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arXiv 2021年

作者： Qiu, Zhen Zhang, Yifan Lin, Hongbin Niu, Shuaicheng Liu, Yanxia Du, Qing Tan, Mingkui School of Software Engineering South China University of Technology School of Computing National University of Singapore Singapore Key Laboratory of Big Data and Intelligent Robot Ministry of Education Pazhou Laboratory

We study a practical domain adaptation task, called source-free unsupervised domain adaptation (UDA) problem, in which we cannot access source domain data due to data privacy issues but only a pre-trained source model and unlabeled target data are available. This task, however, is very difficult due to one key challenge: the lack of source data and target domain labels makes model adaptation very challenging. To address this, we propose to mine the hidden knowledge in the source model and exploit it to generate source avatar prototypes (i.e., representative features for each source class) as well as target pseudo labels for domain alignment. To this end, we propose a Contrastive Prototype Generation and Adaptation (CPGA) method. Specifically, CPGA consists of two stages: (1) prototype generation: by exploring the classification boundary information of the source model, we train a prototype generator to generate avatar prototypes via contrastive learning. (2) prototype adaptation: based on the generated source prototypes and target pseudo labels, we develop a new robust contrastive prototype adaptation strategy to align each pseudo-labeled target data to the corresponding source prototypes. Extensive experiments on three UDA benchmark datasets demonstrate the effectiveness and superiority of the proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： data privacy

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Human Interaction Learning on 3D Skeleton Point Clouds for Video Violence Recognition 1

引用

16th European Conference on Computer Vision, ECCV 2020

作者： Su, Yukun Lin, Guosheng Zhu, Jinhui Wu, Qingyao School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Beijing China Nanyang Technological University Singapore Singapore

ISBN: (数字)9783030585488

ISBN: (纸本)9783030585471

This paper introduces a new method for recognizing violent behavior by learning contextual relationships between related people from human skeleton points. Unlike previous work, we first formulate 3D skeleton point clouds from human skeleton sequences extracted from videos and then perform interaction learning on these 3D skeleton point clouds. A novel Skeleton Points Interaction Learning (SPIL) module, is proposed to model the interactions between skeleton points. Specifically, by constructing a specific weight distribution strategy between local regional points, SPIL aims to selectively focus on the most relevant parts of them based on their features and spatial-temporal position information. In order to capture diverse types of relation information, a multi-head mechanism is designed to aggregate different features from independent heads to jointly handle different types of relationships between points. Experimental results show that our model outperforms the existing networks and achieves new state-of-the-art performance on video violence datasets. © 2020, Springer Nature Switzerland AG.

关键词： Musculoskeletal system

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The Newly Receiving Hood System for Tunnel Shield Machines: Design and Implementation

SSRN

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SSRN 2023年

作者： Lin, Yunhong Guo, Xiaodong Yao, Zhengyu Chen, Zhong He, Hongming Guangzhou Metro Design and Research Institute Co. Ltd Guangdong Guangzhou China School of Geographic Sciences East China Normal University Shanghai200241 China Key Laboratory of Geographic Information Science Ministry of Education East China Normal University Shanghai200241 China Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources Shanghai200241 China Faculty of Land Resources Engineering Kunming University of Science and Technology Kunming630093 China

How to solve the current engineering risks and problems faced in the shield receiving process has become an important research hotspot in the industry. To this end, we have carried out research into newly receiving hood system for tunnel shield machines under unreinforced ground conditions. Based on the concept of active pressurization, a new type of sealing device is used, using an active pressurized sealing system for the whole process from the shield breaking gate to the construction of the ring beam of the gate. The results of the study show that the newly receiving hood system is suitable for all kinds of unreinforced soils and all sizes of shield machines, and is set up in three models of 20m, 40m and super 40m water pressure, using the technique of building pressure in the earth bin before turning the knife plate to grind the enclosure to achieve a good sealing effect. In addition, the station end settlement data shows that with the new initiation technique the works are carried out without negative impact on the surrounding environment. The study proposes a new receiving hood system that provides a good solution to the risks faced by shield operations. © 2023, The Authors. All rights reserved.

关键词： Tunneling machines

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The Newly Launching Hood System for Tunnel Shield Machines: Design and On-Site Operation

SSRN

引用

SSRN 2023年

作者： Lin, Yunhong Chang, Ruicheng Yao, Zhengyu Chen, Zhong He, Hongming Guangzhou Metro Design and Research Institute Co. Ltd Guangdong Guangzhou China Faculty of Land Resources Engineering Kunming University of Science and Technology Kunming630093 China School of Geographic Sciences East China Normal University Shanghai200241 China Key Laboratory of Geographic Information Science Ministry of Education East China Normal University Shanghai200241 China Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources Shanghai200241 China

The resolution of engineering risks and problems during the shield machine launching process has become a crucial research topic in the industry. To address this issue, we conducted a study on a newly launching hood system for unconsolidated soil conditions. Based on the concept of active pressurization and using a new sealing device, we applied an active pressurized sealing system throughout the construction process from the shield breakthrough door to the ring beam of the door. The research results demonstrated that the newly launching hood system is suitable for various unconsolidated soil layers and shield machine sizes, with three models of 20m, 40m, and above 40m water pressure. By using the technology of pressurizing the soil before turning the cutterhead to grind the surrounding structure, a good sealing effect was achieved. Moreover, station end settlement data showed that the use of the newly launching hood system does not have a negative impact on the surrounding environment during construction. The launching hood system for tunnel shield provides an excellent solution for the risks faced in shield machine operations. © 2023, The Authors. All rights reserved.

关键词： Grinding (machining)

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Auction Theory and Game Theory Based Pricing of Edge Computing Resources: A Survey

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IEEE Internet of Things Journal 2025年

作者： Yu, Jiguo Liu, Shun Zou, Yifei Wang, Guijuan Hu, Chunqiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Qufu Normal University School of Computer Science Shandong Rizhao China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250353 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China

The proliferation of Internet of Things (IoT) devices and edge computing applications has heightened the demand for efficient resource allocation and pricing mechanisms. Effective pricing strategies play a crucial role in optimizing resource utilization, reducing latency, and ensuring fair resource distribution among users. Both auction theory and game theory have emerged as fundamental economic frameworks for designing pricing mechanisms in edge computing. This paper presents a comprehensive survey of pricing strategies that leverage both auction and game theory, systematically categorizing existing research based on auction mechanisms and game-theoretic models. Unlike previous surveys that focus solely on auction-based pricing, this work integrates game-theoretic perspectives, providing a holistic analysis of strategic interactions in edge computing resource allocation. Furthermore, we discuss key challenges, emerging trends, and future research directions aimed at enhancing efficiency, fairness, and privacy protection in cloud-edge collaborative computing environments. © 2014 IEEE.

关键词： Mobile edge computing

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BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised Applications

arXiv

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arXiv 2023年

作者： Lin, Jiatai Han, Guoqiang Xu, Xuemiao Liang, Changhong Wong, Tien-Tsin Chen, C.L. Philip Liu, Zaiyi Han, Chu The School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510006 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Southern Medical University Guangzhou510080 China School of Computer Science and Engineering South China University of Technology State Key Laboratory of Subtropical Building Science Ministry of Education Key Laboratory of Big Data and Intelligent Robot Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Class activation mapping (CAM), a visualization technique for interpreting deep learning models, is now commonly used for weakly supervised semantic segmentation (WSSS) and object localization (WSOL). It is the weighted aggregation of the feature maps by activating the high class-relevance ones. Current CAM methods achieve it relying on the training outcomes, such as predicted scores (forward information), gradients (backward information), etc. However, when with small-scale data, unstable training may lead to less effective model outcomes and generate unreliable weights, finally resulting in incorrect activation and noisy CAM seeds. In this paper, we propose an outcome-agnostic CAM approach, called BroadCAM, for small-scale weakly supervised applications. Since broad learning system (BLS) is independent to the model learning, BroadCAM can avoid the weights being affected by the unreliable model outcomes when with small-scale data. By evaluating BroadCAM on VOC2012 (natural images) and BCSS-WSSS (medical images) for WSSS and OpenImages30k for WSOL, BroadCAM demonstrates superior performance than existing CAM methods with small-scale data (less than 5%) in different CNN architectures. It also achieves SOTA performance with large-scale training data. Extensive qualitative comparisons are conducted to demonstrate how BroadCAM activates the high class-relevance feature maps and generates reliable CAMs when with small-scale training data. © 2023, CC BY.

关键词： Learning systems

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Fusion of terahertz spectroscopy and Raman spectroscopy combined with support vector machine to distinguish different pericarpium citri reticulatae species

引用

Heliyon 2025年第6期11卷

作者： Yang, Qiyu Xia, Congzi Cui, Yuanwu Zhang, Xiaoqiuyan Zhang, Tianyu Huang, Yong Liu, Diwei Wei, Yanyu Gong, Yubin Fang, Guangyou Wang, Zhenyou Chen, Xuequan Hu, Min Terahertz Center School of Electronic Science and Engineering University of Electronic Science and Technology Sichuan 611731 China Key Laboratory of Terahertz Technology Ministry of Education Sichuan 611731 China Institute of Big Data for Vocational Education Guangdong Polytechnic of Science and Technology Zhuhai 519000 China GBA Branch of Aerospace Information Research Institute Chinese Academy of Sciences Guangzhou 510700 China Guangdong Provincial Key Laboratory of Terahertz Quantum Electromagnetics Guangzhou 510700 China Shenzhen Authentic Traditional Chinese Medicine Treatment Hospital Shenzhen 518100 China

Pericarpium citri reticulatae (PCR), or Chenpi, is a valuable medicinal and food resource in China, but its classification can be challenging due to variations in medicinal value and market price. Traditional methods for identifying PCR involve subjective sensory analysis or cumbersome chemical techniques. This paper explores a new approach by combining terahertz and Raman spectral data with machine learning to classify seven PCR species of different ages, origins, and artificial skins. We used K-nearest neighbor (KNN), convolutional neural networks (CNN), and support vector machines (SVM) for model selection. The best model achieved 96.8 % accuracy and high AUC metrics. An innovative Manhattan data preprocessing method improved model performance by addressing errors due to instrumental and environmental factors. The T-distributed stochastic neighbor embedding (t-SNE) algorithm visualized data connections, and recursive feature elimination identified key frequencies. The THz band achieved 94.1 % accuracy using just 5.4 % of the original data, and the Raman band reached 77.8 % accuracy with 10 feature frequencies. This study demonstrates the effectiveness of spectral fusion and machine learning for precise PCR classification, offering valuable applications for traditional Chinese medicine and the food industry. © 2025

关键词： Manhattan distance Pericarpium citri reticulatae Raman spectroscopy Support vector machine Terahertz frequency domain spectroscopy

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QoS-Based Beamforming and Compression Design for Cooperative Cellular Networks via Lagrangian Duality

arXiv

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arXiv 2023年

作者： Fan, Xilai Liu, Ya-Feng Liu, Liang Chang, Tsung-Hui State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Ministry of Education Key Laboratory of Mathematics and Information Networks School of Mathematical Sciences Beijing University of Posts and Telecommunications Beijing100876 China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong School of Science and Engineering The Chinese University of Hong Kong Shenzhen Research Institute of Big Data Shenzhen Shenzhen China

This paper considers the quality-of-service (QoS)based joint beamforming and compression design problem in the downlink cooperative cellular network, where multiple relay-like base stations (BSs), connected to the central processor via rate-limited fronthaul links, cooperatively transmit messages to the users. The problem of interest is formulated as the minimization of the total transmit power of the BSs, subject to all users’ signal-to-interference-plus-noise ratio (SINR) constraints and all BSs’ fronthaul rate constraints. In this paper, we first show that there is no duality gap between the considered joint optimization problem and its Lagrangian dual by showing the tightness of its semidefinite relaxation (SDR). Then, we propose an efficient algorithm based on the above duality result for solving the considered problem. The proposed algorithm judiciously exploits the special structure of an enhanced Karush-Kuhn-Tucker (KKT) conditions of the considered problem and approaches the solution that satisfies the enhanced KKT conditions via two fixed point iterations. Two key features of the proposed algorithm are: (1) it is able to detect whether the considered problem is feasible or not and find its globally optimal solution when it is feasible;(2) it is highly efficient because both of the fixed point iterations in the proposed algorithm are linearly convergent and function evaluations in the fixed point iterations are computationally cheap. Numerical results show the global optimality and efficiency of the proposed algorithm. Copyright © 2023, The Authors. All rights reserved.

关键词： Lagrange multipliers

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Image super resolution via multi-regularization combining hybrid Tikhonov-TV prior and deep denoiser prior

Image super resolution via multi-regularization combining hy...

引用

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Jiahao Zhang Shengrong Zhao Hu Liang Changchun Wen Chen Liang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

In a real scenario, the image is often corrupted by complex degradation, and a lot of useful information is lost, which makes super-resolution (SR) reconstruction seriously ill-posed. To effectively solve such a problem, it is crucial to correctly exploit image prior knowledge. Although existing deep learning-based methods can obtain excellent results, they cannot deal with the complex degradation effectively, which would lead to the loss of texture details and the destruction of edge details. In this paper, an efficient multi-regularization method for SR is proposed, which can simultaneously exploit both internal and external image priors within a unified framework. The hybrid Tikhonov-TV prior and deep denoiser prior are introduced to constrain the reconstruction process. That is, the proposed model combines the superiority of the piecewise-smooth prior and deep prior. Moreover, an adaptive weight parameter is employed to make the hybrid component more detail-preserving. Experimental demonstrate that the proposed method achieves better performance in image detail protection than advanced methods.

关键词：

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A Survey on Applications of Reinforcement Learning in Spatial Resource Allocation

arXiv

引用

arXiv 2024年

作者： Zhang, Di Wang, Moyang Mango, Joseph Li, Xiang Xu, Xianrui Key Laboratory of Geographic Information Science Ministry of Education School of Geographic Sciences East China Normal University Shanghai200241 China Department of Transportation and Geotechnical Engineering University of Dar es Salaam Dar es salaam Tanzania United Republic of Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration East China Normal University Shanghai200241 China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources East China Normal University Shanghai200241 China School of Economics and Management Shanghai University of Sport Shanghai200438 China

The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase, traditional algorithms face significant computational pressures, struggling to achieve optimal efficiency and real-time capabilities. In recent years, with the escalating computational power of computers, the remarkable achievements of reinforcement learning in domains like Go and robotics have demonstrated its robust learning and sequential decision-making capabilities. Given these advancements, there has been a surge in novel methods employing reinforcement learning to tackle spatial resource allocation problems. These methods exhibit advantages such as rapid solution convergence and strong model generalization abilities, offering a new perspective on resolving spatial resource allocation problems. Therefore, this paper aims to summarize and review recent theoretical methods and applied research utilizing reinforcement learning to address spatial resource allocation problems. It provides a summary and comprehensive overview of its fundamental principles, related methodologies, and applied research. Additionally, it highlights several unresolved issues that urgently require attention in this direction for the future. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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