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Design, Automation and Test in Europe Conference and Exhibition

作者： Hongfei Wang Chenliang Luo Deqing Zou Hai Jin Wenjie Cai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Wuhan China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Wuhan China Huazhong University of Science and Technology Wuhan China Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China College of Public Administration Wuhan China

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Logic diagnosis is a key step in yield learning. Multiple faults diagnosis is challenging because of several reasons, including error masking, fault reinforcement, and huge search space for possible fault combinations. This work proposes a two-phase method for multiple-fault diagnosis. The first phase efficiently reduces the potential number of fault candidates through machine learning. The second phase obtains the final diagnosis results, by formulating the task as an combinational optimization problem that is later iteratively solved using binary evolution computation. Experiments shows that our method outperforms two existing methods for multiple-fault diagnosis, and achieves better diagnosability (improved by $1.87\times$ ) and resolution (improved by $1.42\times$ ) compared with a state-of-the-art commercial diagnosis tool.

关键词： Fault diagnosis Adaptation models Computational modeling Fitting Filtering algorithms Boosting Computational efficiency

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How to Select Pre-Trained Code Models for Reuse? A Learning Perspective

arXiv

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arXiv 2025年

作者： Bi, Zhangqian Wan, Yao Chu, Zhaoyang Hu, Yufei Zhang, Junyi Zhang, Hongyu Xu, Guandong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Big Data and Software Engineering Chongqing University Chongqing China School of Computer Science University of Technology Sydney Sydney Australia

Pre-training a language model and then fine-tuning it has shown to be an efficient and effective technique for a wide range of code intelligence tasks, such as code generation, code summarization, and vulnerability detection. However, pre-training language models on a large-scale code corpus is computationally expensive. Fortunately, many off-the-shelf Pre-trained Code Models (PCMs), such as CodeBERT, CodeT5, CodeGen, and Code Llama, have been released publicly. These models acquire general code understanding and generation capability during pre-training, which enhances their performance on downstream code intelligence tasks. With an increasing number of these public pre-trained models, selecting the most suitable one to reuse for a specific task is essential. In this paper, we systematically investigate the reusability of PCMs. We first explore three intuitive model selection methods that select by size, training data, or brute-force fine-tuning. Experimental results show that these straightforward techniques either perform poorly or suffer high costs. Motivated by these findings, we explore learning-based model selection strategies that utilize pre-trained models without altering their parameters. Specifically, we train proxy models to gauge the performance of pre-trained models, and measure the distribution deviation between a model's latent features and the task's labels, using their closeness as an indicator of model transferability. We conduct experiments on 100 widely-used open-source PCMs for code intelligence tasks, with sizes ranging from 42.5 million to 3 billion parameters. The results demonstrate that learning-based selection methods reduce selection time to 100 seconds, compared to 2,700 hours with brute-force fine-tuning, with less than 6% performance degradation across related tasks. © 2025, CC BY-NC-SA.

关键词： Adversarial machine learning

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ACGraph: Accelerating Streaming Graph Processing via Dependence Hierarchy 23

ACGraph: Accelerating Streaming Graph Processing via Depende...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Zihan Jiang Fubing Mao Yapu Guo Xu Liu Haikun Liu Xiaofei Liao Hai Jin Wei Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electronic and Computer Engineering HKUST Hong Kong

ISBN: (纸本)9798350323481

Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates. To address this issue, we propose ACGraph, a novel streaming graph processing approach for monotonic graph algorithms. It maintains dependence trees during runtime, and makes affected vertices processed in a top-to-bottom order in the hierarchy of the dependence trees, thus normalizing the state propagation order and coalescing of multiple propagation to the same vertices. Experimental results show that ACGraph reduces the number of updates by 50% on average, and achieves the speedup of 1.75~7.43× over state-of-the-art systems.

关键词： graph processing

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UICopilot: Automating UI Synthesis via Hierarchical Code Generation from Webpage Designs 25

UICopilot: Automating UI Synthesis via Hierarchical Code Gen...

引用

34th ACM Web Conference, WWW 2025

作者： Gui, Yi Wan, Yao Li, Zhen Zhang, Zhongyi Chen, Dongping Zhang, Hongyu Su, Yi Chen, Bohua Zhou, Xing Jiang, Wenbin Zhang, Xiangliang Huazhong University of Science and Technology Wuhan China Chongqing University Chongqing China Hubei University of Automotive Technology Shiyan China Rabbitpre AI Shenzhen China University of Notre Dame Notre Dame United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster Grid Computing Lab School of Computer Science and Technology China

ISBN: (纸本)9798400712746

Automating the synthesis of User Interfaces (UIs) plays a crucial role in enhancing productivity and accelerating the development lifecycle, reducing both development time and manual effort. Recently, the rapid development of Multimodal Large Language Models (MLLMs) has made it possible to generate front-end Hypertext Markup Language (HTML) code directly from webpage designs. However, real-world webpages encompass not only a diverse array of HTML tags but also complex stylesheets, resulting in significantly lengthy code. The lengthy code poses challenges for the performance and efficiency of MLLMs, especially in capturing the structural information of UI designs. To address these challenges, this paper proposes UICopilot, a novel approach to automating UI synthesis via hierarchical code generation from webpage designs. The core idea of UICopilot is to decompose the generation process into two stages: first, generating the coarse-grained HTML hierarchical structure, followed by the generation of fine-grained code. To validate the effectiveness of UICopilot, we conduct experiments on a real-world dataset, i.e., WebCode2M. Experimental results demonstrate that UICopilot significantly outperforms existing baselines in both automatic evaluation metrics and human evaluations. Specifically, statistical analysis reveals that the majority of human annotators prefer the webpages generated by UICopilot over those produced by GPT-4V. © 2025 Copyright held by the owner/author(s).

关键词： Web Design

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EagerLog: Active Learning Enhanced Retrieval Augmented Generation for Log-based Anomaly Detection

EagerLog: Active Learning Enhanced Retrieval Augmented Gener...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Duan, Chiming Jia, Tong Yang, Yong Liu, Guiyang Liu, Jinbu Zhang, Huxing Zhou, Qi Li, Ying Huang, Gang School of Software and Microelectronics Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China National Key Laboratory of Data Space Technology and System Beijing China Alibaba Cloud Computing Company Hangzhou China

ISBN: (纸本)9798350368741

Logs record essential information about system operations and serve as a critical source for anomaly detection, which has generated growing research interest. Utilizing large language models (LLMs) within a retrieval-augmented generation (RAG) framework for log-based anomaly detection is an effective approach due to its strong generalization capabilities and efficient few-shot performance. However, the effectiveness of this method hinges on the quality of the knowledge source, which can be impacted by noise and changes within the software systems. Facing these problems, in this paper, we propose a novel log-based anomaly detection method named EagerLog, employing active learning to choose the logs for humans to label, thereby adding them to the knowledge source, thus enhancing the knowledge source and maintaining its quality. Our experiments on three open datasets (BGL, Thunderbird, Zookeeper) and one industrial dataset demonstrate that EagerLog can achieve 93.65% F1 score with approximately 10 labeled log sequences, surpassing existing methods by 15.32%. © 2025 IEEE.

关键词： Active Learning Anomaly Detection Log Analysis Retrieval Augmented Generation

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How to Select Pre-Trained Code Models for Reuse? A Learning Perspective

How to Select Pre-Trained Code Models for Reuse? A Learning ...

引用

IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Zhangqian Bi Yao Wan Zhaoyang Chu Yufei Hu Junyi Zhang Hongyu Zhang Guandong Xu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Big Data and Software Engineering Chongqing University Chongqing China School of Computer Science University of Technology Sydney Sydney Australia

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

Pre-training a language model and then fine-tuning it has shown to be an efficient and effective technique for a wide range of code intelligence tasks, such as code generation, code summarization, and vulnerability detection. However, pre-training language models on a large-scale code corpus is compu-tationally expensive. Fortunately, many off-the-shelf Pre-trained Code Models (PCMs), such as CodeBERT, CodeT5, CodeGen, and Code Llama, have been released publicly. These models acquire general code understanding and generation capability during pre-training, which enhances their performance on downstream code intelligence tasks. With an increasing number of these public pre-trained models, selecting the most suitable one to reuse for a specific task is essential. In this paper, we systematically investigate the reusability of PCMs. We first explore three intuitive model selection methods that select by size, training data, or brute-force fine-tuning. Experimental results show that these straightforward techniques either perform poorly or suffer high costs. Motivated by these findings, we explore learning-based model selection strategies that utilize pre-trained models without altering their parameters. Specifically, we train proxy models to gauge the performance of pre-trained models, and measure the distribution deviation between a model's latent features and the task's labels, using their closeness as an indicator of model transferability. We conduct experiments on 100 widely-used open-source PCMs for code intelligence tasks, with sizes ranging from 42.5 million to 3 billion parameters. The results demonstrate that learning-based selection methods reduce selection time to 100 seconds, compared to 2,700 hours with brute-force fine-tuning, with less than 6% performance degradation across related tasks.

关键词： Phase change materials Analytical models Adaptation models Codes Costs Computational modeling Training data Machine learning Software Software development management

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Efficient Relaxed Functional Dependency Discovery with Minimal Set Cover

Efficient Relaxed Functional Dependency Discovery with Minim...

引用

International Conference on data engineering

作者： Xiaoou Ding Yida Liu Hongzhi Wang Chen Wang Yichen Song Donghua Yang Jianmin Wang School of Computer Science and Technology Harbin Institute of Technology National Engineering Research Center for Big Data Software EIRI Tsinghua University

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Assessing data quality through Functional Depen-dencies (FDs) is a crucial aspect of data governance. However, with the diverse range of data sources and the exponential growth in data volume, exact FDs can sometimes be impractical for real-world applications. In contrast, relaxed functional dependencies (RFDs), which allows for some flexibility in attribute value comparisons, demonstrates greater adaptability and flexibility for big data scenarios. To address the efficient discovery of RFDs, this paper proposes a novel mining method to supplement the current research gaps. By establishing a difference table for tuples, we transform the problem into a specialized minimal set covering problem. Additionally, we introduce two optimization strategies: reducing the time complexity of enumerating the left-hand side of the base RFDs to 0 (1) and decreasing the search complexity for feasible LHS attributes and threshold candidates from O(2 m-l ) to O(1.5 m-1 ). We rigorously proof that our mining approach guarantees the identification of validity and minimal RFDs. Experiments on nine real-world datasets reveal that our method significantly improves efficiency compared to existing techniques. Furthermore, it uncovers more concise and higher-quality RFDs. Importantly, the RFDs extracted through our methodology exhibit better performance in downstream cleaning tasks.

关键词： data integrity Soft sensors Transforms data engineering Cleaning data governance data mining

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TSDDISCOVER: Discovering data Dependency for Time Series data

TSDDISCOVER: Discovering Data Dependency for Time Series Dat...

引用

International Conference on data engineering

作者： Xiaoou Ding Yingze Li Hongzhi Wang Chen Wang Yida Liu Jianmin Wang School of Computer Science and Technology Harbin Institute of Technology National Engineering Research Center for Big Data Software EIRI Tsinghua University

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Intelligent devices often produce time series data that suffer from significant data quality issues. While the utilization of data dependency in error detection and data repair has been somewhat beneficial, it remains inadequate in accurately representing the data quality of time series datasets. In recognition of the obvious characteristics inherent in time series data, we introduce a novel data dependency, termed TSDD. It effectively captures the contextual relationships embedded within multivariate time series, thereby enhancing the semantic richness of data quality representations. We analyze the complexity of both implication and consistency problems for TSDD reasoning, and develop TSDD discovery algorithm TSDDISCOVER, which consists of functional structure discovery, allowable error bound determination, and validation of TSDD patterns. Experimental results on real-life datasets verify TSDDISCOVER efficiently discovers high-quality TSDD patterns. In comparing the performance of TSDD-based error detection with several leading data quality constraints, our findings reveal that the former achieves an average improvement of 12% in accuracy and 30% in the F1 score over other dependency-based detection methods.

关键词： data integrity Time series analysis Semantics Maintenance engineering data engineering Cognition Cleaning

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DarkFed: A data-Free Backdoor Attack in Federated Learning

arXiv

引用

arXiv 2024年

作者： Li, Minghui Wan, Wei Ning, Yuxuan Hu, Shengshan Xue, Lulu Zhang, Leo Yu Wang, Yichen School of Software Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Federated learning (FL) has been demonstrated to be susceptible to backdoor attacks. However, existing academic studies on FL backdoor attacks rely on a high proportion of real clients with main task-related data, which is impractical. In the context of real-world industrial scenarios, even the simplest defense suffices to defend against the state-of-the-art attack, 3DFed. A practical FL backdoor attack remains in a nascent stage of development. To bridge this gap, we present DarkFed. Initially, we emulate a series of fake clients, thereby achieving the attacker proportion typical of academic research scenarios. Given that these emulated fake clients lack genuine training data, we further propose a data-free approach to backdoor FL. Specifically, we delve into the feasibility of injecting a backdoor using a shadow dataset. Our exploration reveals that impressive attack performance can be achieved, even when there is a substantial gap between the shadow dataset and the main task dataset. This holds true even when employing synthetic data devoid of any semantic information as the shadow dataset. Subsequently, we strategically construct a series of covert backdoor updates in an optimized manner, mimicking the properties of benign updates, to evade detection by defenses. A substantial body of empirical evidence validates the tangible effectiveness of DarkFed. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Nonreciprocity of energy transfer in a nonlinear asymmetric oscillator system with various vibration states

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Applied Mathematics and Mechanics(English Edition) 2023年第5期44卷 727-744页

作者： Jian’en CHEN Jianling LI Minghui YAO Jun LIU Jianhua ZHANG Min SUN Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical EngineeringTianjin University of TechnologyTianjin 300384China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of TechnologyTianjin 300384China School of Aeronautics and Astronautics Tiangong UniversityTianjin 300387China Agricultural Information Institute of Chinese Academy of Agricultural Sciences Key Laboratory of Agricultural Big DataMinistry of Agriculture and Rural AffairsBeijing 100081China School of Science Tianjin Chengjian UniversityTianjin 300384China

The nonreciprocity of energy transfer is constructed in a nonlinear asymmetric oscillator system that comprises two nonlinear oscillators with different parameters placed between two identical linear *** slow-flow equation of the system is derived by the complexification-averaging *** semi-analytical solutions to this equation are obtained by the least squares method,which are compared with the numerical solutions obtained by the Runge-Kutta *** distribution of the average energy in the system is studied under periodic and chaotic vibration states,and the energy transfer along two opposite directions is *** effect of the excitation amplitude on the nonreciprocity of the system producing the periodic responses is analyzed,where a three-stage energy transfer phenomenon is *** the first stage,the energy transfer along the two opposite directions is approximately equal,whereas in the second stage,the asymmetric energy transfer is *** energy transfer is also asymmetric in the third stage,but the direction is reversed compared with the second ***,the excitation amplitude for exciting the bifurcation also shows an asymmetric *** vibrations are generated around the resonant frequency,irrespective of which linear oscillator is *** excitation threshold of these chaotic vibrations is dependent on the linear oscillator that is being *** addition,the difference between the energy transfer in the two opposite directions is used to further analyze the nonreciprocity in the *** results show that the nonreciprocity significantly depends on the excitation frequency and the excitation amplitude.

关键词： nonreciprocity strong nonlinearity energy transfer chaotic vibration higher branch of response

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