12,943 research outputs found
Graph Neural Networks based Log Anomaly Detection and Explanation
Event logs are widely used to record the status of high-tech systems, making
log anomaly detection important for monitoring those systems. Most existing log
anomaly detection methods take a log event count matrix or log event sequences
as input, exploiting quantitative and/or sequential relationships between log
events to detect anomalies. Unfortunately, only considering quantitative or
sequential relationships may result in low detection accuracy. To alleviate
this problem, we propose a graph-based method for unsupervised log anomaly
detection, dubbed Logs2Graphs, which first converts event logs into attributed,
directed, and weighted graphs, and then leverages graph neural networks to
perform graph-level anomaly detection. Specifically, we introduce One-Class
Digraph Inception Convolutional Networks, abbreviated as OCDiGCN, a novel graph
neural network model for detecting graph-level anomalies in a collection of
attributed, directed, and weighted graphs. By coupling the graph representation
and anomaly detection steps, OCDiGCN can learn a representation that is
especially suited for anomaly detection, resulting in a high detection
accuracy. Importantly, for each identified anomaly, we additionally provide a
small subset of nodes that play a crucial role in OCDiGCN's prediction as
explanations, which can offer valuable cues for subsequent root cause
diagnosis. Experiments on five benchmark datasets show that Logs2Graphs
performs at least on par with state-of-the-art log anomaly detection methods on
simple datasets while largely outperforming state-of-the-art log anomaly
detection methods on complicated datasets.Comment: Preprint submitted to Engineering Applications of Artificial
Intelligenc
SwG-former: Sliding-window Graph Convolutional Network Integrated with Conformer for Sound Event Localization and Detection
Sound event localization and detection (SELD) is a joint task of sound event
detection (SED) and direction of arrival (DoA) estimation. SED mainly relies on
temporal dependencies to distinguish different sound classes, while DoA
estimation depends on spatial correlations to estimate source directions. To
jointly optimize two subtasks, the SELD system should extract spatial
correlations and model temporal dependencies simultaneously. However, numerous
models mainly extract spatial correlations and model temporal dependencies
separately. In this paper, the interdependence of spatial-temporal information
in audio signals is exploited for simultaneous extraction to enhance the model
performance. In response, a novel graph representation leveraging graph
convolutional network (GCN) in non-Euclidean space is developed to extract
spatial-temporal information concurrently. A sliding-window graph (SwG) module
is designed based on the graph representation. It exploits sliding-windows with
different sizes to learn temporal context information and dynamically
constructs graph vertices in the frequency-channel (F-C) domain to capture
spatial correlations. Furthermore, as the cornerstone of message passing, a
robust Conv2dAgg function is proposed and embedded into the SwG module to
aggregate the features of neighbor vertices. To improve the performance of SELD
in a natural spatial acoustic environment, a general and efficient SwG-former
model is proposed by integrating the SwG module with the Conformer. It exhibits
superior performance in comparison to recent advanced SELD models. To further
validate the generality and efficiency of the SwG-former, it is seamlessly
integrated into the event-independent network version 2 (EINV2) called
SwG-EINV2. The SwG-EINV2 surpasses the state-of-the-art (SOTA) methods under
the same acoustic environment
Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation
Event extraction is of practical utility in natural language processing. In
the real world, it is a common phenomenon that multiple events existing in the
same sentence, where extracting them are more difficult than extracting a
single event. Previous works on modeling the associations between events by
sequential modeling methods suffer a lot from the low efficiency in capturing
very long-range dependencies. In this paper, we propose a novel Jointly
Multiple Events Extraction (JMEE) framework to jointly extract multiple event
triggers and arguments by introducing syntactic shortcut arcs to enhance
information flow and attention-based graph convolution networks to model graph
information. The experiment results demonstrate that our proposed framework
achieves competitive results compared with state-of-the-art methods.Comment: accepted by EMNLP 201
VideoGraph: Recognizing Minutes-Long Human Activities in Videos
Many human activities take minutes to unfold. To represent them, related
works opt for statistical pooling, which neglects the temporal structure.
Others opt for convolutional methods, as CNN and Non-Local. While successful in
learning temporal concepts, they are short of modeling minutes-long temporal
dependencies. We propose VideoGraph, a method to achieve the best of two
worlds: represent minutes-long human activities and learn their underlying
temporal structure. VideoGraph learns a graph-based representation for human
activities. The graph, its nodes and edges are learned entirely from video
datasets, making VideoGraph applicable to problems without node-level
annotation. The result is improvements over related works on benchmarks:
Epic-Kitchen and Breakfast. Besides, we demonstrate that VideoGraph is able to
learn the temporal structure of human activities in minutes-long videos
- …