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Multimodal dual-embedding networks for malware open-set recognition
Malware open-set recognition (MOSR) is an emerging research domain that aims at jointly
classifying malware samples from known families and detecting the ones from novel …
classifying malware samples from known families and detecting the ones from novel …
Mdenet: multi-modal dual-embedding networks for malware open-set recognition
Malware open-set recognition (MOSR) aims at jointly classifying malware samples from
known families and detect the ones from novel unknown families, respectively. Existing …
known families and detect the ones from novel unknown families, respectively. Existing …
Sliding window algorithms for k-clustering problems
The sliding window model of computation captures scenarios in which data is arriving
continuously, but only the latest $ w $ elements should be used for analysis. The goal is to …
continuously, but only the latest $ w $ elements should be used for analysis. The goal is to …
Minimum coresets for maxima representation of multidimensional data
Coresets are succinct summaries of large datasets such that, for a given problem, the
solution obtained from a coreset is provably competitive with the solution obtained from the …
solution obtained from a coreset is provably competitive with the solution obtained from the …
A fully dynamic algorithm for k-regret minimizing sets
Selecting a small set of representatives from a large database is important in many
applications such as multi-criteria decision making, web search, and recommendation. The k …
applications such as multi-criteria decision making, web search, and recommendation. The k …
Adaptive k-center and diameter estimation in sliding windows
In this paper we present novel streaming algorithms for the k-center and the diameter
estimation problems for general metric spaces under the sliding window model. The key …
estimation problems for general metric spaces under the sliding window model. The key …
Reactive concept drift detection using coresets over sliding windows
M Heusinger, FM Schleif - 2020 IEEE Symposium Series on …, 2020 - ieeexplore.ieee.org
The change of underlying data is one of the biggest challenges in non-stationary
environments. While several algorithms have been proposed to detect these changes …
environments. While several algorithms have been proposed to detect these changes …
Improved weighted matching in the sliding window model
CM Alexandru, P Dvořák, C Konrad… - arxiv preprint arxiv …, 2022 - arxiv.org
We consider the Maximum-weight Matching (MWM) problem in the streaming sliding
window model of computation. In this model, the input consists of a sequence of weighted …
window model of computation. In this model, the input consists of a sequence of weighted …
Learning with high dimensional data and preprocessing in non-stationary environments
M Heusinger - 2023 - pub.uni-bielefeld.de
The internet of things generates huge amounts of multidimensional sensor readings. The
analysis of these high dimensional data is chal-lenging and not sufficiently addressed. In …
analysis of these high dimensional data is chal-lenging and not sufficiently addressed. In …
Simple and Efficient Acceleration of the Smallest Enclosing Ball for Large Data Sets in : Analysis and Comparative Results
V Skala, M Cerny, JY Saleh - International Conference on Computational …, 2022 - Springer
Finding the smallest enclosing circle of the given points in E 2 is a seemingly simple
problem. However, already proposed algorithms have high memory requirements or require …
problem. However, already proposed algorithms have high memory requirements or require …