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Multi-Agent Deep Reinforcement Learning With Trajectory Prediction for Task Migration-Assisted Computation Offloading

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5839-5856页

作者： Zhang, Xinyi Wang, Chunyang Zhu, Yanmin Cao, Jian Liu, Tong Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China East China Normal University School of Data Science and Engineering Shanghai200050 China Shanghai University School of Computer Engineering and Science Shanghai200444 China

Multi-access edge computing has become an effective paradigm to provide offloading services for computation-intensive and delay-sensitive tasks on vehicles. However, high mobility of vehicles usually incurs spatio-temporal load-imbalances among edge servers. Therefore, task migration is employed to maintain dynamic workload balancing by transmitting excessive tasks from overloaded to underloaded servers. Recent studies adopt deep reinforcement learning approaches to generate offloading and migration decisions based on current observations of systems. However, we argue that the migration direction is highly dependent on vehicular movements, and task migration towards the wrong direction could lead to additional delays. Therefore, we emphasize the importance of guiding task migration via exploring prospective trajectories of vehicles. We propose a Mobility-Aware Cooperative Multi-Agent (MCMA) deep reinforcement learning approach to make vehicle-by-vehicle decisions in multi-edge computation offloading scenarios. A two-stage decision framework is designed to solve the joint optimization problem of computation offloading and resource allocation. Additionally, an Informer-based multi-step vehicular trajectory prediction module is incorporated to enhance the capability of forecasting vehicular movements. Extensive experiments and analysis are conducted on synthetic and realistic scenarios, showing that our approach consistently outperforms both heuristic and DRL-based methods. The simulation scenarios and source codes are publicly available here. © 2002-2012 IEEE.

关键词： Computation offloading

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Faster Local Solvers for Graph Diffusion Equations 38

Faster Local Solvers for Graph Diffusion Equations

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bai, Jiahe Zhou, Baojian Yang, Deqing Xiao, Yanghua The School of Data Science Fudan University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

Efficient computation of graph diffusion equations (GDEs), such as Personalized PageRank, Katz centrality, and the Heat kernel, is crucial for clustering, training neural networks, and many other graph-related problems. Standard iterative methods require accessing the whole graph per iteration, making them time-consuming for large-scale graphs. While existing local solvers approximate diffusion vectors through heuristic local updates, they often operate sequentially and are typically designed for specific diffusion types, limiting their applicability. Given that diffusion vectors are highly localizable, as measured by the participation ratio, this paper introduces a novel framework for approximately solving GDEs using a local diffusion process. This framework reveals the suboptimality of existing local solvers. Furthermore, our approach effectively localizes standard iterative solvers by designing simple and provably sublinear time algorithms. These new local solvers are highly parallelizable, making them well-suited for implementation on GPUs. We demonstrate the effectiveness of our framework in quickly obtaining approximate diffusion vectors, achieving up to a hundred-fold speed improvement, and its applicability to large-scale dynamic graphs. Our framework could also facilitate more efficient local message-passing mechanisms for GNNs. © 2024 Neural information processing systems foundation. All rights reserved.

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A data representation method using distance correlation

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Frontiers of computer science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO Keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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Federated learning-outcome prediction with multi-layer privacy protection

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Frontiers of computer science 2024年第6期18卷 205-214页

作者： Yupei ZHANG Yuxin LI Yifei WANG Shuangshuang WEI Yunan XU Xuequn SHANG School of Computer Science Northwestern Polytechnical UniversityXi’an 710129China MIIT Lab of Big Data Storage and Management Xi’an 710129China

Learning-outcome prediction(LOP)is a long-standing and critical problem in educational *** studies have contributed to developing effective models while often suffering from data shortage and low generalization to various institutions due to the privacy-protection *** this end,this study proposes a distributed grade prediction model,dubbed FecMap,by exploiting the federated learning(FL)framework that preserves the private data of local clients and communicates with others through a global generalized *** considers local subspace learning(LSL),which explicitly learns the local features against the global features,and multi-layer privacy protection(MPP),which hierarchically protects the private features,including model-shareable features and not-allowably shared features,to achieve client-specific classifiers of high performance on LOP per *** is then achieved in an iteration manner with all datasets distributed on clients by training a local neural network composed of a global part,a local part,and a classification head in clients and averaging the global parts from clients on the *** evaluate the FecMap model,we collected three higher-educational datasets of student academic records from engineering *** results manifest that FecMap benefits from the proposed LSL and MPP and achieves steady performance on the task of LOP,compared with the state-of-the-art *** study makes a fresh attempt at the use of federated learning in the learning-analytical task,potentially paving the way to facilitating personalized education with privacy protection.

关键词： federated learning local subspace learning hierarchical privacy protection learning outcome prediction privacy-protected representation learning

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From Complex to Simple: Enhancing Multi-Constraint Complex Instruction Following Ability of Large Language Models

From Complex to Simple: Enhancing Multi-Constraint Complex I...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： He, Qianyu Zeng, Jie He, Qianxi Liang, Jiaqing Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Data Science Fudan University China

ISBN: (纸本)9798891761681

It is imperative for Large language models (LLMs) to follow instructions with elaborate requirements (i.e. Complex Instructions Following). Yet, it remains under-explored how to enhance the ability of LLMs to follow complex instructions with multiple constraints. To bridge the gap, we initially study what training data is effective in enhancing complex constraints following abilities. We found that training LLMs with instructions containing multiple constraints enhances their understanding of complex instructions, especially those with lower complexity levels. Additionally, we further propose methods addressing how to obtain and utilize the effective training data. Finally, we conduct extensive experiments to prove the effectiveness of our methods in terms of overall performance and training efficiency. We also demonstrate that our methods improve models' ability to follow instructions generally and generalize effectively across out-of-domain, in-domain, and adversarial settings, while maintaining general capabilities. The datasets and code are publicly available at https://***/meowpass/FollowComplexInstruction. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A novel ensemble causal feature selection approach with mutual information and group fusion strategy for multi-label data

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International Journal of Intelligent Computing and Cybernetics 2024年第4期17卷 671-704页

作者： Yifeng Zheng Xianlong Zeng Wenjie Zhang Baoya Wei Weishuo Ren Depeng Qing School of Computer Science Minnan Normal UniversityZhangzhouChina and Key Laboratory of Data Science and Intelligence ApplicationFujian Province UniversityZhangzhouChina

Purpose-As intelligent technology advances,practical applications often involve data with multiple ***,multi-label feature selection methods have attracted much attention to extract valuable ***,current methods tend to lack interpretability when evaluating the relationship between different types of variables without considering the potential causal ***/methodology/approach-To address the above problems,we propose an ensemble causal feature selection method based on mutual information and group fusion strategy(CMIFS)for multi-label ***,the causal relationship between labels and features is analyzed by local causal structure learning,respectively,to obtain a causal feature ***,we eliminate false positive features from the obtained feature set using mutual information to improve the feature subset ***,we employ a group fusion strategy to fuse the obtained feature subsets from multiple data sub-space to enhance the stability of the ***-Experimental comparisons are performed on six datasets to validate that our proposal can enhance the interpretation and robustness of the model compared with other methods in different ***,the statistical analyses further validate the effectiveness of our ***/value-The present study makes a noteworthy contribution to proposing a causal feature selection approach based on mutual information to obtain an approximate optimal feature subset for multilabel ***,our proposal adopts the group fusion strategy to guarantee the robustness of the obtained feature subset.

关键词： Multi-label learning Feature selection Causal relationship Mutual information Group fusion strategy

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Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases

Is There a One-Model-Fits-All Approach to Information Extrac...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Huang, Wenhao He, Qianyu Li, Zhixu Liang, Jiaqing Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Data Science Fudan University China

ISBN: (纸本)9798891761681

Definition bias is a negative phenomenon that can mislead models. Definition bias in information extraction appears not only across datasets from different domains but also within datasets sharing the same domain. We identify two types of definition bias in IE: bias among information extraction datasets and bias between information extraction datasets and instruction tuning datasets. To systematically investigate definition bias, we conduct three probing experiments to quantitatively analyze it and discover the limitations of unified information extraction and large language models in solving definition bias. To mitigate definition bias in information extraction, we propose a multi-stage framework consisting of definition bias measurement, bias-aware fine-tuning, and task-specific bias mitigation. Experimental results demonstrate the effectiveness of our framework in addressing definition bias. © 2024 Association for Computational Linguistics.

关键词： Information retrieval

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Recent metaheuristic algorithms for medical object localization using MSER detector in computer-aided diagnosis system

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Multimedia Tools and Applications 2025年第15期84卷 14433-14487页

作者： Ait Mehdi, Mohamed Belattar, Khadidja Souami, Feryel Faculty of Computer Science Data Science and Artificial Intelligence Department Laboratory for Research in Artificial Intelligence University of Science and Technology Houari Boumediene BP 32 El-Alia Algiers Bab-Ezzouar16111 Algeria Department of Computer Science University of Algiers Benyoucef Benkhedda Algiers16000 Algeria

Object localization is a critical task in image analysis, often facilitated by artificial intelligence techniques. While the Maximally Stable Extremal Regions (MSER) detection algorithm is a popular choice for local detection, its exhaustive approaches can be algorithmically complex and prone to suboptimal results with improper parameter selection. Various metaheuristic algorithms have been proposed for medical object localization to address this. In this context, four contributions are presented. Firstly, recent metaheuristics, including the Slime Mould Algorithm (SMA), Marine Predators Algorithm (MPA), Heap-based Optimizer (HBO), and Gradient-based Optimizer (GBO), are adapted to tackle the MSER localization problem. These algorithms are rigorously evaluated across diverse medical image datasets using multiple metrics, with their performance statistically validated through the Friedman mean rank test. The second contribution introduces a novel objective function to improve the localization process. The third contribution involves a comparative analysis of the recent algorithms against seven standard metaheuristics specifically designed for MSER localization. Lastly, we present an improved computer-aided diagnosis system that integrates an SVM-based model with Local Binary Patterns (LBP) descriptors extracted from each MSER alongside DenseNet-121 features. Experimental results demonstrate that the HBO and SMA optimizers outperform conventional algorithms, showing a higher diversity, improved exploitation and exploration capabilities, a well-balanced exploration-exploitation trade-off, elevated fitness values, faster convergence speeds, and enhanced computational efficiency. Additionally, we integrate these findings into an improved computer-aided diagnosis system, yielding classification results surpassing state-of-the-art models. These findings highlight the potential advantages of this approach, especially in object detection applications. © The Author(s), under

关键词： Object detection

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Physical information-enhanced graph neural network for predicting phase separation

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Chinese Physics B 2024年第7期33卷 278-283页

作者：张亚强王煦文王雅楠郑文 Institute of Public-Safety and Big Data College of Computer Science and Technology(College of Data Science)Taiyuan University of TechnologyJinzhong 030600China Shanxi Engineering Research Centre for Intelligent Data Assisted Treatment Changzhi Medical CollegeChangzhi 046000China Innovation Academy for Microsatellites of Chinese Academy of Sciences Shanghai 200050China

Although phase separation is a ubiquitous phenomenon, the interactions between multiple components make it difficult to accurately model and predict. In recent years, machine learning has been widely used in physics simulations. Here,we present a physical information-enhanced graph neural network(PIENet) to simulate and predict the evolution of phase separation. The accuracy of our model in predicting particle positions is improved by 40.3% and 51.77% compared with CNN and SVM respectively. Moreover, we design an order parameter based on local density to measure the evolution of phase separation and analyze the systematic changes with different repulsion coefficients and different Schmidt *** results demonstrate that our model can achieve long-term accurate predictions of order parameters without requiring complex handcrafted features. These results prove that graph neural networks can become new tools and methods for predicting the structure and properties of complex physical systems.

关键词： graph neural network phase separation machine learning dissipative particle dynamics

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Optimization of semi-supervised generative adversarial network models:a survey

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International Journal of Intelligent Computing and Cybernetics 2024年第4期17卷 705-736页

作者： Yongqing Ma Yifeng Zheng Wenjie Zhang Baoya Wei Ziqiong Lin Weiqiang Liu Zhehan Li School of Computer Science Minnan Normal UniversityZhangzhouChina and Key Laboratory of Data Science and Intelligence ApplicationFujian Province UniversityZhangzhouChina

Purpose-With the development of intelligent technology,deep learning has made significant progress and has been widely used in various *** learning is data-driven,and its training process requires a large amount of data to improve model ***,labeled data is expensive and not readily ***/methodology/approach-To address the above problem,researchers have integrated semisupervised and deep learning,using a limited number of labeled data and many unlabeled data to train *** this paper,Generative Adversarial Networks(GANs)are analyzed as an entry ***,we discuss the current research on GANs in image super-resolution applications,including supervised,unsupervised,and semi-supervised learning ***,based on semi-supervised learning,different optimization methods are introduced as an example of image ***,experimental comparisons and analyses of existing semi-supervised optimization methods based on GANs will be ***-Following the analysis of the selected studies,we summarize the problems that existed during the research process and propose future research ***/value-This paper reviews and analyzes research on generative adversarial networks for image super-resolution and classification from various learning *** comparative analysis of experimental results on current semi-supervised GAN optimizations is performed to provide a reference for further research.

关键词： Deep learning Generative adversarial network Semi-supervised learning Model optimization Image classification

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