Science China Information Sciences最新文献_第8页

ControlService: a containerized solution for control-algorithm-as-a-service in cloud control systems ControlService：云控制系统中控制算法即服务的容器化解决方案

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-06-27 DOI: 10.1007/s11432-023-4017-0

Chenggang Shan, Runze Gao, Zhen Yang, Wei Zhang, Yuanqing Xia

{"title":"ControlService: a containerized solution for control-algorithm-as-a-service in cloud control systems","authors":"Chenggang Shan, Runze Gao, Zhen Yang, Wei Zhang, Yuanqing Xia","doi":"10.1007/s11432-023-4017-0","DOIUrl":"https://doi.org/10.1007/s11432-023-4017-0","url":null,"abstract":"As an extension of networked control systems, cloud control systems (CCSs) have emerged as a new control paradigm to improve the service quality of emerging control missions, such as data-driven modeling and automated vehicles. Existing studies have used the workflow-based restructured method to optimize the computation-intensive algorithms in the CCSs. However, the challenges here are how to define and submit these algorithms’ workflows as cloud services and execute these algorithms’ workflows in a containerized manner. Based on these challenges, we propose a containerized solution for the control-algorithm-as-a-service (C3aS) in the CCSs, namely ControlService. It offers the control algorithm as a cloud workflow service and uses a customized workflow engine to realize the containerized execution. First, we employ a cloud workflow representation method to define a control algorithm into an abstract cloud workflow form. Afterward, we provide a cloud service representation of the abstract cloud workflow. Next, we design a workflow engine and submit the cloud service to this workflow engine to implement containerized execution of this cloud service in the CCSs. In the experiment, we discuss the cloud service form and containerized implementation of the subspace identification method. Experimental results show that the proposed ControlService has significant performance advantages in computational time, reduction percentage, and speedup ratio compared with the baseline method.","PeriodicalId":21618,"journal":{"name":"Science China Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.8,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141515964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed generalized Nash equilibrium seeking: event-triggered coding-decoding-based secure communication 分布式广义纳什均衡寻求：基于事件触发的编码-解码安全通信

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-06-27 DOI: 10.1007/s11432-023-4009-4

Shaofu Yang, Wenying Xu, Wangli He, Jinde Cao

引用次数: 0

Path signature-based XAI-enabled network time series classification 基于路径特征的 XAI 网络时间序列分类

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-06-27 DOI: 10.1007/s11432-023-3978-y

Le Sun, Yueyuan Wang, Yongjun Ren, Feng Xia

{"title":"Path signature-based XAI-enabled network time series classification","authors":"Le Sun, Yueyuan Wang, Yongjun Ren, Feng Xia","doi":"10.1007/s11432-023-3978-y","DOIUrl":"https://doi.org/10.1007/s11432-023-3978-y","url":null,"abstract":"Classifying network time series (NTS) is crucial for automating network administration and ensuring cyberspace security. It enables the detection of anomalies, the identification of network attacks, and the monitoring of performance issues, thereby providing valuable support for network protection and optimization. However, modern communication networks pose challenges for NTS classification methods. These include handling large-scale and complex NTS data, extracting features from intricate datasets, and addressing explainability requirements. These challenges are particularly pronounced for complex 5G networks. Notably, explainability has become crucial for the widespread deployment of network automation for 5G networks and beyond. To tackle these challenges, we propose a path-signature-based NTS classification model called recurrent signature (RecurSig). This innovative model is designed to overcome the time-consuming feature selection problem by utilizing deep-learning (DL) techniques. Additionally, it provides a solution for addressing the black-box issue associated with DL models in network automation systems (NAS) by incorporating an explainable classification approach. Extensive experimentation on various public datasets demonstrates that RecurSig outperforms existing models in accuracy and explainability. The results indicate its potential for application in cyberspace security and automated network management, offering an explainable solution for network protection and optimization.","PeriodicalId":21618,"journal":{"name":"Science China Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.8,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141515956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ensemble successor representations for task generalization in offline-to-online reinforcement learning 离线到在线强化学习中任务泛化的集合后继表征

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-06-25 DOI: 10.1007/s11432-023-4028-1

Changhong Wang, Xudong Yu, Chenjia Bai, Qiaosheng Zhang, Zhen Wang

{"title":"Ensemble successor representations for task generalization in offline-to-online reinforcement learning","authors":"Changhong Wang, Xudong Yu, Chenjia Bai, Qiaosheng Zhang, Zhen Wang","doi":"10.1007/s11432-023-4028-1","DOIUrl":"https://doi.org/10.1007/s11432-023-4028-1","url":null,"abstract":"In reinforcement learning (RL), training a policy from scratch with online experiences can be inefficient because of the difficulties in exploration. Recently, offline RL provides a promising solution by giving an initialized offline policy, which can be refined through online interactions. However, existing approaches primarily perform offline and online learning in the same task, without considering the task generalization problem in offline-to-online adaptation. In real-world applications, it is common that we only have an offline dataset from a specific task while aiming for fast online-adaptation for several tasks. To address this problem, our work builds upon the investigation of successor representations for task generalization in online RL and extends the framework to incorporate offline-to-online learning. We demonstrate that the conventional paradigm using successor features cannot effectively utilize offline data and improve the performance for the new task by online fine-tuning. To mitigate this, we introduce a novel methodology that leverages offline data to acquire an ensemble of successor representations and subsequently constructs ensemble Q functions. This approach enables robust representation learning from datasets with different coverage and facilitates fast adaption of Q functions towards new tasks during the online fine-tuning phase. Extensive empirical evaluations provide compelling evidence showcasing the superior performance of our method in generalizing to diverse or even unseen tasks.","PeriodicalId":21618,"journal":{"name":"Science China Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.8,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141502710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Continuous advantage learning for minimum-time trajectory planning of autonomous vehicles 自动驾驶汽车最小时间轨迹规划的持续优势学习

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-06-25 DOI: 10.1007/s11432-023-4059-6

Zhuo Li, Weiran Wu, Jialin Wang, Gang Wang, Jian Sun

引用次数: 0

An ensemble and cost-sensitive learning-based root cause diagnosis scheme for wireless networks with spatially imbalanced user data distribution 针对用户数据空间分布不平衡的无线网络的基于集合和成本敏感学习的根本原因诊断方案

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-06-25 DOI: 10.1007/s11432-023-4055-1

Qi Wang, Zhiwen Pan, Nan Liu

引用次数: 0

Towards imbalanced motion: part-decoupling network for video portrait segmentation 实现不平衡运动：用于视频肖像分割的部分解耦网络

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-06-25 DOI: 10.1007/s11432-023-4030-y

Tianshu Yu, Changqun Xia, Jia Li

{"title":"Towards imbalanced motion: part-decoupling network for video portrait segmentation","authors":"Tianshu Yu, Changqun Xia, Jia Li","doi":"10.1007/s11432-023-4030-y","DOIUrl":"https://doi.org/10.1007/s11432-023-4030-y","url":null,"abstract":"Video portrait segmentation (VPS), aiming at segmenting prominent foreground portraits from video frames, has received much attention in recent years. However, the simplicity of existing VPS datasets leads to a limitation on extensive research of the task. In this work, we propose a new intricate large-scale multi-scene video portrait segmentation dataset MVPS consisting of 101 video clips in 7 scenario categories, in which 10843 sampled frames are finely annotated at the pixel level. The dataset has diverse scenes and complicated background environments, which is the most complex dataset in VPS to our best knowledge. Through the observation of a large number of videos with portraits during dataset construction, we find that due to the joint structure of the human body, the motion of portraits is part-associated, which leads to the different parts being relatively independent in motion. That is, the motion of different parts of the portraits is imbalanced. Towards this imbalance, an intuitive and reasonable idea is that different motion states in portraits can be better exploited by decoupling the portraits into parts. To achieve this, we propose a part-decoupling network (PDNet) for VPS. Specifically, an inter-frame part-discriminated attention (IPDA) module is proposed which unsupervisedly segments portrait into parts and utilizes different attentiveness on discriminative features specified to each different part. In this way, appropriate attention can be imposed on portrait parts with imbalanced motion to extract part-discriminated correlations, so that the portraits can be segmented more accurately. Experimental results demonstrate that our method achieves leading performance with the comparison to state-of-the-art methods.","PeriodicalId":21618,"journal":{"name":"Science China Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.8,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rethinking attribute localization for zero-shot learning 反思零点学习的属性定位

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-06-25 DOI: 10.1007/s11432-023-4051-9

Shuhuang Chen, Shiming Chen, Guo-Sen Xie, Xiangbo Shu, Xinge You, Xuelong Li

{"title":"Rethinking attribute localization for zero-shot learning","authors":"Shuhuang Chen, Shiming Chen, Guo-Sen Xie, Xiangbo Shu, Xinge You, Xuelong Li","doi":"10.1007/s11432-023-4051-9","DOIUrl":"https://doi.org/10.1007/s11432-023-4051-9","url":null,"abstract":"Recent advancements in attribute localization have showcased its potential in discovering the intrinsic semantic knowledge for visual feature representations, thereby facilitating significant visual-semantic interactions essential for zero-shot learning (ZSL). However, the majority of existing attribute localization methods heavily rely on classification constraints, resulting in accurate localization of only a few attributes while neglecting the rest important attributes associated with other classes. This limitation hinders the discovery of the intrinsic semantic relationships between attributes and visual features across all classes. To address this problem, we propose a novel attribute localization refinement (ALR) module designed to enhance the model’s ability to accurately localize all attributes. Essentially, we enhance weak discriminant attributes by grouping them and introduce weighted attribute regression to standardize the mapping values of semantic attributes. This module can be flexibly combined with existing attribute localization methods. Our experiments show that when combined with the ALR module, the localization errors in existing methods are corrected, and state-of-the-art classification performance is achieved.","PeriodicalId":21618,"journal":{"name":"Science China Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.8,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive neural network control of a 2-DOF helicopter system considering input constraints and global prescribed performance 考虑输入约束和全局规定性能的 2-DOF 直升机系统的自适应神经网络控制

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-06-25 DOI: 10.1007/s11432-023-3949-3

Zhijia Zhao, Jiale Wu, Zhijie Liu, We He, C. L. Philip Chen

引用次数: 0

Shor’s algorithm does not factor large integers in the presence of noise 肖尔算法在有噪声的情况下不能对大整数进行因式分解

IF 8.8 2区计算机科学

Science China Information Sciences Pub Date : 2024-06-18 DOI: 10.1007/s11432-023-3961-3

Jin-Yi Cai

引用次数: 0