3 papers
cs.CR2026
Fetch Me If You Can: A Comprehensive Analysis of Fetch Metadata
Pedro Bernardo, Alex Ploner, Marco Squarcina +2
Effective server-side defenses against cross-site attacks depend on understanding the context in which browser requests are generated. However, servers often lack the information n…
cs.AI2026
Augmenting Text to Increase Translation Difficulty
William Kalikman, Šimon Sukup, Michal Tešnar +1
As state-of-the-art machine translation models saturate standard benchmarks, the field needs more challenging evaluations to distinguish between models of varying quality. We propo…
cs.LG2025
Modeling of AUV Dynamics with Limited Resources: Efficient Online Learning Using Uncertainty
Michal Tešnar, Bilal Wehbe, Matias Valdenegro-Toro
Machine learning proves effective in constructing dynamics models from data, especially for underwater vehicles. Continuous refinement of these models using incoming data streams,…