Previous security research efforts orbiting around graphs have been exclusively focusing on either (de-)anonymizing the graphs or understanding the security and privacy issues of graph neural networks.
PSPI can be viewed as a robust formulation of the permutation inference or graph matching, where the objective is to find a permutation between two graphs under the assumption that a set of edges may have undergone a perturbation due to an underlying cause.
Log files are files that record events, messages, or transactions.
Autoencoder-based anomaly detection methods have been used in identifying anomalous users from large-scale enterprise logs with the assumption that adversarial activities do not follow past habitual patterns.
The experimental results show that the STAN model can consistently improve the state of the arts in both action detection and action recognition tasks.
We finally apply our model on PAKDD papers published from 2009-2019 to mine insightful results from scientific papers published in a longer time span.
We investigate dynamical generation of macroscopic nonlocal entanglements between two remote massive magnon-superconducting-circuit hybrid systems.
The first contribution is a new dataset, created in a realistic 3D simulator, which is based on real world trajectory data, and then extrapolated by human annotators to achieve different latent goals.
Ranked #1 on Multi-future Trajectory Prediction on ForkingPaths
It is both crucial and natural to extend this research direction to the video domain for video question answering (VideoQA).
Ranked #2 on Video Question Answering on ActivityNet-QA
In this paper, we explore the beneficial effect of undetermined relationships on visual relationship detection.