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32nd British Machine Vision Conference, BMVC 2021

作者： Wu, Kewei Wang, Tao Xie, Zhao Guo, Dan Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Human pose estimation is a challenging task that requires the comprehension of the pose structure. This work can refer to spatial relation inference in a pose structure model;how to model the dynamic spatial relation against various unreliable joints is critical. To this end, we propose a Distilling Dynamic Spatial Relation network (DDSR), which builds pose-based graph representation by exploiting the feature of spatial relation from the location distribution of joints. We use a dynamic message propagation mechanism to update the spatial relation on edges. Specifically, to filter out the noisy predictions, we select the joints with high confidence;to enhance the spatial relation in a large receptive field, we propagate multi-stage messages among joints. Besides, to reduce the computation cost of the multi-stage message propagation, we design a cross-resolution distillation framework. We use a new spatial distillation loss to verify the spatial relation between the teacher model and the student model. Experimental results on COCO and MPII datasets show that our method is superior to the state-of-the-art methods. The visualization results further verify the interpretability of our spatial relation. © 2021. The copyright of this document resides with its authors.

关键词： Distillation

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Fa YOLO: fog-aware instance segmentation of ships with feature refinement in foggy scenes

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Multimedia Systems 2025年第4期31卷 1-25页

作者： Yuan, Ming Meng, Hao Yan, Tianhao Wu, Junbao College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Technology and Application of Marine Equipment Harbin Engineering University Harbin China Research Center for Data Hub and Security Zhejiang Lab Hangzhou China

Ship instance segmentation is essential for intelligent maritime navigation and traffic safety. However, under adverse weather conditions such as fog, the image quality from imaging devices degrades significantly, leading to poor performance of existing instance segmentation methods. To address this challenge, we propose FA YOLO, a ship instance segmentation framework based on interference suppression and feature refinement designed to enhance performance under foggy conditions. First, we propose a Multi-Scale Feature Aggregation Mamba (MFAM) module, which utilizes a state space modeling approach and a multi-scale channel aggregation gating mechanism to enhance long-range dependency modeling and global contextual representation. Second, we propose an Adaptive Fog Dehazing Module (AFDM), which utilizes parallel channel and spatial attention mechanisms along with a window-based multi-head self-attention strategy to suppress fog-related interference and improve focus on target regions. Third, we propose a Multi-Scale Perception-Guided Attention Module (MPAM), which integrates channel-position attention fusion, multi-window branch feature extraction and similarity measurement strategies to adaptively enhance and aggregate multi-scale contextual features. In addition, to address the lack of suitable foggy ship instance segmentation datasets in the community, we collected and annotated a new instance segmentation dataset of maritime ships under foggy conditions, FSISD. This dataset contains 10,249 ship images, covering common ship categories and environmental conditions. Experimental results on Foggy Cityscapes, FSISD and Foggy COCO-boat demonstrate that FA YOLO outperforms the baseline YOLOv8s in segmentation accuracy by 3.3%, 2.2% and 1.3%, respectively, confirming superior performance and strong generalization capability.

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A SnF Scheduling Method for HFL Over Edge Computing Power Optical Network 22

A SnF Scheduling Method for HFL Over Edge Computing Power Op...

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22nd International Conference on Optical Communications and Networks, ICOCN 2024

作者： Zhang, Jia Lin, Xiao Hong, Zhixiang Wu, Guiping Li, Jun Chen, Zhen Sun, Weiqiang Lou, Zhilan College of Physics and Information Engineering Fuzhou University Fuzhou China School of Electronic and Information Engineering Soochow University Suzhou China Shanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai China School of Data Sciences Zhejiang University of Finance and Economics Hangzhou China

A SnF scheduling method is presented to schedule data transfers in the HFL aggregation process across the ECPON. Studies demonstrate that the proposed method outperforms conventional methods in terms of network perfor... 详细信息

ISBN: (纸本)9798350367652

关键词： Edge computing power network hierarchical federated learning model aggregation store-and-forward

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Marginal Value-Based Edge Resource Pricing and Allocation for Deadline-Sensitive Tasks

Marginal Value-Based Edge Resource Pricing and Allocation fo...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Puwei Wang Zhouxing Sun Ying Zhan Haoran Li Xiaoyong Du School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing China Guizhou University of Finance and Economics Guiyang City China

In edge computing (EC), resource allocation is to allocate computing, storage and networking resources on the edge nodes (ENs) efficiently and reasonably to tasks generated by users. Due to the resource-limitation of ENs, the tasks often need to compete for the resources. Pricing mechanisms are widely used to deal with the resource allocation problem, and the valuations of tasks play a critical role in the price mechanisms. However, users naturally are not willing to expose the valuations of their tasks due to conflicts of interests. Current research works usually adopt truthful auctions to motivate the users to report honestly the valuations of their tasks. In this paper, we introduce the marginal value to estimate the valuations of tasks, and propose a marginal value-based pricing mechanism using the incentive theory, which motivates the tasks with higher marginal values to actively request more resources. The EC platform sets the resource prices using the price mechanism, and then the users determine their resource requests relying on the resource prices and the valuations of their tasks. After receiving the deadline-sensitive tasks from the users, the resource allocation can be modeled as a knapsack problem with the deadline constraints. Extensive experimental results demonstrate that our approach is computationally efficient and is promising in enhancing the utility of the EC platform and the tasks.

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Electronic Medical Record Sharing System Based on Hyperledger Fabric and InterPlanetary File System 2021

Electronic Medical Record Sharing System Based on Hyperledge...

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5th International Conference on Compute and data Analysis, ICCDA 2021

作者： Li, Lei Yue, Zhengxiang Wu, Gongqing Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781450389112

Electronic Medical Records (EMR) and other medical data contain important and sensitive privacy information of patients, which provide important basis and reference for their doctors to diagnose and treat them. With the continuous development of data sharing, big data applications and other technologies, new requirements are proposed for the storage of medical privacy data. At present, medical data have the characteristics of obstructed sharing and exchange, difficult data tracing and data sensitivity, which increases the cost and difficulty of each diagnosis. Moreover, patients lack the right to know and control their own medical data, which is easy to cause data fraud or data leakage. As a decentralized, tamper proof and traceable distributed ledger system, blockchain brings new solutions to these problems. This paper designs a medical data fusion and distributed privacy management system based on the Hyperledger Fabric blockchain development platform and InterPlanetary File System. It can not only solve the problem of mutual distrust in the traditional system, but also realize the safe data storage and the safe sharing of medical record data between medical organizations which is a beneficial attempt to overcome the disadvantages of traditional hospital data separation. At the same time, according to the requirements of different levels and types of medical users or groups for data security and personal privacy protection, the access control technology of users is studied to ensure personal privacy data. © 2021 ACM.

关键词： Blockchain

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Decode the Dark Side of the Language: Applications of LLMs in the Dark Web 9

Decode the Dark Side of the Language: Applications of LLMs i...

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9th IEEE International Conference on data Science in Cyberspace, DSC 2024

作者： Chen, Hongfan Diao, Yigang Xiang, Hui Huo, Yixuan Xie, Xiaqing Zhao, Jiapeng Wang, Xuebin Sun, Yanwei Shi, Jinqiao Beijing University of Posts and Telecommunications School of Cyberspace Security Beijing China Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Beijing China Data and Technology Support Center Cyberspace Administration of China Beijing China Chinese Academy of Sciences Institute of Information Engineering Beijing China

ISBN: (纸本)9798350391367

Previous studies have discovered that Transformers pre-trained on large scale datasets have a great advantage over traditional methods and can achieve human-level performance in many NLP tasks. Various encoder-only models like BERT and RoBERTa are used in the researches of the language in the Dark Web and most of them have achieved state-of-the-art performance. However, decoder-only models are not frequently used as they were considered to be unsuitable for NLU tasks until recent advancements in large language models. In this paper, we discuss the application of large scale decoder-only models in NLP tasks of real-world Dark Web research. We explore the capabilities of these models with the most realistic datasets in this specific domain. Surprisingly, results show that decoder-only models can achieve similar or better performance compared with state-of-the-art encoder-only model in some cases and show their own unique advantages in such field. We also discuss the limitations of these models and the challenges associated with their application in such field. Our study unveils the potential of decoder-only models in NLP tasks of real-world Dark Web research. © 2024 IEEE.

关键词： Decoding

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Strangeness S=-2 baryon-baryon interactions and femtoscopic correlation functions in covariant chiral effective field theory

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Chinese Physics C 2023年第2期47卷 174-189页

作者：刘志伟李凯文耿立升 School of Physics Beihang UniversityBeijing 102206China Medical Management Department CAS Ion Medical Technology Co.Ltd.Beijing 100190China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang UniversityKey Laboratory of Big Data-Based Precision Medicine(Beihang University)Ministry of Industry and Information TechnologyBeijing 100191China Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang UniversityBeijing 102206China School of Physics and Microelectronics Zhengzhou UniversityZhengzhou 450001China

We study the baryon-baryon interactions with strangeness S=-2 and corresponding momentum correlation functions in leading order covariant chiral effective field *** relevant low energy constants are determined by fitting to the latest HAL QCD simulations,taking into account all the coupled *** the so-obtained strong interactions to the physical point and considering both quantum statistical effects and the Coulomb interaction,we calculate the ΛΛ and Ξ^(-)p correlation functions with a spherical Gaussian source and compare them with recent experimental *** find a good agreement between our predictions and the experimental measurements by using the source radius determined in proton-proton correlations,which demonstrates the consistency between theory,experiment,and lattice QCD ***,we predict the Σ^(+)Σ^(+),Σ^(+)Λ,and Σ^(+)Σ^(-) interactions and corresponding momentum correlation *** further investigate the influence of the source shape and size of the hadron pair on the correlation functions studied and show that the current data are not very sensitive to the source *** experimental measurements of the predicted momentum correlation functions will provide a non-trivial test of not only SU(3) flavor symmetry and its breaking but also the baryon-baryon interactions derived in covariant chiral effective field theory.

关键词： covariant chiral effective field theory momentum correlation functions baryon-baryon interactions

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Space-Time Coordinated Scheduling Approach in Computing Power Optical Networks 22

Space-Time Coordinated Scheduling Approach in Computing Powe...

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22nd International Conference on Optical Communications and Networks, ICOCN 2024

作者： Wu, Guiping Lin, Xiao Lin, Huihuang Hong, Zhixiang Zhang, Jia Chen, Zhen Li, Jun Sun, Weiqiang Lou, Zhilan College of Physics and Information Engineering Fuzhou University Fuzhou China Shanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai China School of Electronic and Information Engineering Soochow University Suzhou China School of Data Sciences Zhejiang University of Finance and Economics Hangzhou China

In this paper, an STC-SnF approach is presented to schedule bulk data transfers across the CPON. Studies show it can ensure the coordinated space-time relation of bandwidth fragments and hence benefit the SnF scheduli... 详细信息

ISBN: (纸本)9798350367652

关键词： Computing power

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A Dynamic Computing Power Scheduling Scheme for the EDWC Project in China 22

A Dynamic Computing Power Scheduling Scheme for the EDWC Pro...

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22nd International Conference on Optical Communications and Networks, ICOCN 2024

作者： Hong, Zhixiang Lin, Xiao Wu, Guiping Zhang, Jia Li, Jun Chen, Zhen Sun, Weiqiang Lou, Zhilan College of Physics and Information Engineering Fuzhou University Fuzhou China School of Electronic and Information Engineering Soochow University Suzhou China Shanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai China School of Data Sciences Zhejiang University of Finance and Economics Hangzhou China

In this paper, we conduct the cost analysis of the EDWC project. Studies show the current transmission cost is too high. We present a scheme to schedule the computing jobs based on the price fluctuation. © 2024 I... 详细信息

ISBN: (纸本)9798350367652

关键词： Computing power

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Quantum partial least squares regression algorithm for multiple correlation problem

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Chinese Physics B 2022年第3期31卷 177-186页

作者： Yan-Yan Hou Jian Li Xiu-Bo Chen Yuan Tian School of Artificial Intelligence Beijing University of Post and TelecommunicationsBeijing 100876China College of Information Science and Engineering Zaozhuang UniversityZaozhuang 277160China Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Post and TelecommunicationsBeijing 100876China GuiZhou University Guizhou Provincial Key Laboratory of Public Big DataGuiyang 550025China

Partial least squares(PLS) regression is an important linear regression method that efficiently addresses the multiple correlation problem by combining principal component analysis and multiple regression. In this paper, we present a quantum partial least squares(QPLS) regression algorithm. To solve the high time complexity of the PLS regression, we design a quantum eigenvector search method to speed up principal components and regression parameters construction. Meanwhile, we give a density matrix product method to avoid multiple access to quantum random access memory(QRAM)during building residual matrices. The time and space complexities of the QPLS regression are logarithmic in the independent variable dimension n, the dependent variable dimension w, and the number of variables m. This algorithm achieves exponential speed-ups over the PLS regression on n, m, and w. In addition, the QPLS regression inspires us to explore more potential quantum machine learning applications in future works.

关键词： quantum machine learning partial least squares regression eigenvalue decomposition

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