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Combining neural network-based method with heuristic policy for optimal task scheduling in hierarchical edge cloud

Digital Communications and Networks 2023年第3期9卷 688-697页

作者： Zhuo Chen Peihong Wei Yan Li College of Computer Science and Engineering Chongqing University of TechnologyChongqing200433China School of Artificial Intelligence Chongqing University of TechnologyChongqing200433China

Deploying service nodes hierarchically at the edge of the network can effectively improve the service quality of offloaded task requests and increase the utilization of *** this paper,we study the task scheduling problem in the hierarchically deployed edge *** first formulate the minimization of the service time of scheduled tasks in edge cloud as a combinatorial optimization problem,blue and then prove the NP-hardness of the *** from the existing work that mostly designs heuristic approximation-based algorithms or policies to make scheduling decision,we propose a newly designed scheduling policy,named Joint Neural Network and Heuristic Scheduling(JNNHSP),which combines a neural network-based method with a heuristic based *** takes the Sequence-to-Sequence(Seq2Seq)model trained by Reinforcement Learning(RL)as the primary policy and adopts the heuristic algorithm as the auxiliary policy to obtain the scheduling solution,thereby achieving a good balance between the quality and the efficiency of the scheduling ***-depth experiments show that compared with a variety of related policies and optimization solvers,JNNHSP can achieve better performance in terms of scheduling error ratio,the degree to which the policy is affected by re-sources limitations,average service latency,and execution efficiency in a typical hierarchical edge cloud.

关键词： Edge cloud Task scheduling Neural network Reinforcement learning

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Power Switching Based on Trajectory Planning and Sliding Mode Control for Solid Oxide Fuel Cell Systems

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Journal of Modern Power Systems and Clean Energy 2024年第6期12卷 1968-1979页

作者： Zhen Wang Guoqiang Liu Xingbo Liu Jie Wang Zhiyang Jin Xiaowei Fu Zhuo Wang Bing Jin Zhonghua Deng Xi Li Key Laboratory of Imaging Processing and Intelligent Control of Ministry of Education School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina Research Institute of Huazhong University of Science and Technology in Shenzhen ShenzhenChina Hubei Huazhong Electric Power Technology Development Co. Ltd.WuhanChina School of Computer Science and Technology Wuhan University of Science and TechnologyWuhanChina School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhanChinaand he is also with the Wuhan Huamao Automation Co.Ltd.WuhanChina

To improve the safety of the solid oxide fuel cell(SOFC)systems and avoid the generation of large amounts of pollutants during power switching,this paper designs a power switching strategy based on trajectory planning and sliding mode control(TP-SMC).The design elements of the power switching strategy are proposed through simulation analysis at ***,based on the gas transmission delay time and the change of gas flow obtained from testing,trajectory planning(TP)is *** with other power switching strategies,it has been proven that the power switching strategy based on TP has significantly better control ***-thermore,considering the shortcomings and problems of TP in practical application,this paper introduces sliding mode control(SMC)on the basis of TP to improve the power switching *** final simulation results also prove that the TP-SMC can effectively suppress the impact of uncertainty in gas flow and gas transmission delay *** with TP,TP-SMC can ensure that under uncertain conditions,the SOFC system does not experience fuel starvation and temperature exceeding limit during power ***,the NOx emissions are also within the normal and acceptable *** paper can guide the power switching process of the actual SOFC sys-tems to avoid safety issues and excessive generation of NOx,which is very helpful for improving the performance and ser-vice lifeof the SOFCsystems.

关键词： Solid oxide fuel cell(SOFC)system power switching safety NOx emission trajectory planning sliding mode control

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Domain-invariant feature learning with label information integration for cross-domain classification

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Neural Computing and Applications 2024年第21期36卷 13107-13126页

作者： Jiang, Lin Wu, Jigang Zhao, Shuping Li, Jiaxing The School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Institute of Artificial Intelligence Guangzhou University Guangzhou510006 China

Traditional methods for unsupervised cross-domain classification learn a common low-dimensional subspace using images from a well-labeled source domain and an unlabeled target domain. To achieve domain-invariant feature extraction, researchers typically focus on minimizing the distribution discrepancy. However, these methods often overlook the fact that label information contains critical categorization information for discriminative subspace learning. This paper proposes a novel method, named domain-invariant feature learning with label information integration (DILI), which integrates metric learning and label information extraction to learn a cross-domain discriminant subspace. DILI first reduces the distances between the source and target domains to mitigate the marginal distribution discrepancy. Then, it reduces the distances between cross-domain samples from the same class to mitigate the conditional distribution discrepancy. Dual terms are imposed to balance the label information of both domains to learn common features, and a discriminant subspace is learned for cross-domain tasks. This method obtains domain-invariant features, while the label information of the source domain and pseudo labels from the target domain are used to improve the discriminant of subspaces. Experimental results on eight cross-domain datasets show that DILI outperforms some state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Classification (of information)

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A comprehensive survey of federated transfer learning:challenges,methods and applications

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Frontiers of computer science 2024年第6期18卷 27-60页

作者： Wei GUO Fuzhen ZHUANG Xiao ZHANG Yiqi TONG Jin DONG Institute of Artificial Intelligence Beihang UniversityBeijing 100191China SKLSDE School of Computer ScienceBeihang UniversityBeijing 100191China School of Computer Science and Technology Shandong UniversityShandong 266237China Beijing Academy of Blockchain and Edge Computing Beijing 100080China

Federated learning(FL)is a novel distributed machine learning paradigm that enables participants to collaboratively train a centralized model with privacy preservation by eliminating the requirement of data *** practice,FL often involves multiple participants and requires the third party to aggregate global information to guide the update of the target ***,many FL methods do not work well due to the training and test data of each participant may not be sampled from the same feature space and the same underlying ***,the differences in their local devices(system heterogeneity),the continuous influx of online data(incremental data),and labeled data scarcity may further influence the performance of these *** solve this problem,federated transfer learning(FTL),which integrates transfer learning(TL)into FL,has attracted the attention of numerous ***,since FL enables a continuous share of knowledge among participants with each communication round while not allowing local data to be accessed by other participants,FTL faces many unique challenges that are not present in *** this survey,we focus on categorizing and reviewing the current progress on federated transfer learning,and outlining corresponding solutions and ***,the common setting of FTL scenarios,available datasets,and significant related research are summarized in this survey.

关键词： federated transfer learning federated learning transfer learning survey

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Identifying Counterexamples Without Variability in Software Product Line Model Checking

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computers, Materials & Continua 2023年第5期75卷 2655-2670页

作者： Ling Ding Hongyan Wan Luokai Hu Yu Chen School of Computer Science Hubei University of EducationWuhan430205China School of Computer Science and Artificial Intelligence Wuhan Textile UniversityWuhan430200China

Product detection based on state abstraction technologies in the software product line(SPL)is more complex when compared to a single *** variability constitutes a new complexity,and the counterexample may be valid for some products but spurious for *** this paper,we found that spurious products are primarily due to the failure states,which correspond to the spurious *** violated products correspond to the real ***,identifying counterexamples is a critical problem in detecting violated *** our approach,we obtain the violated products through the genuine counterexamples,which have no failure state,to avoid the tedious computation of identifying spurious products dealt with by the existing *** can be executed in parallel to improve the efficiency *** results showthat our approach performswell,varying with the growth of the system *** analyzing counterexamples in the abstract model,we observed that spurious products occur in the failure *** approach helps in identifying whether a counterexample is spurious or *** approach also helps to check whether a failure state exists in the *** performance evaluation shows that the proposed approach helps significantly in improving the efficiency of abstraction-based SPL model checking.

关键词： Software product line model checking parallel algorithm

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Residual diverse ensemble for long-tailed multi-label text classification

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science China(Information sciences) 2024年第11期67卷 92-105页

作者： Jiangxin SHI Tong WEI Yufeng LI National Key Laboratory for Novel Software Technology Nanjing University School of Artificial Intelligence Nanjing University School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of Education

Long-tailed multi-label text classification aims to identify a subset of relevant labels from a large candidate label set, where the training datasets usually follow long-tailed label distributions. Many of the previous studies have treated head and tail labels equally, resulting in unsatisfactory performance for identifying tail labels. To address this issue, this paper proposes a novel learning method that combines arbitrary models with two steps. The first step is the “diverse ensemble” that encourages diverse predictions among multiple shallow classifiers, particularly on tail labels, and can improve the generalization of tail *** second is the “error correction” that takes advantage of accurate predictions on head labels by the base model and approximates its residual errors for tail labels. Thus, it enables the “diverse ensemble” to focus on optimizing the tail label performance. This overall procedure is called residual diverse ensemble(RDE). RDE is implemented via a single-hidden-layer perceptron and can be used for scaling up to hundreds of thousands of labels. We empirically show that RDE consistently improves many existing models with considerable performance gains on benchmark datasets, especially with respect to the propensity-scored evaluation ***, RDE converges in less than 30 training epochs without increasing the computational overhead.

关键词： multi-label learning extreme multi-label learning long-tailed distribution multi-label text classification ensemble learning

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DAUNet: Detail-Aware U-Shaped Network for 2D Human Pose Estimation

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computers, Materials & Continua 2024年第11期81卷 3325-3349页

作者： Xi Li Yuxin Li Zhenhua Xiao Zhenghua Huang Lianying Zou College of Information and Artificial Intelligence Nanchang Institute of Science and TechnologyNanchang330108China School of Electrical and Information Engineering Wuhan Institute of TechnologyWuhan430205China School of Computer Science and Technology Hubei Business CollegeWuhan430079China

Human pose estimation is a critical research area in the field of computer vision,playing a significant role in applications such as human-computer interaction,behavior analysis,and action *** this paper,we propose a U-shaped keypoint detection network(DAUNet)based on an improved ResNet subsampling structure and spatial grouping *** network addresses key challenges in traditional methods,such as information loss,large network redundancy,and insufficient sensitivity to low-resolution *** is composed of three main ***,we introduce an improved BottleNeck block that employs partial convolution and strip pooling to reduce computational load and mitigate feature ***,after upsampling,the network eliminates redundant features,improving the overall ***,a lightweight spatial grouping attention mechanism is applied to enhance low-resolution semantic features within the feature map,allowing for better restoration of the original image size and higher *** results demonstrate that DAUNet achieves superior accuracy compared to most existing keypoint detection models,with a mean PCKh@0.5 score of 91.6%on the MPII dataset and an AP of 76.1%on the COCO ***,real-world experiments further validate the robustness and generalizability of DAUNet for detecting human bodies in unknown environments,highlighting its potential for broader applications.

关键词： Human pose estimation keypoint detection U-shaped network architecture spatial grouping mechanism

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ControlVideo: conditional control for one-shot text-driven video editing and beyond

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science China(Information sciences) 2025年第3期68卷 150-162页

作者： Min ZHAO Rongzhen WANG Fan BAO Chongxuan LI Jun ZHU Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML CenterTsinghua Laboratory of Brain and Intelligence Lab Tsinghua University ShengShu Technology Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Pazhou Laboratory (Huangpu)

This paper presents ControlVideo for text-driven video editing — generating a video that aligns with a given text while preserving the structure of the source video. Building on a pre-trained text-to-image diffusion model, ControlVideo enhances the fidelity and temporal consistency by incorporating additional conditions(such as edge maps), and fine-tuning the key-frame and temporal attention on the source video-text pair via an in-depth exploration of the design space. Extensive experimental results demonstrate that ControlVideo outperforms various competitive baselines by delivering videos that exhibit high fidelity w.r.t. the source content, and temporal consistency, all while aligning with the text. By incorporating low-rank adaptation layers into the model before training, ControlVideo is further empowered to generate videos that align seamlessly with reference images. More importantly, ControlVideo can be readily extended to the more challenging task of long video editing(e.g., with hundreds of frames), where maintaining long-range temporal consistency is crucial. To achieve this, we propose to construct a fused ControlVideo by applying basic ControlVideo to overlapping short video segments and key frame videos and then merging them by pre-defined weight functions. Empirical results validate its capability to create videos across 140 frames, which is approximately 5.83 to 17.5 times more than what previous studies achieved. The code is available at https://***/thu-ml/controlvideo.

关键词： diffusion models controllable generation text-driven editing video editing long video editing

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Integer wavelet transform-based secret image sharing using rook polynomial and hamming code with authentication

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CAAI Transactions on intelligence technology 2024年第6期9卷 1435-1450页

作者： Sara Charoghchi Zahra Saeidi Samaneh Mashhadi Cryptography and Data Security Laboratory School of Mathematics and Computer ScienceIran University of Science&TechnologyTehranIran

As an effective way to securely transfer secret images,secret image sharing(SIS)has been a noteworthy area of *** in a SIS scheme,a secret image is shared via shadows and could be reconstructed by having the required number of them.A major downside of this method is its noise-like shadows,which draw the malicious users'*** order to overcome this problem,SIS schemes with meaningful shadows are introduced in which the shadows are first hidden in innocent-looking cover images and then *** most of these schemes,the cover image cannot be recovered without distortion,which makes them useless in case of utilising critical cover images such as military or medical ***,embedding the secret data in Least significant bits of the cover image,in many of these schemes,makes them very fragile to steganlysis.A reversible IWT-based SIS scheme using Rook polynomial and Hamming code with authentication is *** order to make the scheme robust to steganalysis,the shadow image is embedded in coefficients of Integer wavelet transform of the cover *** Rook polynomial makes the scheme more secure and moreover makes authentication very easy and with no need to share private key to ***,utilising Hamming code lets us embed data with much less required modifications on the cover image which results in high-quality stego images.

关键词： hamming code homogeneous linear recursion IWT rook polynomial (t n)-secret image sharing

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The Application of Adversarial Training Based on Gradient Constraint Optimization Method to Sentiment Analysis

Journal of Network Intelligence

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Journal of Network intelligence 2024年第1期9卷 587-598页

作者： Xie, Zhichun Liu, Jianhua Hu, Renyuan Wang, Jiacan Wang, Xiaofeng School of Data and Computer Science Xiamen Institute of Technology Xiamen361021 China School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China

The previous adversarial training models failed to pay attention to the influence of the changing gradient of the loss function in the current training on the model. The perturbation injected into the model is only processed by standardization and constraints. This paper proposes a two-way encoding of a converter based on a fast gradient descent of a constraint strategy. The device characterizes the technical model and applies it to sentiment analysis tasks. The model can adapt to gradient changes, and can improve the model’s convergence ability in the early stage of training. In the later stage of training, the perturbation can be appropriately increased according to the gradient to improve the robustness of the model. The experimental results show that even in the expectations of different languages, the classification accuracy of the model is significantly improved compared with the previous transfer learning models and adversarial training. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Sentiment analysis

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