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2020 10th International Workshop on computer Science and Engineering, WCSE 2020

作者： Jiang, Quan Qu, Bin Li, Siyu Wang, Li-E Li, Xianxian Guangxi Key Lab of Multi-Source Information Mining and Security College of Computer Science and Information Engineering Guangxi Normal University Guilin China

ISBN: (纸本)9789811447877

Blockchain is a new type of distributed data storage technology. With the rapid development of blockchain, vast amounts of data have been accumulated in these applications, which provide researchers with unprecedented opportunities to analyze blockchain data. However, if blockchain data is published openly, it may cause privacy leaks. Owing to the characteristics of blockchain data, the traditional anonymous method based on data publishing cannot be directly applied to blockchain data. This paper proposes an anonymous method based on clustering named clustering partition based on Bisecting k-medoids (CP-BK). We treat the transaction data of the blockchain as table data and use the k-anonymity model to protect the identity privacy of blockchain users. Finally, we evaluated the information loss and efficiency of this algorithm in experiments. © WCSE 2020.

关键词： Blockchain

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A Novel Blockchain-Based Revocable Permission Management on CP-ABE Scheme 7th

A Novel Blockchain-Based Revocable Permission Management on ...

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7th International Conference on Artificial Intelligence and security, ICAIS 2021

作者： Xv, Qianchuan Song, Yubo Li, Xuan Hu, Aiqun Wang, Junbo Key Laboratory of Computer Networking Technology of Jiangsu Province Nanjing211189 China Purple Mountain Laboratory of Network Communication and Security Nanjing211189 China School of Information Science and Engineering Nanjing211189 China National Mobile Communications Research Laboratory Nanjing211189 China

ISBN: (纸本)9783030786205

Relying on the characteristics of decentralized, tamper-proof, and traceable, blockchain technology has begun to be widely used in permission management. It can be used for access control by using smart contracts to distribute tokens. But also because of the immutability of the blockchain, it cannot dynamically revoke permission. This paper proposed an on-chain user permission management mechanism that could support automatic and active permission revocation. This model proposed an extended CP-ABE chain user permission management model, which realized the permission grant based on user attributes by smart contracts. Furthermore, on this basis, an extended CP-ABE permission revocation mechanism was proposed. The timestamp technology was used to realize the automatic revocation of permission, and the subset coverage algorithm was used to realize the active revocation of permission. A blockchain-based permission distribution model was built, and the permission revocation scheme was introduced into it to conduct experiments. Its performance was tested and analyzed. The experimental results showed that when the number of permission distributions is 1000, the delay is about 5 s. At the same time, the permission change is above 130TPS, which met actual application requirements. © 2021, Springer Nature Switzerland AG.

关键词： Blockchain

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Demand Response Scheme: Achieving Sustainability in Distributed Data Processing

Demand Response Scheme: Achieving Sustainability in Distribu...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Xueying Zhai Yunfeng Peng Xiuping Guo Liang Zhang Wei Zhang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Guizhou Key Laboratory of Electric Power Big Data Guizhou Institute of Technology Guizhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. Data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization.

关键词： Renewable energy sources Energy consumption Costs Electricity Simulation Carbon dioxide Spatiotemporal phenomena Internet of Things Sustainable development Optimization

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Learning Fair and Efficient Multiple Access Schemes with Decomposed MADDPG

Learning Fair and Efficient Multiple Access Schemes with Dec...

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Future Communications and networks (FCN), International Conference on

作者： Zhaoyang Liu Haoxin Lin Xijun Wang Jie Gong Xiang Chen School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin China State Key Lab of Novel Software Technology Nanjing University Nanjing China Guangdong Key Laboratory of Information Security Technology School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350396034

ISBN: (纸本)9798350396041

With the growing demand for wireless communication networks, achieving efficient and equitable channel access schemes has become paramount. In this paper, we delve into the realm of distributed channel access in homogeneous wireless networks, focusing on both fairness and efficiency. We introduce a media access control protocol, named Fair and Efficient Multiple Access (FEMA), by leveraging decomposed Multi-Agent Deep Deterministic Policy Gradient (MADDPG). FEMA decouples the dual objectives into self-oriented efficiency reward and team-oriented fairness reward. Correspondingly, local critic networks and global critic networks target self-oriented reward and team-oriented reward, respectively. Extensive simulations demonstrate FEMA’s superiority over conventional MAC protocols and other multiple access techniques based on deep reinforcement learning, showcasing its ability to simultaneously enhance fairness and efficiency.

关键词： Protocols Wireless networks Focusing Media Access Protocol Dynamic scheduling Deep reinforcement learning Resource management

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Rack-Aware Minimum Storage Partially Cooperative Regenerating Codes with Small Sub-Packetization

arXiv

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arXiv 2025年

作者： Zhao, Hengming Wu, Dianhua Cheng, Minquan School of Computer Science and Engineering Guangxi Normal University Guilin541004 China School of Mathematics and Statistics Nanning Normal University Nanning530100 China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China School of Mathematics and Statistics Guangxi Normal University Guilin541006 China

In the rack-aware model, there are n¯ racks each of which has u nodes with the same storage capacity. Assume that there are h failed nodes uniformly distributed in h¯ host racks (defined as racks containing failed nodes), each rack containing h/h¯ failed nodes where h is divisible by h¯. Then together with its internal helper nodes, each host rack downloads recovery data from d¯ helper racks and repairs its failed nodes. The repair bandwidth is defined as the total inter-rack data transfer required for failures recovery, as the intra-rack communication does not contribute to this cost. The full cooperative repair model requires that each host rack must exchange the data with all the other h¯ host racks during the cooperative repair phase. However, in the partial cooperative repair model, each host rack only needs to exchange data with h¯ − δ (1 ≤ δ ≤ h¯ − 1) other host racks, during the cooperative repair phase. In this paper, we focus on the rack-aware minimum storage partially cooperative regenerating (MSPCR) codes for repairing the h node failures. We first derive the lower bound on the repair bandwidth for rack-aware MSPCR codes using extremal combinatorics, and then construct two classes of optimal repair schemes for rack-aware MSPCR codes with small sub-packetization level. In particular, when δ = 1, our second codes reduce to rack-aware minimum-storage cooperative regenerating (MSCR) codes, while achieving an (h¯ + 1)-fold reduction in sub-packetization level compared to known rack-aware MSCR codes. Copyright © 2025, The Authors. All rights reserved.

关键词： Static random access storage

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Improving the Transmission Rate by A Two-Phase Hybrid Duplex Scheme for Gaussian Relay Channel

Improving the Transmission Rate by A Two-Phase Hybrid Duplex...

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IEEE International Conference on Communications (ICC)

作者： Jianxin Duan Zhengchuan Chen Zhong Tian Min Wang Li Zhen Dapeng O. Wu Tony Q. S. Quek School of Microelectronics and Communication Engineering. Chongquing University Chongqing China Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications Xi'an China School of Optoelectronics Engineering Chongqing University of Posts and Telecommunications Chongqing China Department of Computer Science City University of Hong Kong Hong Kong Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Combining half-duplex (HD) and full-duplex (FD) is promising in improving the information transmission rate of relay channels. This work proposes a novel two-phase hybrid duplex scheme for Gaussian relay channel where the relay operates in FD mode for a fraction of time and only transmits information for the rest of time. The achievable rate of the proposed hybrid duplex scheme is characterized in detail. Based on the obtained result, a joint time division and power allocation problem is formulated to maximize the achievable rate. In particular, the formulated problem is solved through a two-step optimization method. Firstly, the optimal relay power allocation is obtained for given time division factors. Then, the achievable rate maximization problem is addressed by finding the optimal time division factors. The closed-form expression for the maximal achievable rate is derived for some specific cases. Numerical results show that the proposed two-phase hybrid duplex scheme significantly improves the achievable rate of Gaussian relay channel compared with existing benchmark schemes.

关键词： Closed-form solutions Half-duplex system Optimization methods information processing Full-duplex system Benchmark testing Resource management

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Point Cloud Registration-Driven Robust Feature Matching for 3D Siamese Object Tracking

arXiv

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arXiv 2022年

作者： Jiang, Haobo Lan, Kaihao Hui, Le Li, Guangyu Xie, Jin Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Learning robust feature matching between the template and search area is crucial for 3D Siamese tracking. The core of Siamese feature matching is how to assign high feature similarity on the corresponding points between the template and search area for precise object localization. In this paper, we propose a novel point cloud registration-driven Siamese tracking framework, with the intuition that spatially aligned corresponding points (via 3D registration) tend to achieve consistent feature representations. Specifically, our method consists of two modules, including a tracking-specific nonlocal registration module and a registration-aided Sinkhorn template-feature aggregation module. The registration module targets at the precise spatial alignment between the template and search area. The tracking-specific spatial distance constraint is proposed to refine the cross-attention weights in the nonlocal module for discriminative feature learning. Then, we use the weighted SVD to compute the rigid transformation between the template and search area, and align them to achieve the desired spatially aligned corresponding points. For the feature aggregation model, we formulate the feature matching between the transformed template and search area as an optimal transport problem and utilize the Sinkhorn optimization to search for the outlier-robust matching solution. Also, a registration-aided spatial distance map is built to improve the matching robustness in indistinguishable regions (e.g., smooth surface). Finally, guided by the obtained feature matching map, we aggregate the target information from the template into the search area to construct the target-specific feature, which is then fed into a CenterPoint-like detection head for object localization. Extensive experiments on KITTI, NuScenes and Waymo datasets verify the effectiveness of our proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Adaptive ELM-Based Time-Constrained Control of Uncertain Robotic Manipulators With Minimum Learning Computation

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IEEE Transactions on Emerging Topics in Computing 2025年

作者： Jin, Xiaozheng Hou, Yuhan Wu, Xiaoming Chi, Jing Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Jinan250013 China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Shandong Jinan250200 China Shandong University of Finance and Economics School of Computing and Artificial Intelligence Shandong Jinan250014 China

This paper studies the extreme learning machine (ELM)-based time-constrained trajectory tracking control of a class of robotic manipulator systems with model/input uncertainties, matched and mismatched disturbances. A uniform robust exact differentiator (URED) is developed to construct a multi-layer virtual control framework of the robotic manipulator system. Under the control framework, adaptive ELM-based control strategies are proposed to suppress the influence of the uncertainties and disturbances layer-by-layer, combining with the minimum learning parameter (MLP) technique which is employed to reduce the calculation complexity of ELM parameters. The significance of this study is that general model/input uncertainties, matched and mismatched disturbances can be effectively compensated within a limited time, so that bounded trajectory tracking of the robotic manipulator can be achieved avoiding the complexity explosion issue. The time-constrained stability of the closed-loop robotic error systems is proved through Lyapunov stability theory. Finally, comparative simulations are employed to display the feasibility and superiority of the designed robust adaptive ELM-based control schemes of a robotic manipulator system. © 2013 IEEE.

关键词： Robust control

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Unsupervised Detailed Human Shape Estimation from Multi-view Color Images 11th

Unsupervised Detailed Human Shape Estimation from Multi-view...

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11th International Conference on Image and Graphics, ICIG 2021

作者： Zheng, Huayu Wang, Kangkan Li, Wei Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Lab for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9783030873578

This paper presents a novel framework to estimate detailed human body shape from color images in an unsupervised manner. It is a challenging task due to factors such as variations in human shapes, occlusion, and cloth details. The existing methods are mainly supervised and require a large number of ground truth real training data which is usually hard to obtain. To solve this problem, we propose an unsupervised detailed human shape estimation method from multi-view color images. Specifically, we first predict the depth map for the source view through robust photometric consistency with different views. Then, we predict the initial SMPL model from the color image and refine it by an iterative error feedback regressor based on point clouds of the predicted depth map. Finally, the refined SMPL model is deformed to fit the details (i.e., clothes and faces) on the point clouds to recover the detailed human shapes which are represented by adding a set of offsets to the SMPL model. The experimental results on different dataset demonstrate that our method outperforms the state-of-the-art methods and achieves higher reconstruction accuracy. © 2021, Springer Nature Switzerland AG.

关键词： Iterative methods

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Toward Fair Graph Neural networks Via Dual-Teacher Knowledge Distillation

arXiv

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arXiv 2024年

作者： Li, Chengyu Cheng, Debo Zhang, Guixian Li, Yi Zhang, Shichao Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guangxi Guilin541004 China UniSA STEM University of South Australia AdelaideSA5095 Australia School of Computer Science and Technology China University of Mining and Technology Jiangsu Xuzhou221116 China

Graph Neural networks (GNNs) have demonstrated strong performance in graph representation learning across various real-world applications. However, they often produce biased predictions caused by sensitive attributes, such as religion or gender, an issue that has been largely overlooked in existing methods. Recently, numerous studies have focused on reducing biases in GNNs. However, these approaches often rely on training with partial data (e.g., using either node features or graph structure alone), which can enhance fairness but frequently compromises model utility due to the limited utilization of available graph information. To address this trade-off, we propose an effective strategy to balance fairness and utility in knowledge distillation. Specifically, we introduce FairDTD, a novel Fair representation learning framework built on Dual-Teacher Distillation, leveraging a causal graph model to guide and optimize the design of the distillation process. Specifically, FairDTD employs two fairness-oriented teacher models: a feature teacher and a structure teacher, to facilitate dual distillation, with the student model learning fairness knowledge from the teachers while also leveraging full data to mitigate utility loss. To enhance information transfer, we incorporate graph-level distillation to provide an indirect supplement of graph information during training, as well as a node-specific temperature module to improve the comprehensive transfer of fair knowledge. Experiments on diverse benchmark datasets demonstrate that FairDTD achieves optimal fairness while preserving high model utility, showcasing its effectiveness in fair representation learning for GNNs. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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