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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhu, Yan Yang, Peng Zhu, Jingyang Wen, Dingzhu Wang, Ting Zhou, Yong Shi, Yuanming Jiang, Chunxiao East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China

ISBN: (纸本)9798350351255

With the surge in the number of low earth orbit (LEO) satellites, continuous research has emerged on using satellite data to train artificial intelligence models. On one hand, traditional centralized training on the ground is not feasible due to privacy concerns and limited bandwidth for downloading raw satellite data. On the other hand, due to the limited energy and computational capability of satellites, training directly on satellites suffers from prolonged latency, especially for large models. To alleviate these issues, we propose a novel satellite-ground collaborative federated fine-tuning architecture, where ground stations (GSs) and satellites collaboratively train a global model without the need for data downloads. In this proposed architecture, satellites serve as edge devices and the ground server serves as a coordinator. However, the short satellite-ground communication windows caused by the high mobility of satellites and the substantial intra-orbit data transmission bring special challenges to the transmission process of federated edge learning. To tackle these challenges, we carefully design the satellite-ground collaborative fine-tuning architecture and utilize an optimized ring all-reduce algorithm and network flow algorithm to enhance the intra-orbit and ground-satellite transmissions, respectively. Experimental results demonstrate that our proposed architecture significantly reduces the training time by 40% compared to training solely on satellite. © 2024 IEEE.

关键词： Satellite ground stations

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A Multi-Armed Bandits Learning-Based Approach to Service Caching in Edge Computing Environment 30th

A Multi-Armed Bandits Learning-Based Approach to Service Ca...

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30th International Conference on Web Services, ICWS 2023

作者： Li, Jinpeng Zhao, Jiale Chen, Peng Xia, Yunni Li, Fan Li, Yin Zeng, Feng Liu, Hui School of Computer Chongqing University Chongqing400030 China School of Computer and Software Engineering Xihua University Chengdu610039 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610065 China Guangzhou Institute of Software Application Technology Guangzhou510990 China Limited Shenzhen518129 China School of Computer Science and Technology Beijing Institute of Technology Beijing100083 China

ISBN: (纸本)9783031448355

Mobile edge computing (MEC) is a newly emerging concept that provides significant local computing power and reduces end-to-end latency. In MEC environments, caching frequently accessed services on edge servers effectively reduces latency and improves system responsiveness. An ongoing research topic in such a cachable MEC context is to design novel algorithms for yielding high-quality caching decision that guarantee high user-perceived quality-of-service (QoS) and high system responsiveness of delivery of cached content with the difference of caching capacities of edge servers and diversified content popularity appropriately addressed. In this article, we propose a multi-armed bandits learning-based method busing a Thompson sampling for generating caching decisions. We introduce a genetic multi-armed bandits algorithm (GMAB), which synthesizes the genetic algorithm (GA) and multi-armed bandits (MAB), for optimizing caching effectiveness with timing and space constraints. The experiment results show that GMAB outperforms traditional methods in terms of multiple aspects. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Magnetic Type Classification in Sunspot Group Based on Semi-supervised Learning and Knowledge Distillation 7

Magnetic Type Classification in Sunspot Group Based on Semi-...

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7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022

作者： Liu, Junhong Li, Baoping Luo, Zihui School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing China Beijing Institute of Computer Technology and Application Beijing China

ISBN: (纸本)9781665478571

Sunspot group, known as active solar regions, is the main sources of solar storms. The morphological and magnetic characteristics of solar active regions play a very important role in solar storm forecasting and is well described by Mount Wilson Sunspot Classification Scheme. The development of convolutional neural network methods in the field of image processing makes efficient magnetic type classification possible. In this paper, we propose a method based on semi-supervised learning and knowledge distillation for magnetic type classification in sunspot group. On the sunspot magnetic type classification dataset, our method achieves 95.14% total classification accuracy, 97.4%, 94.43% and 85.71% F1-scores of Alpha, Beta, and Beta-x types respectively. © 2022 IEEE.

关键词： Solar energy

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Identify and Update Test Cases when Production Code Changes: A Transformer-Based Approach 23

Identify and Update Test Cases when Production Code Changes:...

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Proceedings of the 38th IEEE/ACM International Conference on Automated software Engineering

作者： Xing Hu Zhuang Liu Xin Xia Zhongxin Liu Tongtong Xu Xiaohu Yang School of Software Technology Zhejiang University Ningbo China College of Computer Science and Technology Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei Hangzhou China

ISBN: (纸本)9798350329964

software testing is one of the most essential parts of the software lifecycle and requires a substantial amount of time and effort. During the software evolution, test cases should coevolve with the production code. However, the co-evolution of test cases often fails due to tight project schedules and other reasons. Obsolete test cases improve the cost of software maintenance and may fail to reveal faults and even lead to future bugs. Therefore, it is essential to detect and update these obsolete test cases in time. In this paper, we propose a novel approach Ceprot (Co-Evolution of Production-Test Code) to identify outdated test cases and update them automatically according to changes in the production code. Ceprot consists of two stages, i.e., obsolete test identification and updating. Specifically, given a production code change and a corresponding test case, Ceprot first identifies whether the test case should be updated. If the test is identified as obsolete, Ceprot will update it to a new version of test case. To evaluate the effectiveness of the two stages, we construct two datasets. Our dataset focuses on method-level production code changes and updates on their obsolete test cases. The experimental results show that Ceprot can effectively identify obsolete test cases with precision and recall of 98.3% and 90.0%, respectively. In addition, test cases generated by Ceprot are identical to the ground truth for 12.3% of samples that are identified as obsolete by Ceprot. We also conduct dynamic evaluation and human evaluation to measure the effectiveness of the updated test cases by Ceprot. 48.0% of updated test cases can be compiled and the average coverage of updated cases is 34.2% which achieves 89% coverage improvement over the obsolete tests. We believe that this study can motivate the co-evolution of production and test code.

关键词： test code maintenance

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Towards practical data alignment in production federated learning

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Frontiers of Computer science 2025年第1期19卷 167-169页

作者： Yexuan SHI Wei YU Yuanyuan ZHANG Chunbo XUE Yuxiang ZENG Zimu ZHOU Manxue GUO Lun XIN Wenjing NIE State Key Laboratory of Complex&Critical Software Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang UniversityBeijing 100191China Zhongguancun Pan Connected Mobile Communication Technology Innovation and Application Research Institute Beijing 100088China China Mobile Research Institute Beijing 100053China School of Data Science City University of Hong KongHong Kong 999077China

1 Introduction Federated learning has emerged as a promising par-adigm for collaborative model training that facilitates cooperation among multiple parties while ensuring data privacy[1].Successful alignment of data across parties is crucial for effective federated learning[2].This alignment involves harmonizing heterogeneous data from different parties to identify shared data for joint model *** set intersection(PSI)is a technique that allows the alignment of common entities between parties without revealing additional ***,efficiently performing data alignment with PSI in federated learning[3],especially when dealing with highly unbalanced data,remains challenging due to the low efficiency.

关键词： alignment dealing parties

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TMFIF:Transformer-based multi-focus image fusion 5

TMFIF:Transformer-based multi-focus image fusion

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Li, Rui Geng, Shengling Zhang, Dan Zhou, Mingquan Qinghai Normal University School of Computer Science Xining China Nanyang Institute of Technology School of Computer and Software Nanyang China People's Government of Qinghai Province & Beijing Normal University Academy of Plateau Science and Sustainability Xining China Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application China

ISBN: (纸本)9798350373820

Multi-focus image fusion is a hot topic in the field of image processing, and it is a fundamental problem in the fields of image editing, image synthesis, and target retrieval. In previous fusion methods, although feature-rich datasets, models, and algorithms have been provided, there are still many problems with the effective fusion of distant and near views in complex backgrounds. To solve the challenging multi-focus image fusion problem more accurately, we introduce the Transformer Network based on Encoder Decoder (TMFIF), which can extract more generalized features based on the features of multi-focused images to achieve image fusion in complex backgrounds for better visual effects. In this work, we achieve fusion by inputting two images, near and far view. We compare the performance with other multifocal image fusion algorithms by conducting experiments on publicly available datasets and illustrated using existing evaluation methods and evaluation metrics;the results of the experiments show that our method visualizes the fusion effect better through the encoder and the decoder and the evaluation metrics are also relatively good. © 2024 IEEE.

关键词： Image fusion

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DataFly: A Confidentiality-Preserving Data Migration Across Heterogeneous Blockchains

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IEEE Transactions on Computers 2025年第6期74卷 1814-1828页

作者： Li, Taotao Huang, Huawei Abla, Parhat Deng, Zhihong Yang, Qinglin Xie, Anke He, Debiao Zheng, Zibin Sun Yat-sen University School of Software Engineering Zhuhai528478 China South China Normal University School of Artificial Intelligence Foshan528225 China South China Normal University Postdoctoral Research Station in Mathematics Guangzhou510006 China Chinese Academy of Sciences Key Laboratory of Cyberspace Security Defense Institute of Information Engineering Beijing100085 China Guangzhou University School of Mathematics and Information Science Guangzhou510006 China Guangzhou University Cyberspace Institute of Advanced Technology Huangpu Research School of Guangzhou University Huangpu Guangdong Guangzhou510006 China Yunnan Innovation Institute of Beihang University Yunnan Key Laboratory of Blockchain Application Technology Kunming650233 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China

Permissioned blockchains play a significant role in various application scenarios. applications built on heterogeneous permissioned blockchains need to migrate data from one chain to another, aiming to keep their competitiveness and security. Thus, data migration across heterogeneous chains is a building block of permissioned blockchains. However, existing data migration protocols across heterogeneous chains are rarely used in practice since data migration technologies are insecure. To this end, we propose a data migration protocol across permissioned blockchains, named DataFly. We design a peg consensus mechanism, which provides consistent data-migration functionality between any two permissioned blockchains. To preserve the confidentiality of data, we invoke two classical cryptographic methods, i.e., i) ECDSA feature and ii) the integrated signature and public key encryption scheme. Through combining those two methods, data can be securely migrated from one permissioned blockchain to another without exposing the migrated data to anyone except associated parties. To demonstrate the practicality of DataFly, we implement a prototype of DataFly using existing popular permissioned blockchains, i.e., Hyperledger Fabric and private enterprise Ethereum. Measurement results demonstrate that DataFly outperforms related works in terms of transaction latency and gas costs. © 1968-2012 IEEE.

关键词： Blockchain

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Edge Learning-Based Efficient Data Imputation of Water Quality 7th

Edge Learning-Based Efficient Data Imputation of Water Quali...

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7th International Conference on Computing, Control and Industrial Engineering, CCIE 2023

作者： Wang, Yongsheng Chen, Zhen Liu, Limin Gao, Jing Liu, Guangwen Xu, Zhiwei School of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering Technology Research Center of Big Data Based Software Service Hohhot010080 China Institute of Computing Technology Chinese Academy of Sciences Beijing100000 China

ISBN: (纸本)9789819927296

Water quality monitoring is critical for developing effective surface water management strategies, whereas data preprocessing is the foundation for monitoring water quality. Water quality monitoring usually suffers from abnormal and missing values in the collected data of surface water quality, and thus an efficient data imputation method for water quality data is highly desired. To handle these challenges, we propose an edge learning-based data imputation method. By using VAE as the generator of WGAN-LP, the performance of value imputation of water quality is generalized and improved. In addition, we demonstrate the performance of the proposed method on two real-world datasets, collected from Wuliangsu Lake and Taihu Lake in Inner Mongolia. Compared with state of the art, the enhanced imputation accuarcy of water quality is achieved. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Water quality

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Research on intelligent penetration mechanism of netgap data based on database adaptive matching 3

Research on intelligent penetration mechanism of netgap data...

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3rd International Conference on Electronic Information Engineering, Big Data, and Computer technology, EIBDCT 2024

作者： Wu, Rutao Xu, Zhiwei Liu, Xuyang Liu, Wenjing Liu, Limin School of Data Science and Application Inner Mongolia University of Technology Huhehaote010000 China Inner Mongolia Autonomous Reg. Software Eng. Serv. Eng. Technology Research Center Based on Big Data Huhehaote010000 China Inner Mongolia Autonomous Region Product Quality Inspection and Research Institute Huhehaote010070 China Haihe Laboratory of Information Technology Application Innovation Tianjin300450 China Institute of Computing Technology of the Chinese Academy of Sciences Beijing100080 China

ISBN: (纸本)9781510680449

The purpose of data penetration is to achieve timely exchange of data information while ensuring the security of network isolation environment. The current research mainly focuses on protecting data privacy and developing basic data penetration methods for netgap, but these methods generally overlook the adaptive matching of data. Therefore, this article proposes an intelligent netgap data penetration mechanism based on database adaptive matching, which is an end-to-end cross netgap data packet adaptation mechanism. To overcome the problems of overfitting and local optima, XGBoost based on adaptive particle swarm optimization is adopted. By increasing the diversity of subgroups, the adaptive matching effect was improved, and the optimized XGBoost model was fitted to real-time data, with an accuracy increase from 72% to 94%. The experimental results show that this mechanism significantly improves the efficiency of packet adaptive matching while solving the problem of data penetration in the isolated environment of the network gate. The time cost is only 50% of the previous one, which improves the throughput of the system. The optimized XGBoost model is superior to the original model in terms of error and accuracy, and has certain practicality and easy generalization. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Data privacy

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Research On Low-Temperature Risk Assessment for Spatial and Temporal Distribution in Northeast and South China

Research On Low-Temperature Risk Assessment for Spatial and ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Min Chen Lei He Bin Liu Yuxia Li Han Luo Jialin Tao Binyang Yang School of Software Engineering Chengdu University of Information Technology Chengdu China Sichuan Province Engineering Technology Research Centre of Support Software of Informatization Application Chengdu China School of Automation University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Low-temperature disasters seriously affect the ecological environment and people's daily lives. In the research, the spatial distribution of low-temperature risk over 30 years has been obtained by collecting meteorological and socio-economic data from 1991 to 2020, and then a risk assessment model based on hazard, exposure, and vulnerability indicators is built. The results show that the high-risk areas are mainly concentrated in the central, eastern, and southern parts of Northeast China, and the overall risk shows an increasing trend; the regions at high risk are primarily found in Hunan Province and the central part of Jiangxi Province in south China. Further, the change in risk is most obvious between 1991-2005, and the overall risk value decreased about 41.28%. The Contribution to low-temperature risk in Northeast China comes mainly from vulnerability. While, In South China, the contribution of various factors to risk is more evenly distributed.

关键词： Graphical models Disasters Biological system modeling Geoscience and remote sensing Market research Hazards Data models

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