imagery. Several methods have been put forth, including neural networks which generate data-dependent models (i.e. model is biased toward data) and static rule-based approaches with thresholds which are limited in terms of diversity(i.e. model lacks diversity in terms of rules). However, the problem of having a machine learning model that, given a large amount of training data, can classify multiple classes over different geographic Sentinel-2 imagery that out scales existing approaches remains open. On the other hand, supervised machine learning has evolved into an essential part of many areas due to the increasing number of labeled datasets. Examples include creating classifiers for applications that recognize images and voices, anticipate traffic, propose products, act as a virtual personal assistant and detect online fraud, among many more. Since these classifiers are highly dependent from the training datasets, without human interaction or accurate labels, the performance of these generated classifiers with unseen observations is uncertain. Thus, researchers attempted to evaluate a number of independent models using a statistical distance. However, the problem of, given a train-test split and classifiers modeled over the train set, identifying a prediction error using the relation between train and test sets remains open. Moreover, while some training data is essential for supervised machine learning, what happens if there is insufficient labeled data? After all, assigning labels to unlabeled datasets is a time-consuming process that may need significant expert human involvement. When there aren’t enough expert manual labels accessible for the vast amount of openly available data, active learning becomes crucial. However, given a large amount of training and unlabeled datasets, having an active learning model that can reduce the training cost of the classifier and at the same time assist in labeling new data points remains an open problem. From the experimental approaches and findings, the main research contributions, which concentrate on the issue of optical satellite image scene classification include: building labeled Sentinel-2 datasets with surface reflectance values; proposal of machine learning models for pixel-based image scene classification; proposal of a statistical distance based Evidence Function Model (EFM) to detect ML models misclassification; and proposal of a generalised sampling approach for active learning that, together with the EFM enables a way of determining the most informative examples. Firstly, using a manually annotated Sentinel-2 dataset, Machine Learning (ML) models for scene classification were developed and their performance was compared to Sen2Cor the reference package from the European Space Agency – a micro-F1 value of 84% was attained by the ML model, which is a significant improvement over the corresponding Sen2Cor performance of 59%. Secondly, to quantify the misclassification of the ML models, the Mahalanobis distance-based EFM was devised. This model achieved, for the labeled Sentinel-2 dataset, a micro-F1 of 67.89% for misclassification detection. Lastly, EFM was engineered as a sampling strategy for active learning leading to an approach that attains the same level of accuracy with only 0.02% of the total training samples when compared to a classifier trained with the full training set. With the help of the above-mentioned research contributions, we were able to provide an open-source Sentinel-2 image scene classification package which consists of ready-touse Python scripts and a ML model that classifies Sentinel-2 L1C images generating a 20m-resolution RGB image with the six studied classes (Cloud, Cirrus, Shadow, Snow, Water, and Other) giving academics a straightforward method for rapidly and effectively classifying Sentinel-2 scene images. Additionally, an active learning approach that uses, as sampling strategy, the observed prediction uncertainty given by EFM, will allow labeling only the most informative points to be used as input to build classifiers; Sumário: Uma Abordagem de Previsão de Incerteza para Aprendizagem Ativa – Aplicação à Observação da Terra O mapeamento da cobertura do solo e a dinâmica da utilização do solo são cruciais na deteção remota uma vez que os agricultores são incentivados a intensificar ou estender as culturas devido ao aumento contínuo da população mundial. Uma questão importante nesta área é interpretar e classificar cenas capturadas em imagens de satélite de alta resolução. Várias aproximações têm sido propostas incluindo a utilização de redes neuronais que produzem modelos dependentes dos dados (ou seja, o modelo é tendencioso em relação aos dados) e aproximações baseadas em regras que apresentam restrições de diversidade (ou seja, o modelo carece de diversidade em termos de regras). No entanto, a criação de um modelo de aprendizagem automática que, dada uma uma grande quantidade de dados de treino, é capaz de classificar, com desempenho superior, as imagens do Sentinel-2 em diferentes áreas geográficas permanece um problema em aberto. Por outro lado, têm sido utilizadas técnicas de aprendizagem supervisionada na resolução de problemas nas mais diversas áreas de devido à proliferação de conjuntos de dados etiquetados. Exemplos disto incluem classificadores para aplicações que reconhecem imagem e voz, antecipam tráfego, propõem produtos, atuam como assistentes pessoais virtuais e detetam fraudes online, entre muitos outros. Uma vez que estes classificadores são fortemente dependente do conjunto de dados de treino, sem interação humana ou etiquetas precisas, o seu desempenho sobre novos dados é incerta. Neste sentido existem propostas para avaliar modelos independentes usando uma distância estatística. No entanto, o problema de, dada uma divisão de treino-teste e um classificador, identificar o erro de previsão usando a relação entre aqueles conjuntos, permanece aberto. Mais ainda, embora alguns dados de treino sejam essenciais para a aprendizagem supervisionada, o que acontece quando a quantidade de dados etiquetados é insuficiente? Afinal, atribuir etiquetas é um processo demorado e que exige perícia, o que se traduz num envolvimento humano significativo. Quando a quantidade de dados etiquetados manualmente por peritos é insuficiente a aprendizagem ativa torna-se crucial. No entanto, dada uma grande quantidade dados de treino não etiquetados, ter um modelo de aprendizagem ativa que reduz o custo de treino do classificador e, ao mesmo tempo, auxilia a etiquetagem de novas observações permanece um problema em aberto. A partir das abordagens e estudos experimentais, as principais contribuições deste trabalho, que se concentra na classificação de cenas de imagens de satélite óptico incluem: criação de conjuntos de dados Sentinel-2 etiquetados, com valores de refletância de superfície; proposta de modelos de aprendizagem automática baseados em pixels para classificação de cenas de imagens de satétite; proposta de um Modelo de Função de Evidência (EFM) baseado numa distância estatística para detetar erros de classificação de modelos de aprendizagem; e proposta de uma abordagem de amostragem generalizada para aprendizagem ativa que, em conjunto com o EFM, possibilita uma forma de determinar os exemplos mais informativos. Em primeiro lugar, usando um conjunto de dados Sentinel-2 etiquetado manualmente, foram desenvolvidos modelos de Aprendizagem Automática (AA) para classificação de cenas e seu desempenho foi comparado com o do Sen2Cor – o produto de referência da Agência Espacial Europeia – tendo sido alcançado um valor de micro-F1 de 84% pelo classificador, o que representa uma melhoria significativa em relação ao desempenho Sen2Cor correspondente, de 59%. Em segundo lugar, para quantificar o erro de classificação dos modelos de AA, foi concebido o Modelo de Função de Evidência baseado na distância de Mahalanobis. Este modelo conseguiu, para o conjunto de dados etiquetado do Sentinel-2 um micro-F1 de 67,89% na deteção de classificação incorreta. Por fim, o EFM foi utilizado como uma estratégia de amostragem para a aprendizagem ativa, uma abordagem que permitiu atingir o mesmo nível de desempenho com apenas 0,02% do total de exemplos de treino quando comparado com um classificador treinado com o conjunto de treino completo. Com a ajuda das contribuições acima mencionadas, foi possível desenvolver um pacote de código aberto para classificação de cenas de imagens Sentinel-2 que, utilizando num conjunto de scripts Python, um modelo de classificação, e uma imagem Sentinel-2 L1C, gera a imagem RGB correspondente (com resolução de 20m) com as seis classes estudadas (Cloud, Cirrus, Shadow, Snow, Water e Other), disponibilizando à academia um método direto para a classificação de cenas de imagens do Sentinel-2 rápida e eficaz. Além disso, a abordagem de aprendizagem ativa que usa, como estratégia de amostragem, a deteção de classificacão incorreta dada pelo EFM, permite etiquetar apenas os pontos mais informativos a serem usados como entrada na construção de classificadores

    Towards Optimizing Quality Assurance Outcomes of Knowledge-Based Radiation Therapy Treatment Plans Using Machine Learning

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    Knowledge-based planning (KBP) techniques have been shown to provide improvements in plan quality, consistency, and efficiency for advanced radiation therapies such as volumetric modulated arc therapy (VMAT). While the potential clinical benefits of KBP methods are generally well known, comparatively less is understood regarding the impact of using these systems on resulting plan complexity and pre-treatment quality assurance (QA) measurements, especially for in-house KBP systems. Therefore, the overarching purpose of this work was to assess QA implications with using an in-house KBP system and explore data-driven methods for mitigating increased plan complexity and QA error rates without compromising dosimetric plan quality. Specifically, this study evaluated differences in dose, complexity, and QA outcomes between reference clinical plans and plans designed with a previously established in-house KBP system. Further, a machine learning model – trained and tested using a database of 500 previous VMAT treatment plans and QA measurements – was developed to predict VMAT QA measurements based on selected mechanical features of the plan. This model was deployed as a feedback mechanism within a heuristic optimization algorithm designed to modify plan parameters (identified by the machine learning model as important for accurately predicting QA outcomes) towards improving the predicted delivery accuracy of the plan. While KBP plans achieved average reductions of 6.4 Gy (p \u3c 0.001) and 8.2 Gy (p \u3c 0.001) in mean bladder and rectum dose compared to reference clinical plans across thirty-one prostate patients, significant (p \u3c 0.05) increases in both complexity and QA measurement errors were observed. A support vector machine (SVM) was developed – using a database of 500 previous VMAT plans – to predict gamma passing rates (GPRs; 3%/3mm percent dose-difference/distance-to-agreement with local normalization) based on selected complexity features. A QA-based optimization algorithm was devised by utilizing the SVM model to iteratively modify mechanical treatment features most commonly associated with suboptimal GPRs. The feasibility was evaluated on 13 prostate VMAT plans designed with an in-house KBP method. Using a maximum random leaf gap displacement setting of 3 mm, predicted GPRs increased by an average of 1.14 ± 1.25% (p = 0.006) with minimal differences in dose and radiobiological metrics

    Pattern recognition and machine learning for magnetic resonance images with kernel methods

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    The aim of this thesis is to apply a particular category of machine learning and pattern recognition algorithms, namely the kernel methods, to both functional and anatomical magnetic resonance images (MRI). This work specifically focused on supervised learning methods. Both methodological and practical aspects are described in this thesis. Kernel methods have the computational advantage for high dimensional data, therefore they are idea for imaging data. The procedures can be broadly divided into two components: the construction of the kernels and the actual kernel algorithms themselves. Pre-processed functional or anatomical images can be computed into a linear kernel or a non-linear kernel. We introduce both kernel regression and kernel classification algorithms in two main categories: probabilistic methods and non-probabilistic methods. For practical applications, kernel classification methods were applied to decode the cognitive or sensory states of the subject from the fMRI signal and were also applied to discriminate patients with neurological diseases from normal people using anatomical MRI. Kernel regression methods were used to predict the regressors in the design of fMRI experiments, and clinical ratings from the anatomical scans

    Cyber Security of Critical Infrastructures

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    Critical infrastructures are vital assets for public safety, economic welfare, and the national security of countries. The vulnerabilities of critical infrastructures have increased with the widespread use of information technologies. As Critical National Infrastructures are becoming more vulnerable to cyber-attacks, their protection becomes a significant issue for organizations as well as nations. The risks to continued operations, from failing to upgrade aging infrastructure or not meeting mandated regulatory regimes, are considered highly significant, given the demonstrable impact of such circumstances. Due to the rapid increase of sophisticated cyber threats targeting critical infrastructures with significant destructive effects, the cybersecurity of critical infrastructures has become an agenda item for academics, practitioners, and policy makers. A holistic view which covers technical, policy, human, and behavioural aspects is essential to handle cyber security of critical infrastructures effectively. Moreover, the ability to attribute crimes to criminals is a vital element of avoiding impunity in cyberspace. In this book, both research and practical aspects of cyber security considerations in critical infrastructures are presented. Aligned with the interdisciplinary nature of cyber security, authors from academia, government, and industry have contributed 13 chapters. The issues that are discussed and analysed include cybersecurity training, maturity assessment frameworks, malware analysis techniques, ransomware attacks, security solutions for industrial control systems, and privacy preservation methods

    From Manual to Automated Design of Biomedical Semantic Segmentation Methods

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    Digital imaging plays an increasingly important role in clinical practice. With the number of images that are routinely acquired on the rise, the number of experts devoted to analyzing them is by far not increasing as rapidly. This alarming disparity calls for automated image analysis methods to ease the burden on the experts and prevent a degradation of the quality of care. Semantic segmentation plays a central role in extracting clinically relevant information from images, either all by themselves or as part of more elaborate pipelines, and constitutes one of the most active fields of research in medical image analysis. Thereby, the diversity of datasets is mirrored by an equally diverse number of segmentation methods, each being optimized for the datasets they are addressing. The resulting diversity of methods does not come without downsides: The specialized nature of these segmentation methods causes a dataset dependency which makes them unable to be transferred to other segmentation problems. Not only does this result in issues with out-of-the-box applicability, but it also adversely affects future method development: Improvements over baselines that are demonstrated on one dataset rarely transfer to another, testifying a lack of reproducibility and causing a frustrating literature landscape in which it is difficult to discern veritable and long lasting methodological advances from noise. We study three different segmentation tasks in depth with the goal of understanding what makes a good segmentation model and which of the recently proposed methods are truly required to obtain competitive segmentation performance. To this end, we design state of the art segmentation models for brain tumor segmentation, cardiac substructure segmentation and kidney and kidney tumor segmentation. Each of our methods is evaluated in the context of international competitions, ensuring objective performance comparison with other methods. We obtained the third place in BraTS 2017, the second place in BraTS 2018, the first place in ACDC and the first place in the highly competitive KiTS challenge. Our analysis of the four segmentation methods reveals that competitive segmentation performance for all of these tasks can be achieved with a standard, but well-tuned U-Net architecture, which is surprising given the recent focus in the literature on finding better network architectures. Furthermore, we identify certain similarities between our segmentation pipelines and notice that their dissimilarities merely reflect well-structured adaptations in response to certain dataset properties. This leads to the hypothesis that we can identify a direct relation between the properties of a dataset and the design choices that lead to a good segmentation model for it. Based on this hypothesis we develop nnU-Net, the first method that breaks the dataset dependency of traditional segmentation methods. Traditional segmentation methods must be developed by experts, going through an iterative trial-and-error process until they have identified a good segmentation pipeline for a given dataset. This process ultimately results in a fixed pipeline configuration which may be incompatible with other datasets, requiring extensive re-optimization. In contrast, nnU-Net makes use of a generalizing method template that is dynamically and automatically adapted to each dataset it is applied to. This is achieved by condensing domain knowledge about the design of segmentation methods into inductive biases. Specifically, we identify certain pipeline hyperparameters that do not need to be adapted and for which a good default value can be set for all datasets (called blueprint parameters). They are complemented with a comprehensible set of heuristic rules, which explicitly encode how the segmentation pipeline and the network architecture that is used along with it must be adapted for each dataset (inferred parameters). Finally, a limited number of design choices is determined through empirical evaluation (empirical parameters). Following the analysis of our previously designed specialized pipelines, the basic network architecture type used is the standard U-Net, coining the name of our method: nnU-Net (”No New Net”). We apply nnU-Net to 19 diverse datasets originating from segmentation competitions in the biomedical domain. Despite being applied without manual intervention, nnU-Net sets a new state of the art in 29 out of the 49 different segmentation tasks encountered in these datasets. This is remarkable considering that nnU-Net competed against specialized manually tuned algorithms on each of them. nnU-Net is the first out-of-the-box tool that makes state of the art semantic segmentation methods accessible to non-experts. As a framework, it catalyzes future method development: new design concepts can be implemented into nnU-Net and leverage its dynamic nature to be evaluated across a wide variety of datasets without the need for manual re-tuning. In conclusion, the thesis presented here exposed critical weaknesses in the current way of segmentation method development. The dataset dependency of segmentation methods impedes scientific progress by confining researchers to a subset of datasets available in the domain, causing noisy evaluation and in turn a literature landscape in which results are difficult to reproduce and true methodological advances are difficult to discern. Additionally, non-experts were barred access to state of the art segmentation for their custom datasets because method development is a time consuming trial-and-error process that needs expertise to be done correctly. We propose to address this situation with nnU-Net, a segmentation method that automatically and dynamically adapts itself to arbitrary datasets, not only making out-of-the-box segmentation available for everyone but also enabling more robust decision making in the development of segmentation methods by enabling easy and convenient evaluation across multiple datasets

    The Gene Ontology Handbook

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    bioinformatics; biotechnolog

    Information resources management, 1984-1989: A bibliography with indexes

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    This bibliography contains 768 annotated references to reports and journal articles entered into the NASA scientific and technical information database 1984 to 1989
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