Distributed tracing has been an important part of microservice infrastructure and learning-based trace analysis has been used to detect anomalies in microservice systems. Existing learning-based trace anomaly detectio...
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ISBN:
(数字)9781665451321
ISBN:
(纸本)9781665451338
Distributed tracing has been an important part of microservice infrastructure and learning-based trace analysis has been used to detect anomalies in microservice systems. Existing learning-based trace anomaly detection approaches ei-ther assume that trace patterns can be learned from normal execution or rely on fault injection to produce labeled traces (i.e., normal/anomalous ones). However, in practice it is often difficult to ensure that the normal execution does not involve anomalous traces or obtain a large variety of normal and anomalous traces through fault injection. In this paper, we propose PUTraceAD, a trace anomaly detection approach that can alleviate the above problems. PUTraceAD represents a trace as a span causal graph with node features such as operation name, response code, duration time. Based on the graph representation, PUTraceAD trains a GNN- and PU learning-based trace anomaly detection model. During the process, PU (Positive and Unlabeled) learning optimizes model parameters through estimating the data distribution. Therefore, PUTraceAD can train the model based on a small set of labeled anomalous traces and a large set of unlabeled traces. Our evaluation shows that PUTraceAD outperforms existing unsupervised trace anomaly detection approaches and only slightly underperforms a supervised learning-based approach that takes full advantage of labeled traces.
Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many ...
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Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline invol...
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The term telemedicine was first used in the 1920s, although used many years ago and has continued to evolve today. Medical diagnoses usually require visual information, but remote display systems have recently become ...
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High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they ...
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Multi-level cache systems enhance I/O performance by optimizing data placement across various cache levels from a global perspective. However, existing methods often struggle to place data at the optimal cache level p...
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While deep learning has significantly advanced point cloud analysis, extracting effective features from their disordered structure remains challenging. Existing approaches often rely on complex network architectures o...
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ISBN:
(数字)9798331520861
ISBN:
(纸本)9798331520878
While deep learning has significantly advanced point cloud analysis, extracting effective features from their disordered structure remains challenging. Existing approaches often rely on complex network architectures or manual feature engineering to achieve rotation invariance, which increases inference latency. This paper offers a novel perspective on this challenge, showing that rotation-sensitive models can achieve performance comparable to rotation-invariant models when trained with rotation-augmented data. Given the significant differences in the feature distributions of rotation-sensitive and rotation-invariant models, we propose Feature Rotation Invariance Learning (FRIL), a framework that leverages Distance Metric Learning and rotation-based constraints to help rotation-sensitive models extract rotation-invariant features. This approach eliminates the need for additional modules, enabling robust performance against rotations. Extensive experiments demonstrate that FRIL consistently outperforms existing rotation-invariant methods across various visual tasks.
We retarget video stitching to an emerging issue, named warping shake, which unveils the temporal content shakes induced by sequentially unsmooth warps when extending image stitching to video stitching. Even if the in...
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Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are still struggling to achieve high learn...
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Emerging non-volatile devices have shown great potential in computing in-memory (CIM). This work proposes a logic design method based on the complementary resistance switching (CRS) structure, which is connected by tw...
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