12 citations · 12 across the 4 of their papers we have counts for
5 papers
Beyond Measurement Metrics: A Human-Centered Framework for Semantic Validation of Network Traffic Classification
Igor Cherepanov, David Sessler, Alex Ulmer +2
Machine learning (ML) has become the dominant approach for network traffic classification, achieving very high predictive performance. However, a model is only valuable if it learn…
Dynamic Tree Colors: Adaptive Discriminable Hierarchies with Minimum Instability
Tobias Mertz, Steven Lamarr Reynolds, Jörn Kohlhammer
Hierarchical color maps can support users in the analysis of hierarchical data. For large hierarchies, dynamic color maps can improve discriminability upon user interactions, but t…
Interactive Analysis of Global Explanations using Aggregated Class Activation Maps for Network Data
Igor Cherepanov, David Sessler, Alex Ulmer +3
Recent machine learning (ML) advances have demonstrated that deep learning (DL) achieves impressive results in different application domains, including the classification of comput…
Reflections on the Use of Dashboards in the Covid-19 Pandemic
Alessio Arleo, Rita Borgo, Jörn Kohlhammer +3
Dashboards have arguably been the most used visualizations during the COVID-19 pandemic. They were used to communicate its evolution to national governments for disaster mitigation…
Visualization Of Class Activation Maps To Explain AI Classification Of Network Packet Captures
Igor Cherepanov, Alex Ulmer, Jonathan Geraldi Joewono +1
The classification of internet traffic has become increasingly important due to the rapid growth of today's networks and applications. The number of connections and the addition of…