396 research outputs found
Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Services
Time series anomaly detection is crucial for industrial monitoring services
that handle a large volume of data, aiming to ensure reliability and optimize
system performance. Existing methods often require extensive labeled resources
and manual parameter selection, highlighting the need for automation. This
paper proposes a comprehensive framework for automatic parameter optimization
in time series anomaly detection models. The framework introduces three
optimization targets: prediction score, shape score, and sensitivity score,
which can be easily adapted to different model backbones without prior
knowledge or manual labeling efforts. The proposed framework has been
successfully applied online for over six months, serving more than 50,000 time
series every minute. It simplifies the user's experience by requiring only an
expected sensitive value, offering a user-friendly interface, and achieving
desired detection results. Extensive evaluations conducted on public datasets
and comparison with other methods further confirm the effectiveness of the
proposed framework.Comment: Accepted by 2023 IJCAI Worksho
Conversational Word Embedding for Retrieval-Based Dialog System
Human conversations contain many types of information, e.g., knowledge,
common sense, and language habits. In this paper, we propose a conversational
word embedding method named PR-Embedding, which utilizes the conversation pairs
to learn word embedding. Different
from previous works, PR-Embedding uses the vectors from two different semantic
spaces to represent the words in post and reply. To catch the information among
the pair, we first introduce the word alignment model from statistical machine
translation to generate the cross-sentence window, then train the embedding on
word-level and sentence-level. We evaluate the method on single-turn and
multi-turn response selection tasks for retrieval-based dialog systems. The
experiment results show that PR-Embedding can improve the quality of the
selected response. PR-Embedding source code is available at
https://github.com/wtma/PR-EmbeddingComment: To appear at ACL 202
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