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Scheduling optimization for upstream dataflows in edge computing

Digital Communications and Networks 2023年第6期9卷 1448-1457页

作者： Haohao Wang Mengmeng Sun Lianming Zhang Pingping Dong Yehua Wei Jing Mei College of Information Science and Engineering Hunan Normal UniversityChangsha410081China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal UniversityChangsha410081China Hunan Xiangjiang Artificial Intelligence Academy Hunan Normal UniversityChangsha410081China

Edge computing can alleviate the problem of insufficient computational resources for the user equipment,improve the network processing environment,and promote the user *** computing is well known as a prospective method for the development of the Internet of Things(IoT).However,with the development of smart terminals,much more time is required for scheduling the terminal high-intensity upstream dataflow in the edge server than for scheduling that in the downstream *** this paper,we study the scheduling strategy for upstream dataflows in edge computing networks and introduce a three-tier edge computing network *** propose a Time-Slicing Self-Adaptive Scheduling(TSAS)algorithm based on the hierarchical queue,which can reduce the queuing delay of the dataflow,improve the timeliness of dataflow processing and achieve an efficient and reasonable performance of dataflow *** experimental results show that the TSAS algorithm can reduce latency,minimize energy consumption,and increase system throughput.

关键词： Edge computing Time-slicing Dataflow scheduling Dynamic analysis

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The blessing of Depth Anything:An almost unsupervised approach to crop segmentation with depth-informed pseudo labeling

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植物表型组学（英文） 2025年第1期7卷 49-65页

作者： Songliang Cao Binghui Xu Wei Zhou Letian Zhou Jiafei Zhang Yuhui Zheng Weijuan Hu Zhiguo Han Hao Lu National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan430074China PhenoTrait Technology Co. Ltd.Beijing100096China MetaPheno Laboratory Shanghai201114China National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan430074China PhenoTrait Technology Co. Ltd.Beijing100096China MetaPheno Laboratory Shanghai201114China SpeCloud Technology Co. Ltd.Sanya572025China State Key Laboratory of Plant Cell and Chromosome Engineering Institute of Genetics and Developmental BiologyChinese Academy of SciencesBeijing100101China

We present Depth-Informed Crop Segmentation(DepthCropSeg),an almost unsupervised crop segmentation approach without manual pixel-level *** segmentation is a fundamental vision task in agriculture,which benefits a number of downstream applications such as crop growth monitoring and yield *** the past decade,image-based crop segmentation approaches have shifted from classic color-based paradigms to recent deep learning-based *** latter,however,rely heavily on large amounts of data with high-quality manual annotation such that considerable human labor and time are *** this work,we leverage Depth Anything V2,a vision foundation model,to produce high-quality pseudo crop masks for training segmentation *** compile a dataset of 17,199 images from six public plant segmentation sources,generating pseudo masks from depth maps after normalization and *** a coarse-to-fine manual screening,1378 images with reliable masks are *** compare four semantic segmentation models and enhance the top-performing one with depth-informed two-stage self-training and depth-informed *** evaluate the feasibility and robustness of DepthCropSeg,we benchmark the segmentation performance on 10 public crop segmentation testing sets and a self-collect dataset covering in-field,laboratory,and unmanned aerial vehicle(UAV)*** results show that our DepthCropSeg approach can achieve crop segmentation performance comparable to the fully supervised model trained with manually annotated data(86.91 vs.87.10).For the first time,we demonstrate almost unsupervised,close-to-full-supervision crop segmentation successfully.

关键词： Crop segmentation Plant phenotyping Depth anything Segment anything Efficient labeling

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An Update Method of Decision Implication Canonical Basis on Attribute Granulating

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Intelligent Automation & Soft computing 2023年第8期37卷 1833-1851页

作者： Yanhui Zhai Rujie Chen Deyu Li School of Computer and Information Technology Shanxi UniversityTaiyuanShanxi Province030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuanShanxi Province030006China

Decision implication is a form of decision knowledge represen-tation,which is able to avoid generating attribute implications that occur between condition attributes and between decision *** with other forms of decision knowledge representation,decision implication has a stronger knowledge representation *** granularization may facilitate the knowledge extraction of different attribute granularity layers and thus is of application *** implication canonical basis(DICB)is the most compact set of decision implications,which can efficiently represent all knowledge in the decision *** order to mine all deci-sion information on decision context under attribute granulating,this paper proposes an updated method of *** this end,the paper reduces the update of DICB to the updates of decision premises after deleting an attribute and after adding granulation attributes of some *** on this,the paper analyzes the changes of decision premises,examines the properties of decision premises,designs an algorithm for incrementally generating DICB,and verifies its effectiveness through *** real life,by using the updated algorithm of DICB,users may obtain all decision knowledge on decision context after attribute granularization.

关键词： Decision context attribute granulating decision implication decision implication canonical basis

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GeneCompass:deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model

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细胞研究（英文版） 2024年第12期34卷 830-845页

作者： Xiaodong Yang Guole Liu Guihai Feng Dechao Bu Pengfei Wang Jie Jiang Shubai Chen Qinmeng Yang Hefan Miao Yiyang Zhang Zhenpeng Man Zhongming Liang Zichen Wang Yaning Li Zheng Li Yana Liu Yao Tian Wenhao Liu Cong Li Ao Li Jingxi Dong Zhilong Hu Chen Fang Lina Cui Zixu Deng Haiping Jiang Wentao Cui Jiahao Zhang Zhaohui Yang Handong Li Xingjian He Liqun Zhong Jiaheng Zhou Zijian Wang Qingqing Long Ping Xu The X-Compass Consortiudm Hongmei Wang Zhen Meng Xuezhi Wang Yangang Wang Yong Wang Shihua Zhang Jingtao Guo Yi Zhao Yuanchun Zhou Fei Li Jing Liu Yiqiang Chen Ge Yang Xin Li Baoyang Hu Wei Li Fei Gao Leqian Yu Qi Gu Weiwei Zhai Zhengting Zou Jingqi Yu Wenhui Wu Xinxin Lin Yu Zou Yongshun Ren Fan Li Yixiao Zhao Yike Xin Longfei Han Shuyang Jiang Kai Ma Qicheng Chen Haoyuan Wang Huanhuan Wu Chaofan He Yilong Hu Shuyu Guo Yiyun Li Zaitian Wang Huimin He Shan Zong Jiajia Wang Yan Chen Chunyang Zhang Chengrui Wang Ran Zhang Meng Xiao Yining Wang Xin Qin Jiaxin Qin Chenhao Li Zhufeng Xu Zeyuan Zhang Xiaoning Qi Wuliang Huang Yaoru Luo Qinxuan Luo Ziwen Liu Teng Wang Yiming Huang Shirui Li Kangning Dong Qunlun Shen State Key Laboratory of Stem Cell and Reproductive Biology Institute of ZoologyChinese Academy of SciencesBeijingChina Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing TechnologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Artificial Intelligence University of Chinese Academy of SciencesBeijingChina State Key Laboratory of Stem Cell and Reproductive Biology Institute of ZoologyChinese Academy of SciencesBeijingChina Institute for Stem Cell and Regenerative Medicine Chinese Academy of SciencesBeijingChina Beijing Institute for Stem Cell and Regenerative Medicine BeijingChina Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing TechnologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina Research Center for Ubiquitous Computing Systems Institute of Computing TechnologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina Computer Network Information Center Chinese Academy of SciencesBeijingChina Institute of Automation Chinese Academy of SciencesBeijingChina Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing TechnologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina Computer Network Information Center Chinese Academy of SciencesBeijingChina State Key Laboratory of Stem Cell and Reproductive Biology Institute of ZoologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina University of Chinese Academy of Sciences BeijingChina CEMS NCMISHCMSMDISRCSDSAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina State Key Laboratory of Stem Cell and Reproductive Biology Institute of Zoology

Deciphering universal gene regulatory mechanisms in diverse organisms holds great potential for advancing our knowledge of fundamental life processes and facilitating clinical ***,the traditional research paradigm primarily focuses on individual model organisms and does not integrate various cell types across *** breakthroughs in single-cell sequencing and deep learning techniques present an unprecedented opportunity to address this *** this study,we built an extensive dataset of over 120 million human and mouse single-cell *** data preprocessing,we obtained 101,768,420 single-cell transcriptomes and developed a knowledge-informed cross-species foundation model,named *** pre-training,GeneCompass effectively integrated four types of prior biological knowledge to enhance our understanding of gene regulatory mechanisms in a self-supervised *** fine-tuning for multiple downstream tasks,GeneCompass outperformed state-of-the-art models in diverse applications for a single species and unlocked new realms of cross-species biological *** also employed GeneCompass to search for key factors associated with cell fate transition and showed that the predicted candidate genes could successfully induce the differentiation of human embryonic stem cells into the gonadal ***,GeneCompass demonstrates the advantages of using artificial intelligence technology to decipher universal gene regulatory mechanisms and shows tremendous potential for accelerating the discovery of critical cell fate regulators and candidate drug targets.

关键词： knowledge model with cross-species deciphering foundation genecompass informed mechanisms regulatory

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Incomplete multi-view clustering via local and global co-regularization

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Science China(information Sciences) 2022年第5期65卷 96-111页

作者： Jiye LIANG Xiaolin LIU Liang BAI Fuyuan CAO Dianhui WANG Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Artificial Intelligence Research Institute China University of Mining and Technology Department of Computer Science and Computer Engineering La Trobe University

The incompleteness of multi-view data is a phenomenon associated with real-world data mining applications, which brings a huge challenge for multi-view clustering. Although various types of clustering methods, which try to obtain a complete and consensus clustering result from a latent subspace, have been developed to overcome this problem, most methods excessively rely on views-public instances to bridge the connection with view-private instances. When lacking sufficient views-public instances, existing methods fail to transmit the information among incomplete views effectively. To overcome this limitation, we propose an incomplete multi-view clustering algorithm via local and global co-regularization(IMVC-LG). In this algorithm, we define a new objective function that is composed of two terms: local clustering from each view and global clustering from multiple views, which constrain each other to exploit the local clustering information from different incomplete views and determine a global consensus clustering result, ***, an iterative optimization method is proposed to minimize the objective function. Finally, we compare the proposed algorithm with other state-of-the-art incomplete multi-view clustering methods on several benchmark datasets to illustrate its effectiveness.

关键词： incompleteness multi-view clustering local clustering global clustering co-regularization

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Relaxing Distillation Constraints for Improved New Class Learning in Continual Semantic Segmentation

Relaxing Distillation Constraints for Improved New Class Lea...

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

作者： Ti, Zheng Hao, Xuze Chen, Renhai College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9798350368741

Continual Semantic Segmentation (CSS) aims to continuously learn new classes while mitigating catastrophic forgetting. Existing CSS methods primarily address this challenge through knowledge distillation. While they focus on maintaining stability for old classes, this emphasis often restricts plasticity in learning new ones. In this work, we propose two plug-and-play methods to better balance stability and plasticity. First, for feature map distillation, we replace pixel-level constraints with statistical information extraction within a window, improving the learning of new classes while preserving structural integrity to overcome forgetting. Second, we use relative ranks of predictions to replace exact probabilistic values to loosen the constraints in the logit distribution of old classes. Extensive experiments demonstrate that our methods outperform state-of-the-art methods when integrating into several baselines across various CSS scenarios. © 2025 IEEE.

关键词： Continual Learning Knowledge Distillation Semantic Segmentation

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Towards Privacy-Aware Causal Structure Learning in Federated Setting

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IEEE Transactions on Big Data 2023年第6期9卷 1525-1535页

作者： Huang, Jianli Guo, Xianjie Yu, Kui Cao, Fuyuan Liang, Jiye Intelligent Interconnected Systems Laboratory of Anhui Province School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Taiyuan030006 China

Causal structure learning has been extensively studied and widely used in machine learning and various applications. To achieve an ideal performance, existing causal structure learning algorithms often need to centralize a large amount of data from multiple data sources. However, in the privacy-preserving setting, it is impossible to centralize data from all sources and put them together as a single dataset. To preserve data privacy, federated learning as a new learning paradigm has attached much attention in machine learning in recent years. In this paper, we study a privacy-aware causal structure learning problem in the federated setting and propose a novel federated PC (FedPC) algorithm with two new strategies for preserving data privacy without centralizing data. Specifically, we first propose a novel layer-wise aggregation strategy for a seamless adaptation of the PC algorithm into the federated learning paradigm for federated skeleton learning, then we design an effective strategy for learning consistent separation sets for federated edge orientation. The extensive experiments validate that FedPC is effective for causal structure learning in federated learning setting. © 2023 IEEE.

关键词： Big data

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Computational Experiments for Complex Social Systems:Experiment Design and Generative Explanation

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1022-1038页

作者： Xiao Xue Deyu Zhou Xiangning Yu Gang Wang Juanjuan Li Xia Xie Lizhen Cui Fei-Yue Wang the School of Computer Software College of Intelligence and ComputingTianjin UniversityTianjin 300350China the School of Software and the Joint SDUNTU Centre for Artificial Intelligence Research(C-FAIR) Shandong UniversityJinan 250101China the Information School Xi’an University of Financial and EconomicsXi’an 710100China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Hainan University Haikou 570228China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 1001901China

Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system *** address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating ***,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.

关键词： Agent-based modeling computational experiments cyber-physical-social systems(CPSS) generative deduction generative experiments meta model

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A Novel Approach for State of Health Estimation and Remaining Useful Life Prediction of Supercapacitors Using an Improved Honey Badger Algorithm Assisted Hybrid Neural Network

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Protection and Control of Modern Power Systems 2024年第6期9卷 1-18页

作者： Zhenxiao Yi Shi Wang Zhaoting Li Licheng Wang Kai Wang the School of Electrical Engineering Qingdao UniversityQingdao 266071Chinaand the State Key Laboratory of Reliability and Intelligence of Electrical EquipmentHebei University of TechnologyTianjin 300130China the Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China the School of Engineering Brown UniversityProvidenceRhode Island 02912USA the School of Information Engineering Zhejiang University of TechnologyHangzhou 310014China the School of Electrical Engineering Qingdao UniversityQingdao 266071China

Supercapacitors(SCs)are widely recognized as excellent clean energy storage *** state of health(SOH)estimation and remaining useful life(RUL)prediction are essential for ensuring their safe and reliable *** paper introduces a novel method for SOH estimation and RUL prediction,based on a hybrid neural network optimized by an improved honey badger algorithm(HBA).The method combines the advantages of convolutional neural network(CNN)and bidirectional long-short-term memory(BiLSTM)neural *** HBA optimizes the hyperparameters of the hybrid neural *** CNN automatically extracts deep features from time series data and reduces dimensionality,which are then used as input for the ***,recurrent dropout is introduced in the recurrent layer to reduce overfitting and facilitate the learning *** approach not only improves the accuracy of estimates and forecasts but also significantly reduces data processing *** under different working conditions are used to validate the proposed *** results show that the proposed hybrid model effectively extracts features,enriches local details,and enhances global perception *** proposed hybrid model outperforms single models,reducing the root mean square error to below 1%,and offers higher prediction accuracy and robustness compared to other methods.

关键词： Supercapacitors state of health remaining useful life honey badger algorithm recurrent dropout.

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A prompt-based approach to adversarial example generation and robustness enhancement

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Frontiers of Computer Science 2024年第4期18卷 85-96页

作者： Yuting YANG Pei HUANG Juan CAO Jintao LI Yun LIN Feifei MA Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Computer Science Stanford UniversityCA 94305USA School of Computing National University of SingaporeSingapore 119077Singapore Laboratory of Parallel Software and Computational Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

Recent years have seen the wide application of natural language processing(NLP)models in crucial areas such as finance,medical treatment,and news media,raising concerns about the model robustness and *** find that prompt paradigm can probe special robust defects of pre-trained language *** prompt texts are first constructed for inputs and a pre-trained language model can generate adversarial examples for victim models via *** results show that prompt paradigm can efficiently generate more diverse adversarial examples besides synonym ***,we propose a novel robust training approach based on prompt paradigm which incorporates prompt texts as the alternatives to adversarial examples and enhances robustness under a lightweight minimax-style optimization *** on three real-world tasks and two deep neural models show that our approach can significantly improve the robustness of models to resist adversarial attacks.

关键词： robustness adversarial example prompt learning pre-trained language model

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