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Graph Neural Networks (GNNs) have shown superior performance in modeling graph data. Existing studies have shown that a lot of GNNs perform well on homophilic graphs while performing poorly on heterophilic graphs. Recently, researchers have turned their ...

The presence of unhealthy nodes in cloud infrastructure signals the potential failure of machines, which can significantly impact the availability and reliability of cloud services, resulting in negative customer experiences. Effectively addressing ...

Landslide displacement forecasting is a core issue in geohazard research, it is particularly challenging for accumulation-type landslides with complex geological patterns. Traditional landslide displacement prediction methods use single-point ...

Cloud-based machine learning services offer significant advantages but also introduce the risk of tampering with cloud-deployed deep neural network (DNN) models. Black-box integrity verification (BIV) allows model owners and end-users to determine if a ...

Icons are ubiquitous visual elements in graphic design, yet their creation is often complex and time-consuming. To resolve this problem, we draw inspiration from the booming text-to-image field and propose Text-Guided Icon Set Expansion, a novel task ...

We address the real problem of safe, robust, adaptive resource oversubscription in uncertain environments with our proposed novel technique of chance-constrained imitation learning. Our objective is to enhance resource efficiency while ensuring safety ...

Differentiable causal discovery has made significant advancements in the learning of directed acyclic graphs. However, its application to real-world datasets remains restricted due to the ubiquity of latent confounders and the requirement to learn ...

In large-scale online service systems, numerous Key Performance Indicators (KPIs), such as service response time and error rate, are gathered in a time-series format. KPI Anomaly Detection (KAD) is a critical data mining problem due to its widespread ...

In large-scale cloud service systems, monitoring metric data and conducting anomaly detection is an important way to maintain reliability and stability. However, great disparity exists between academic approaches and industrial practice to anomaly ...

Language models, such as GPT-3 and ChatGPT, demonstrate remarkable abilities to follow diverse human instructions and perform a wide range of tasks, using instruction fine-tuning. However, when we test language models with a range of basic table-...

Spreadsheets are widely recognized as the most popular end-user programming tools, which blend the power of formula-based computation, with an intuitive table-based interface. Today, spreadsheets are used by billions of users to manipulate tables, most ...

Cloud failure prediction (e.g., disk failure prediction, memory failure prediction, node failure prediction, etc.) is a crucial task for ensuring the reliability and performance of cloud systems.However, the problem of class imbalance poses a huge ...

Time series Anomaly Detection (AD) plays a crucial role for web systems. Various web systems rely on time series data to monitor and identify anomalies in real time, as well as to initiate diagnosis and remediation procedures. Variational Autoencoders (...

In this paper, we explore two robust models for the k-median and k-means problems: the outlier-version (k-MedO/k-MeaO) and the penalty-version (k-MedP/k-MeaP), enabling the marking and elimination of certain points as outliers. In k-MedO/k-MeaO, ...

Ensuring the reliability and availability of cloud services necessitates efficient root cause analysis (RCA) for cloud incidents. Traditional RCA methods, which rely on manual investigations of data sources such as logs and traces, are often laborious, ...

Large-scale cloud systems play a pivotal role in modern IT infrastructure. However, incidents occurring within these systems can lead to service disruptions and adversely affect user experience. To swiftly resolve such incidents, on-call engineers depend ...

Graph Neural Networks (GNNs) have achieved great success on a variety of tasks with graph-structural data, among which node classification is an essential one. Unsupervised Graph Domain Adaptation (UGDA) shows its practical value of reducing the labeling ...

Large language models (LLMs) are becoming attractive as few-shot reasoners to solve Natural Language (NL)-related tasks. However, there is still much to learn about how well LLMs understand structured data, such as tables. Although tables can be used as ...

Various storage and seepage spaces exist in fractured-vuggy carbonate reservoirs composed of multi-scale dissolution pores and fractures. The frequent regulation of the working system causes nonlinear and unstable production data along with ...

Recent years have witnessed the substantial progress of large-scale models across various domains, such as natural language processing and computer vision, facilitating the expression of concrete concepts. Unlike concrete concepts that are usually ...