Investigating the use of an ensemble of evolutionary algorithms for letter identification in tremulous medieval handwriting

Ensemble classifiers are known for performing good generalization from simpler and less accurate classifiers. Ensembles have the ability to use the variety in classification patterns of the smaller classifiers in order to make better predictions. However, to create an ensemble it is necessary to determine how the component classifiers should be combined to generate the final predictions. One way to do this is to search different combinations of classifiers with evolutionary algorithms, which are largely employed when the objective is to find a structure that serves for some purpose. In this work, an investigation is carried about the use of ensembles obtained via evolutionary algorithm for identifying individual letters in tremulous medieval writing and to differentiate between scribes. The aim of this research is to use this process as the first step towards classifying the tremor type with more accuracy. The ensembles are obtained through evolutionary search of trees that aggregate the output of base classifiers, which are neural networks trained prior to the ensemble search. The misclassification patterns of the base classifiers are analysed in order to determine how much better an ensemble of those classifiers can be than its components. The best ensembles have their misclassification patterns compared to those of their component classifiers. The results obtained suggest interesting methods for letter (up to 96% accuracy) and user classification (up to 88% accuracy) in an offline scenario

Difficulty Classification of Mountainbike Downhill Trails utilizing Deep Neural Networks

The difficulty of mountainbike downhill trails is a subjective perception. However, sports-associations and mountainbike park operators attempt to group trails into different levels of difficulty with scales like the Singletrail-Skala (S0-S5) or colored scales (blue, red, black, ...) as proposed by The International Mountain Bicycling Association. Inconsistencies in difficulty grading occur due to the various scales, different people grading the trails, differences in topography, and more. We propose an end-to-end deep learning approach to classify trails into three difficulties easy, medium, and hard by using sensor data. With mbientlab Meta Motion r0.2 sensor units, we record accelerometer- and gyroscope data of one rider on multiple trail segments. A 2D convolutional neural network is trained with a stacked and concatenated representation of the aforementioned data as its input. We run experiments with five different sample- and five different kernel sizes and achieve a maximum Sparse Categorical Accuracy of 0.9097. To the best of our knowledge, this is the first work targeting computational difficulty classification of mountainbike downhill trails.Comment: 11 pages, 5 figure

Titration of chaos with added noise

Deterministic chaos has been implicated in numerous natural and man-made complex phenomena ranging from quantum to astronomical scales and in disciplines as diverse as meteorology, physiology, ecology, and economics. However, the lack of a definitive test of chaos vs. random noise in experimental time series has led to considerable controversy in many fields. Here we propose a numerical titration procedure as a simple “litmus test” for highly sensitive, specific, and robust detection of chaos in short noisy data without the need for intensive surrogate data testing. We show that the controlled addition of white or colored noise to a signal with a preexisting noise floor results in a titration index that: (i) faithfully tracks the onset of deterministic chaos in all standard bifurcation routes to chaos; and (ii) gives a relative measure of chaos intensity. Such reliable detection and quantification of chaos under severe conditions of relatively low signal-to-noise ratio is of great interest, as it may open potential practical ways of identifying, forecasting, and controlling complex behaviors in a wide variety of physical, biomedical, and socioeconomic systems