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A Real-Time Lightweight Perceptron for Cloud–Edge Collaborative Predictive Maintenance of Online Service Systems

IEEE Internet of Things Journal 2025年第9期12卷 12640-12657页

作者： Zhang, Linzi Shi, Yong Wang, Donghan Communication University of China School of Economics and Management Beijing100024 China Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science Key Laboratory of Big Data Mining and Knowledge Management Beijing100190 China

In the era of expanding cloud–edge online service systems, predictive maintenance (PdM) based on key performance indicators (KPIs), such as CPU utilization, response rate, and network bandwidth, is essential for system operational reliability and security. Traditional data-driven PdM approaches for cloud–edge services often monitor individual indicators in isolation, neglecting their interrelationships. Although convolutional modules have been utilized for collaborative representation learning of localized high-dimensional KPIs, the high-computational complexity of CNNs and the limited kernel size hinder their efficacy in modeling high-dimensional, time-sensitive KPIs. To address these challenges, we propose a lightweight multichannel multilayer perceptron (MCMLP) framework for collaborative PdM. This framework enables efficient pointwise manipulation to capture inner-indicator hidden patterns within KPIs sequences without relying on grid-based convolution. Our MCMLP enhances locality through a two-stage dual-channel feature extraction process that captures spatial and temporal dependencies, integrating inner-indicator and cross-timeline features. The proposed MCMLP can also be easily plugged into existing CNN-based detectors as a substitute, offering a computationally economical alternative. Empirical results in real-world KPI data from various cloud–edge service scenarios demonstrate that MCMLP significantly outperforms traditional CNN-based methods, with a roughly 22% reduction in training time at a 95% confidence level (α = 0.05 ). This study also provides actionable insights for deploying MCMLP in cloud–edge service systems. © 2014 IEEE.

关键词： Predictive maintenance

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Digitizing Highways:Unleashing Potential Through data Element Circulation for Safety,Cost Efficiency,and Environmental Impact Optimization

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Journal of Systems Science and Information 2025年第1期13卷 23-60页

作者： Yanyan JIA Wen LONG Yingjie TIAN School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China

As the era of large-scale highway maintenance arrives,the maintenance strategies have transitioned to a holistic approach that prioritizes safety,economic feasibility,and environmental *** research introduces a multi-objective optimization model for highway maintenance that incorporates the interplay of decision-maker preferences across three key objectives:Highway safety performance,maintenance engineering cost,and carbon *** study employs a large-sample data analysis on a subset of the Lianhuo Highway network,which includes 2,842 pavement *** approach mitigates the impact of outliers,ensuring a substantial data buffer that fortifies the model’s capacity for generalization and bolsters its *** findings reveal a Pareto-optimal relationship among the three scrutinized variables.A particularly noteworthy observation is the M-shaped trajectory of carbon emissions,which initially rise,then decline,and ultimately rebound,contingent upon the selected maintenance ***,an examination of the relationship between maintenance costs and safety performance discloses a trend of diminishing marginal returns,illustrating that the incremental gains in safety performance attenuate as maintenance investment escalates.

关键词： data element circulation highway maintenance multi-objective optimization

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Subpart Suppression Network for Few-Shot Object Counting

Subpart Suppression Network for Few-Shot Object Counting

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

作者： Liu, Lanxin Liu, Xinyan Li, Guorong School of Computer Science and Technology Key Lab of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China School of Computer Science and Technology Harbin Institute of Technology Weihai China

ISBN: (纸本)9798350368741

Few-shot object counting and detection aim to count objects along with their bounding boxes specified by exemplar bounding boxes. Current mainstream methods predict density maps by applying similarity between exemplar and image features to get counting results and detecting peak points from the density map as the positions of objects. However, the sub-parts of objects can also have high similarity to the exemplars, harming the counting and detection performance. To address these issues, we propose a two-stage Subpart Suppression Network (SSN), consisting of a Subpart Suppression Density map Predictor (SSDP), which enforces the model focus on whole objects rather than subparts, and a SAM-based Detector and Verifier, which refines the final prediction by a clustering method. Extensive experiments on two popular datasets show an advantage in performance over the state-of-the-art methods and prove the components' effectiveness. © 2025 IEEE.

关键词： Counting Detection Few-Shot Localization

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Subpart Suppression Network for Few-Shot Object Counting

Subpart Suppression Network for Few-Shot Object Counting

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lanxin Liu Xinyan Liu Guorong Li School of Computer Science and Technology Key Lab of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences School of Computer Science and Technology Harbin Institute of Technology (Weihai)

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Accuracy Filtering Convolution Clustering methods Detectors Predictive models Feature extraction Acoustics Speech processing Kernel

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Less is More: Token Context-aware Learning for Object Tracking

arXiv

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arXiv 2025年

作者： Xu, Chenlong Zhong, Bineng Liang, Qihua Zheng, Yaozong Li, Guorong Song, Shuxiang Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

Recently, several studies have shown that utilizing contextual information to perceive target states is crucial for object tracking. They typically capture context by incorporating multiple video frames. However, these naive frame-context methods fail to consider the importance of each patch within a reference frame, making them susceptible to noise and redundant tokens, which deteriorates tracking performance. To address this challenge, we propose a new token context-aware tracking pipeline named LMTrack, designed to automatically learn high-quality reference tokens for efficient visual tracking. Embracing the principle of Less is More, the core idea of LMTrack is to analyze the importance distribution of all reference tokens, where important tokens are collected, continually attended to, and updated. Specifically, a novel Token Context Memory module is designed to dynamically collect high-quality spatio-temporal information of a target in an autoregressive manner, eliminating redundant background tokens from the reference frames. Furthermore, an effective Unidirectional Token Attention mechanism is designed to establish dependencies between reference tokens and search frame, enabling robust cross-frame association and target localization. Extensive experiments demonstrate the superiority of our tracker, achieving state-of-the-art results on tracking benchmarks such as GOT-10K, TrackingNet, and LaSOT. Code and models are available at https://***/XuChenLong/LMTrack. Copyright © 2025, The Authors. All rights reserved.

关键词： Object detection

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Information content and sentiment: the role of environmental disclosure in stock price crash risk

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International Review of Financial Analysis 2025年 104卷

作者： Long, Wen Ma, Ruiqi Guo, Man School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Sino-Danish College University of Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Environmental disclosure is a topic of global importance. When negative environmental news emerges and is eventually revealed, it can influence investor decisions and lead to significant stock price movements, potentially resulting in stock price crashes. Therefore, using a sample of A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2017 to 2022, we construct an environmental sentiment dictionary, establish an environmental disclosure content index system, and explore the influence mechanism of environmental disclosure on stock price crash risk. Findings show that increased environmental disclosure sentiment intensity can boost stock price crash risk, whereas greater environmental disclosure content can significantly reduce it. Furthermore, we identify a significant threshold effect related to the sentiment intensity of environmental disclosure. Notably, in environmental disclosure, sentiment tends to have a stronger impact than content. Mechanism analysis reveals that environmental disclosure content is influenced by external disclosure sources, the tone of internal annual reports, and internal control mechanisms. Meanwhile, environmental disclosure sentiment intensity is driven by earnings management and investor risk perception. This study develops a more comprehensive method for measuring environmental information disclosure and offers important insights for improving environmental management among listed companies. © 2025

关键词： Environmental information disclosure Sentiment analysis Stock price crash risk

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Sample selection for noisy partial label learning with interactive contrastive learning

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Pattern Recognition 2025年 166卷

作者： Yu, Xiaotong Sun, Shiding Tian, Yingjie School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China China Aerospace Science & Industry Corp Defense Technology R&T Center Beijing100854 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS Beijing100190 China China Unicom Research Institute Beijing100176 China

In the context of weakly supervised learning, partial label learning (PLL) addresses situations where each training instance is associated with a set of partial labels, with only one being accurate. However, in complex realworld tasks, the restrictive assumption may be invalid which means the ground-truth may be outside the candidate label set. In this work, we loose the constraints and address the noisy label problem for PLL. First, we introduce a selection strategy, which enables deep models to select clean samples via the loss values of flipped and original images. Besides, we progressively identify the true labels of the selected samples and ensemble two models to acquire the knowledge of unselected samples. To extract better feature representations, we introduce pseudo-labeled interactive contrastive learning to aggregate cross-network information of all samples. Experimental results verify that our approach surpasses baseline methods on noisy PLL task with different levels of label noise. © 2025 Elsevier Ltd

关键词： Contrastive Learning

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Detecting Difference Between Coefficients in Linear Model Using Jackknife Empirical Likelihood

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Journal of Systems Science & Complexity 2016年第2期29卷 542-556页

作者： WU Xinqi ZHANG Qingzhao ZHANG Sanguo School of Mathematical Sciences University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences

Empirical likelihood has been found very useful in many different occasions. It usually runs into serious computational difficulties while jackknife empirical likelihood （JEL） is shown to be effective when applied to some complicated statistics. In this paper, to test the difference between coefficients of two linear regression models, the authors apply JEL to construct the confidence regions. Based on the 3EL ratio test, a version of Wilks＇ theorem is developed. Furthermore, to improve the coverage accuracy of confidence regions, a Bartlett correction is applied. Simulation studies are carried out to show the effectiveness of the proposed method in aspects of coverage accuracy. A real data set is analyzed with the proposed method as an example.

关键词： Bartlett correction coverage accuracy Jackknife empirical likelihood linear regression model.

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Empirical Likelihood Test for Regression Coefficients in High Dimensional Partially Linear Models

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Journal of Systems Science & Complexity 2021年第3期34卷 1135-1155页

作者： LIU Yan REN Mingyang ZHANG Sanguo School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China

This paper considers tests for regression coefficients in high dimensional partially linear *** authors first use the B-spline method to estimate the unknown smooth function so that it could be linearly ***,the authors propose an empirical likelihood method to test regression *** authors derive the asymptotic chi-squared distribution with two degrees of freedom of the proposed test statistics under the null *** addition,the method is extended to test with nuisance *** show that the proposed method have a good performance in control of type-I error rate and *** proposed method is also employed to analyze a data of Skin Cutaneous Melanoma(SKCM).

关键词： Empirical likelihood test high dimensional analysis partially linear models regression coefficients

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New Discoveries of Domination Between Traffic Matrices

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Acta Mathematicae Applicatae Sinica 2017年第3期33卷 561-566页

作者： Peng-fei LIU Wen-guo YANG Tian-de GUO School of Mathematics Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100049China

In this paper the definition of domination is generalized to the case that the elements of the traffic matrices may have negative values. It is proved that D3 dominates D3 ＋ λ（D2 - D1） for any λ ≥0 if D1 dominates D2. Let u（D） be the set of all the traffic matrices that are dominated by the traffic matrix D. It is shown that u （ D∞） and u （D ∈） are isomorphic. Besides, similar results are obtained on multi-commodity flow problems. Fhrthermore, the results are the generalized to integral flows.

关键词： robust network design multi-commodity flows domination

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