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Multiuser Computation Offloading for Long-Term Sequential Tasks in Mobile Edge Computing Environments

Tsinghua Science and technology 2023年第1期28卷 93-104页

作者： Huanhuan Xu Jingya Zhou Wenqi Wei Baolei Cheng School of Computer Science and Technology Soochow UniversitySuzhou 215006China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou 215006China School of Computer Science Georgia Institute of TechnologyAtlantaGA 30332USA

Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network ***,current computation offloading studies in the heterogeneous edge environment face multifaceted challenges:Dependencies among computational tasks,resource competition among multiple users,and diverse long-term *** applications typically consist of several functionalities,and one huge category of the applications can be viewed as a series of sequential *** this study,we first proposed a novel multiuser computation offloading framework for long-term sequential ***,we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading ***,we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve *** further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.

关键词： mobile edge computing sequential tasks computation offloading dependency

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Fusion of data dimensionality reduction algorithms baced on category representation theory 4

Fusion of data dimensionality reduction algorithms baced on ...

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4th International Conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Xu, Xiaoxiang Li, Fanzhang Zhang, Li School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

ISBN: (纸本)9781510687615

For a long time, people have believed that representation problems are one of the bottlenecks in the field of machine learning. Therefore, it is a long-term and exploratory work to study machine learning representation methods. Due to this, we use category theory to study the fusion representation of data dimensionality reduction. We propose the basic concept of category representation for data dimensionality reduction and provide a data dimensionality reduction fusion representation framework. We have conducted research and analysis on algorithms such as PCA, KPCA, and LDA, identified the essential connection between PCA, KPCA, and LDA and proposed a data dimensionality reduction fusion representation algorithm based on a data dimensionality reduction fusion representation framework. Finally, we demonstrated the feasibility of the proposed method through experiments. © 2025 SPIE.

关键词： Dimensionality reduction

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Research Progress on Solar Flare Forecast Methods Based on Data-driven Models

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Research in Astronomy and Astrophysics 2023年第6期23卷 12-27页

作者： Ke Han Meng-Yao Yu Jun-Feng Fu Wen-Bin Ling De-quan Zheng Jie Wan Peng E School of Computer and Information Engineering Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information ProcessingHarbin University of CommerceHarbin 150028China Laboratory for Space Environment and Physical Sciences Harbin Institute of TechnologyHarbin 150001China

Eruption of solar flares is a complex nonlinear process,and the rays and high-energy particles generated by such an eruption are detrimental to the reliability of space-based or ground-based *** far,there are not reliable physical models to accurately account for the flare outburst mechanism,but a lot of data-driven models have been built to study a solar flare and forecast *** the paper,the status of solar-flare forecasting is reviewed,with emphasis on the machine learning methods and data-processing techniques used in the *** first,the essential forecast factors strongly relevant to solar flare outbursts,such as classification information of the sunspots and evolution pattern of the magnetic field,are reviewed and ***,methods of resampling for data preprocessing are introduced to solve the problems of class imbalance in the solar flare ***,typical model structures adopted for flare forecasting are reviewed from the aspects of the single and fusion models,and the forecast performances of the different models are ***,we herein summarize the current research on solar flare forecasting and outline its development trends.

关键词： Sun:activity Sun:flares (Sun:)sunspots Sun:magnetic fields

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FSA-Net:A Cost-efficient Face Swapping Attention Network with Occlusion-Aware Normalization

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Intelligent Automation & Soft Computing 2023年第7期37卷 971-983页

作者： Zhipeng Bin Huihuang Zhao Xiaoman Liang Wenli Chen College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002China

The main challenges in face swapping are the preservation and adaptive superimposition of attributes of two *** this study,the Face Swapping Attention Network(FSA-Net)is proposed to generate photoreal-istic face *** existing face-swapping methods ignore the blending attributes or mismatch the facial keypoint(cheek,mouth,eye,nose,etc.),which causes artifacts and makes the generated face silhouette *** address this problem,a novel reinforced multi-aware attention module,referred to as RMAA,is proposed for handling facial fusion and expression occlusion *** framework includes two *** the first stage,a novel attribute encoder is proposed to extract multiple levels of target face attributes and integrate identities and attributes when synthesizing swapped *** the second stage,a novel Stochastic Error Refinement(SRE)module is designed to solve the problem of facial occlusion,which is used to repair occlusion regions in a semi-supervised way without any *** proposed method is then compared with the current state-of-the-art *** obtained results demonstrate the qualitative and quantitative outperformance of the proposed *** details are provided at the footnote link and at https://***/view/fsa-net-official.

关键词： Attention face-swapping neural network face manipulation identity swap image translation

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Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

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Digital Communications and Networks 2023年第2期9卷 400-410页

作者： Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Department of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen UniversityXiamen361005China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University) Fuzhou350121China Faculty of Information Science and Engineering Ocean University of ChinaQingdao266100China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)JinanChina

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent *** mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all *** variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication *** data secure transmission is critical for mobile IIoT *** paper investigates the data secure transmission performance prediction of mobile IIoT *** cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first ***,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction *** mobile signals,the important features may be removed by the pooling *** will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is *** of the input and output layers,it removes the pooling layer and contains six convolution ***,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed *** simulation analysis,good prediction accuracy is achieved by the CNN *** prediction accuracy obtains a 59%increase.

关键词： Mobile IIoT networks Data secure transmission Performance analysis Intelligent prediction Improved CNN

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Learning Unbiased Rewards with Mutual information in Adversarial Imitation Learning 48

Learning Unbiased Rewards with Mutual Information in Adversa...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Zhang, Lihua Liu, Quan Huang, Zhigang Wu, Lan Soochow University School of Computer Science and Technology Suzhou China Soochow University Provincial Key Laboratory for Computer Information Processing Technology Suzhou China

ISBN: (纸本)9781728163277

A powerful method for automated decision systems is Adversarial Imitation Learning (AIL). It is based on a generative adversarial framework that alternately optimizes a generator (learner) and a discriminator (reward function). In the popular mind, a high-accuracy discriminator results in informative rewards, thus AIL must balance the performance between a generator and a discriminator. However, we find that the AIL reward function is biased and also results in informative rewards. Thus, we theoretically analyze the bias in the AIL reward function and find that balancing the performance of a generator and a discriminator is not necessary when we recover an unbiased reward function. Further, we propose a mutual information based auxiliary reward function. Experiments on continuous control tasks indicate that MI-GAIL is able to address the bias problem of the AIL reward function and further improve sample efficiency and training stability compared with up-to-date algorithms. © 2023 IEEE.

关键词： Adversarial imitation learning Continuous control Mutual information Potential-based reward function Reward bias

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A Perturbation-Based Policy Distillation Framework with Generative Adversarial Nets 48

A Perturbation-Based Policy Distillation Framework with Gene...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Zhang, Lihua Liu, Quan Zhang, Xiongzhen Xu, Yapeng Soochow University School of Computer Science and Technology Suzhou China Soochow University Provincial Key Laboratory for Computer Information Processing Technology Suzhou China

ISBN: (纸本)9781728163277

We study the problem of imitation learning in automated decision systems, in which a learner is trained to imitate an expert demonstrator. A widely used method is adversarial imitation learning that alternately optimizes a generator (learner) and a discriminator (reward function). However, the discriminator is biased during the initial and intermediate training stages. Consequently, the gradient descent direction of the learner is misguided, which leads to unstable training and sample complexity. In this paper, we propose deep imitation learning through a guidance-based policy distillation (GIL) algorithm. First, GIL proposes a teacher model, the guidance-based variational autoencoder, which is pre-trained with expert demonstrations. Then, GIL proposes a perturbation-based policy distillation method that uses the teacher model to guide the learner in the correct optimization direction, enabling the learner to imitate the expert policy with fewer detours. The experimental results show that our approach achieve higher sample efficiency compared with multiple baselines. © 2023 IEEE.

关键词： Distillation

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Unsigned Road Incidents Detection Using Improved RESNET From Driver-View Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

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EPI-Mamba: State Space Model for Semantic Segmentation from Light Fields

EPI-Mamba: State Space Model for Semantic Segmentation from ...

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

作者： Li, Yan Chen, Jianan Wang, Qiong Xu, Jinshan College of Computer Science & Technology Zhejiang University of Technology Hangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China

ISBN: (纸本)9798350368741

Global contextual dependency is of significance for semantic segmentation from light fields. However, previous works mostly exploit attention mechanisms to model spatial context dependency and angular context dependency separately, since a light field capture is very data-intensive. Considering that self-attention is resource-consuming, existing methods only use it to capture spatial contextual dependency for the central view of light fields, and employ resource-efficient attention mechanisms to model angular view dependencies instead. Inspired by the recent success of State Space Sequence model (SSM), we present a novel SSM-based encoder for light field semantic segmentation, which models spatio-angular contextual dependency together. The proposed method is evaluated on the existing light field semantic segmentation datasets, and experimental results show that the proposed method significantly outperforms previous state-of-the-art methods. The code and models used in this work are available in https://***/YanWQ/EPI-Mamba. © 2025 IEEE.

关键词： Light field semantic segmentation Mamba State space model

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An Optimization Method for Weight Computation in Hypergraph Transductive Learning Model 9

An Optimization Method for Weight Computation in Hypergraph ...

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9th International Conference on Advanced Cloud and Big Data, CBD 2021

作者： Yang, Zhe Xu, Liangkui Bu, Ziyu Chen, Yipeng Information Processing Technology Provincial Key Laboratory for Computer Soochow University School of Computer Science and Technology Suzhou China

ISBN: (纸本)9781665407458

Compared with the graph structure, the hyper-graph structure can convey more complex and multivariate relationships. In addition, hypergraphs are more capable of portraying and mining higher-order nonlinear correlations among objects. Therefore, it has been widely used in feature selection, clustering classification, and addressing other issues, and hypergraph learning has also become an important branch in the field of machine learning. Hypergraph learning, however, is a relatively high time consuming learning algorithm, because in its iterative solving process, there are lots of high-dimension matrix computations with large-scale parameters. This paper proposes an optimization of the weights, which is constructed by Gaussian kernel, in hyper-graph transductive learning model the weight matrix is replaced by fixed parameters when constructing the hypergraph laplacian matrix, which reduces the time consuming of weights computation in iterative process. Extensive experimental with semi-supervised node classification experiment results on the UCI and Kaggle datasets show that the optimized model reduces the time consuming of the hypergraph model, while ensuring classification accuracy. After model optimization, the four accuracy indicators fluctuate on average 3.03%, the time consuming is reduced by 28.99% on average, and the maximum saving is 42.84%. © 2022 IEEE.

关键词： Learning algorithms

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