10 citations · 36 across the 9 of their papers we have counts for
12 papers
FAIRification of MLC data
Ana Kostovska, Jasmin Bogatinovski, Andrej Treven +3
The multi-label classification (MLC) task has increasingly been receiving interest from the machine learning (ML) community, as evidenced by the growing number of papers and method…
Failure Identification from Unstable Log Data using Deep Learning
Jasmin Bogatinovski, Sasho Nedelkoski, Li Wu +2
The reliability of cloud platforms is of significant relevance because society increasingly relies on complex software systems running on the cloud. To improve it, cloud providers…
Data-Driven Approach for Log Instruction Quality Assessment
Jasmin Bogatinovski, Sasho Nedelkoski, Alexander Acker +2
In the current IT world, developers write code while system operators run the code mostly as a black box. The connection between both worlds is typically established with log messa…
A2Log: Attentive Augmented Log Anomaly Detection
Thorsten Wittkopp, Alexander Acker, Sasho Nedelkoski +4
Anomaly detection becomes increasingly important for the dependability and serviceability of IT services. As log lines record events during the execution of IT services, they are a…
Explaining the Performance of Multi-label Classification Methods with Data Set Properties
Jasmin Bogatinovski, Ljupčo Todorovski, Sašo Džeroski +1
Meta learning generalizes the empirical experience with different learning tasks and holds promise for providing important empirical insight into the behaviour of machine learning…
Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models
Harold Ott, Jasmin Bogatinovski, Alexander Acker +2
Anomalies or failures in large computer systems, such as the cloud, have an impact on a large number of users that communicate, compute, and store information. Therefore, timely an…