Massive key performance indicators (KPIs) are monitored as multivariate time
series data (MTS) to ensure the reliability of the software applications and
service system. Accurately detecting the abnormality of MTS is very critical
for subsequent fault elimination. The scarcity of anomalies and manual labeling
has led to the development of various self-supervised MTS anomaly detection
(AD) methods, which optimize an overall objective/loss encompassing all
metrics' regression objectives/losses. However, our empirical study uncovers
the prevalence of conflicts among metrics' regression objectives, causing MTS
models to grapple with different losses. This critical aspect significantly
impacts detection performance but has been overlooked in existing approaches.
To address this problem, by mimicking the design of multi-gate
mixture-of-experts (MMoE), we introduce CAD, a Conflict-aware multivariate KPI
Anomaly Detection algorithm. CAD offers an exclusive structure for each metric
to mitigate potential conflicts while fostering inter-metric promotions. Upon
thorough investigation, we find that the poor performance of vanilla MMoE
mainly comes from the input-output misalignment settings of MTS formulation and
convergence issues arising from expansive tasks. To address these challenges,
we propose a straightforward yet effective task-oriented metric selection and
p&s (personalized and shared) gating mechanism, which establishes CAD as the
first practicable multi-task learning (MTL) based MTS AD model. Evaluations on
multiple public datasets reveal that CAD obtains an average F1-score of 0.943
across three public datasets, notably outperforming state-of-the-art methods.
Our code is accessible at this https URL

通过模仿多门专家混合（MMoE）的设计，我们引入了 CAD，一种冲突感知的多变量关键绩效指标异常检测算法，通过为每个指标提供独特的结构来缓解潜在冲突，同时促进指标之间的相互促进，并且通过简单而有效的任务导向的度量选择和个性化与共享的门控机制来建立 CAD 作为第一个实用的基于多任务学习的多变量时间序列异常检测模型，对多个公共数据集的评估表明，CAD 在三个公共数据集上获得了平均 F1 分数 0.943，显著优于现有的方法。

超越共享：冲突感知的多变量时间序列异常检测

Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly  Detection

Entity alignment aims to discover unique equivalent entity pairs with the
same meaning across different knowledge graphs (KGs). Existing models have
focused on projecting KGs into a latent embedding space so that inherent
semantics between entities can be captured for entity alignment. However, the
adverse impacts of alignment conflicts have been largely overlooked during
training, thereby limiting the entity alignment performance. To address this
issue, we propose a novel Conflict-aware Pseudo Labeling via Optimal Transport
model (CPL-OT) for entity alignment. The key idea is to iteratively
pseudo-label alignment pairs empowered with conflict-aware optimal transport
(OT) modeling to boost the precision of entity alignment. CPL-OT is composed of
two key components -- entity embedding learning with global-local aggregation
and iterative conflict-aware pseudo labeling -- that mutually reinforce each
other. To mitigate alignment conflicts during pseudo labeling, we propose to
use optimal transport as an effective means to warrant one-to-one entity
alignment between two KGs with the minimal overall transport cost. Extensive
experiments on benchmark datasets validate the superiority of CPL-OT over
state-of-the-art baselines under both settings with and without prior alignment
seeds.

本研究提出了一种基于最优传输建模的冲突感知伪标记（CPL-OT）模型，用于实现不同知识图谱之间的实体对齐，并证明其在基准数据集上比现有模型具有更好的表现。