165 research outputs found
Reaction to New Security Threat Class
Each new identified security threat class triggers new research and
development efforts by the scientific and professional communities. In this
study, we investigate the rate at which the scientific and professional
communities react to new identified threat classes as it is reflected in the
number of patents, scientific articles and professional publications over a
long period of time. The following threat classes were studied: Phishing; SQL
Injection; BotNet; Distributed Denial of Service; and Advanced Persistent
Threat. Our findings suggest that in most cases it takes a year for the
scientific community and more than two years for industry to react to a new
threat class with patents. Since new products follow patents, it is reasonable
to expect that there will be a window of approximately two to three years in
which no effective product is available to cope with the new threat class
BagStacking: An Integrated Ensemble Learning Approach for Freezing of Gait Detection in Parkinson's Disease
This paper introduces BagStacking, a novel ensemble learning method designed
to enhance the detection of Freezing of Gait (FOG) in Parkinson's Disease (PD)
by using a lower-back sensor to track acceleration. Building on the principles
of bagging and stacking, BagStacking aims to achieve the variance reduction
benefit of bagging's bootstrap sampling while also learning sophisticated
blending through stacking. The method involves training a set of base models on
bootstrap samples from the training data, followed by a meta-learner trained on
the base model outputs and true labels to find an optimal aggregation scheme.
The experimental evaluation demonstrates significant improvements over other
state-of-the-art machine learning methods on the validation set. Specifically,
BagStacking achieved a MAP score of 0.306, outperforming LightGBM (0.234) and
classic Stacking (0.286). Additionally, the run-time of BagStacking was
measured at 3828 seconds, illustrating an efficient approach compared to
Regular Stacking's 8350 seconds. BagStacking presents a promising direction for
handling the inherent variability in FOG detection data, offering a robust and
scalable solution to improve patient care in PD
Linking Motif Sequences with Tale Types by Machine Learning
Abstract units of narrative content called motifs constitute sequences, also known as tale types. However whereas the dependency of tale types on the constituent motifs is clear, the strength of their bond has not been measured this far. Based on the observation that differences between such motif sequences are reminiscent of nucleotide and chromosome mutations in genetics, i.e., constitute "narrative DNA", we used sequence mining methods from bioinformatics to learn more about the nature of tale types as a corpus. 94% of the Aarne-Thompson-Uther catalogue (2249 tale types in 7050 variants) was listed as individual motif strings based on the Thompson Motif Index, and scanned for similar subsequences. Next, using machine learning algorithms, we built and evaluated a classifier which predicts the tale type of a new motif sequence. Our findings indicate that, due to the size of the available samples, the classification model was best able to predict magic tales, novelles and jokes
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