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34th ACM Web Conference, WWW 2025

作者： Wang, Yejing Zhang, Chi Zhao, Xiangyu Liu, Qidong Wang, Maolin Wei, Xuetao Liu, Zitao Shi, Xing Yang, Xudong Zhong, Ling Lin, Wei City University of Hong Kong Hong Kong Harbin Engineering University Harbin China Xi’an Jiaotong University Xi’an China City University of Hong Kong Xi’an China Southern University of Science and Technology Shenzhen China Jinan University Guang Zhou China Alibaba Cloud Hangzhou China

ISBN: (纸本)9798400712746

Delivering superior search services is crucial for enhancing customer experience and driving revenue growth in e-commerce. Conventionally, search systems model user behaviors by combining user preference and query-item relevance statically, often through a fixed logical ‘and’ relationship. This paper reexamines existing approaches through a unified lens using both causal graphs and Venn diagrams, uncovering two prevalent yet significant issues: entangled preference and relevance effects, and a collapsed modeling space. To surmount these challenges, our research introduces a novel framework, DRP, which enhances search accuracy through two components to reconstruct the behavior modeling space. Specifically, we implement preference editing to proactively remove the relevance effect from preference predictions, yielding untainted user preferences. Additionally, we employ adaptive fusion, which dynamically adjusts fusion criteria to align with the varying patterns of relevance and preference, facilitating more nuanced and tailored behavior predictions within the reconstructed modeling space. Empirical validation on two public datasets and a proprietary e-commerce search dataset underscores the superiority of our proposed methodology, demonstrating marked improvements in performance over existing approaches. © 2025 Copyright held by the owner/author(s).

关键词： Marketplaces

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DARC: High-dimensional Diffusing Anomaly Detection and Root Cause Location in cloud computing Systems

DARC: High-dimensional Diffusing Anomaly Detection and Root ...

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IEEE International Conference on Big Data

作者： Wanning Sun Xuhua Ma Ruimin Peng Yifan Xu Dongrui Wu Alibaba Cloud Alibaba Group Beijing China Alibaba Cloud Alibaba Group Hangzhou China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

The modern cloud computing system has evolved into a highly dynamic and complex ecosystem with thousands of modules. Modifications and updates to these modules occur every day to accommodate customer needs. Unfortunately, these frequent changes may introduce anomalies to the system, whose diffusion can undermine the system performance and even cause system outage. Though very important, it is challenging to detect anomalies at the early stages and locate their root causes, due to the complexity of the cloud ecosystem and the huge number of attribute combinations. This paper proposes DARC for high-dimensional diffusing anomaly detection and root cause location in cloud computing systems. DARC uses first two-stage percentile analysis and Mann-Kendall score thresholding to detect rare anomalies, and then a bottom-up search strategy with three computational complexity reduction techniques to efficiently locate the root causes. Extensive experiments showed that DARC is able to accurately and efficiently locate root causes of diffusing anomalies. It has been successfully used in the daily practice of alibaba cloud, one of the world’s largest cloud computing service providers.

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

EagerLog: Active Learning Enhanced Retrieval Augmented Gener...

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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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Some Thoughts on the Classification Standard of Intelligent Autonomous Level of Measurement and Control System 9

Some Thoughts on the Classification Standard of Intelligent ...

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9th International Conference on Big Data and Information Analytics, BigDIA 2023

作者： Yuan, Guangfu Yang, Liu Han, Haoran Li, Yong Shao, Cong Zhan, Siyu Gansu Photoelectric Measurement Technology Research Laboratory Gansu735018 China University of Electronic Science and Technology of China Laboratory of Intelligent Collaborative Computing Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China

ISBN: (纸本)9798350330076

Intelligence is an important trend in the development of measurement and control systems. However, there is still a lack of unified understanding of the intelligent autonomous classification of the measurement and control system. The establishment of intelligent evaluation standards in this field has become an urgent research topic. This paper first gives an overview of the aviation measurement and control system, and then takes the classification standards of autonomy in the fields of automobile driving automation in china and the United states, the autonomy and autonomy level of typical ships in china, and the automation of civil unmanned aircraft system in china as a reference. A 5-level intelligent grading standard based on task adaptability, environmental complexity and human-computer interaction is proposed. The classification standard is practical and easy to understand. It can provide reference for the establishment of intelligent standards for measurement and control systems, improve the application speed of intelligent autonomous technology in the field of measurement and control, and broaden the application prospect of intelligent measurement and control systems. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Research on cross-project software defect prediction based on feature transfer method 4

Research on cross-project software defect prediction based o...

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4th International Conference on Advanced Information science and System, AISS 2022

作者： Wang, Wennan Zhao, Hanxu Li, Yu Su, Junyu Lu, Jiadong Wang, Baoping Academy of Management Guangdong University of Science and Technology China Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology China Institute of Data Science City University of Macau China

ISBN: (纸本)9781450397933

In this paper, the research and experimental analysis of cross-project application software defect prediction is carried out, and the TCA model is used to improve the application function of its prediction. The models pointed out in this paper usually include: normalization processing model and mathematical linear kernel mathematical statistics The difference between the functional SVM classifier and the extended migration component analysis TCA+ model is that the model pointed out in this paper not only satisfies the prediction of software defects within the project suitable for TCA, but also meets the prediction of software defects in the cross-project of TCA+, so the most appropriate normalization can be selected. Optimized processing options to improve cross-project software defect prediction capabilities. © 2022 ACM.

关键词： Forecasting

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Enhancing Energy Efficiency with a Dynamic Trust Measurement Scheme in Power Distribution Network

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Computers, Materials & Continua 2024年第3期78卷 3909-3927页

作者： Yilei Wang Xin Sun Guiping Zheng Ahmar Rashid Sami Ullah Hisham Alasmary Muhammad Waqas Zhejiang Electric-Power Corporation Research Institute Zhejiang310014China Faculty of Information Technology Beijing University of TechnologyBeijing100124China Beijing Trusty Cloud Technology Co. Ltd.Beijing100022China Department of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and EngineeringTopi23640Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir18050Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia School of Computing and Mathematical Science Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerth6027Australia

The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent *** sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and ***,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion ***,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart *** response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these *** scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT *** then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy *** comprehensive approach reduces the impact of subjective factors on trust ***,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious ***,in turn,enhances the security and reliability of the smart grid *** effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation ***,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.

关键词： IIoT trusted measure energy efficient

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A Spatial-temporal Neural Network for Photovoltaic Power Prediction 13

A Spatial-temporal Neural Network for Photovoltaic Power Pre...

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13th International Conference on Power and Energy Systems, ICPES 2023

作者： Huang, Yan Cao, Junwei Cai, Shixia Tsinghua University Department of Automation Beijing China Tsinghua University Beijing National Research Center of Information Science and Technology Beijing China Alibaba Cloud Intelligence Group Global Technical Services Business Unit Beijing China

ISBN: (纸本)9798350345063

The access of photovoltaic (PV) systems in the distribution networks has facilitated accurate PV power prediction for energy coordination and operations planning. Due to the power quality and photoelectric consumption of distribution system, the prediction task is still challenging. This paper aims to provide a spatial-temporal network to capture appropriate dependencies between temporal historical PV data and spatial effect factors for the multiple-step situational predictions. It involves a simplified version of convolution and pooling operation used to capture local features and model spatial correlations in the input data, and an attention based bidirectional long short-term memory (BiLSTM) used to enhance that association data prediction. Experimental results demonstrate that the proposed prediction network indicates enhanced accuracy across various time intervals and exhibits superior performance in prediction stability and robustness when compared to typical prediction methods based on the actual measurements from a photovoltaic micro plant. The method excels in capturing both temporal and spatial dependencies, making it a valuable tool for energy planning and operations in distribution networks. © 2023 IEEE.

关键词： Convolution

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Coarse-to-Fine Proposal Refinement Framework for Audio Temporal Forgery Detection and Localization 24

Coarse-to-Fine Proposal Refinement Framework for Audio Tempo...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Junyan Lu, Wei Luo, Xiangyang Yang, Rui Wang, Qian Cao, Xiaochun School of Computer Science and Engineering Sun Yat-sen University Guangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Alibaba Group Hangzhou China School of Cyber Science and Engineering Wuhan University Wuhan China School of Cyber Science and Technology Sun Yat-sen University Shenzhen Campus Shenzhen China

ISBN: (纸本)9798400706868

Recently, a novel form of audio partial forgery has posed challenges to its forensics, requiring advanced countermeasures to detect subtle forgery manipulations within long-duration audio. However, existing countermeasures still serve a classification purpose and fail to perform meaningful analysis of the start and end timestamps of partial forgery segments. To address this challenge, we introduce a novel coarse-to-fine proposal refinement framework (CFPRF) that incorporates a frame-level detection network (FDN) and a proposal refinement network (PRN) for audio temporal forgery detection and localization. Specifically, the FDN aims to mine informative inconsistency cues between real and fake frames to obtain discriminative features that are beneficial for roughly indicating forgery regions. The PRN is responsible for predicting confidence scores and regression offsets to refine the coarse-grained proposals derived from the FDN. To learn robust discriminative features, we devise a difference-aware feature learning (DAFL) module guided by contrastive representation learning to enlarge the sensitive differences between different frames induced by minor manipulations. We further design a boundary-aware feature enhancement (BAFE) module to capture the contextual information of multiple transition boundaries and guide the interaction between boundary information and temporal features via a cross-attention mechanism. Extensive experiments show that our CFPRF achieves state-of-the-art performance on various datasets, including LAV-DF, ASVS2019PS, and HAD. © 2024 ACM.

关键词： Contrastive Learning

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Relational Algorithms for Top-k Query Evaluation

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Proceedings of the ACM on Management of Data 2024年第3期2卷 1-27页

作者： Qichen Wang Qiyao Luo Yilei Wang Hong Kong Baptist University Hong Kong SAR China Hong Kong University of Science and Technology Hong Kong SAR China Alibaba Cloud Hangzhou China

The evaluation of top-k conjunctive queries, a staple in business analysis, often requires evaluating the conjunctive query prior to filtering the top-k results, leading to a significant computational overhead within Database Management Systems (DBMSs). While efficient algorithms have been proposed, their integration into DBMSs remains arduous. We introduce relational algorithms, a paradigm where each algorithmic step is expressed by a relational operator. This allows the algorithm to be represented as a set of SQL queries, enabling easy deployment across different systems that support SQL. We introduce two novel relational algorithms, level-k and product-k, specifically designed for evaluating top-k conjunctive queries and demonstrate that level-k achieves optimal running time for top-k free-connex queries. Furthermore, these algorithms enable easy translation into an oblivious algorithm for secure query evaluations. The presented algorithms are not only theoretically optimal but also exhibit eminent efficiency in practice. The experiment results show significant improvements, with our rewritten SQL outperforming the baseline by up to 6 orders of magnitude. Moreover, our secure implementations not only achieve substantial speedup compared to the baseline with secure guarantees but even surpass those baselines that have no secure guarantees.

关键词： conjunctive query relational algorithm secure multi-party computation top-k query

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Lifelong Knowledge Editing for Vision Language Models with Low-Rank Mixture-of-Experts

arXiv

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arXiv 2024年

作者： Chen, Qizhou Wang, Chengyu Wang, Dakan Zhang, Taolin Li, Wangyue He, Xiaofeng East China Normal University Shanghai China Alibaba Cloud Computing Hangzhou China Alibaba Group Hangzhou China Exacity Inc. Shanghai China Hefei University of Technology Hefei China

Model editing aims to correct inaccurate knowledge, update outdated information, and incorporate new data into Large Language Models (LLMs) without the need for retraining. This task poses challenges in lifelong scenarios where edits must be continuously applied for real-world applications. While some editors demonstrate strong robustness for lifelong editing in pure LLMs, Vision LLMs (VLLMs), which incorporate an additional vision modality, are not directly adaptable to existing LLM editors. In this paper, we propose LiveEdit, a Lifelong vision language model Edit to bridge the gap between lifelong LLM editing and VLLMs. We begin by training an editing expert generator to independently produce low-rank experts for each editing instance, with the goal of correcting the relevant responses of the VLLM. A hard filtering mechanism is developed to utilize visual semantic knowledge, thereby coarsely eliminating visually irrelevant experts for input queries during the inference stage of the post-edited model. Finally, to integrate visually relevant experts, we introduce a soft routing mechanism based on textual semantic relevance to achieve multi-expert fusion. For evaluation, we establish a benchmark for lifelong VLLM editing. Extensive experiments demonstrate that LiveEdit offers significant advantages in lifelong VLLM editing scenarios. Further experiments validate the rationality and effectiveness of each module design in LiveEdit. © 2024, CC BY.

关键词： Visual languages

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