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Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

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A Multitask Learning Approach for Chinese National Instruments Recognition and Timbre Space Regression 9th

A Multitask Learning Approach for Chinese National Instrumen...

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9th Conference on Sound and Music technology, CSMT 2021

作者： Xu, Shenyang Jiang, Yiliang Li, Zijin Sun, Xiaoheng Li, Wei Central Conservatory of Music Beijing100031 China School of Computer Science and Technology Fudan University Shanghai200438 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9789811947025

Musical instrument recognition is an essential task in the domain of music information retrieval. So far, most existing research are focused on western instruments. In this research, we turn to Chinese national instruments recognition. First, a dataset containing 30 Chinese national instruments is created. Then, a well-designed end-to-end Convolutional Recurrent Neural Network is proposed. Moreover, we combine instrument recognition with instrument timbre space regression using a multitask learning approach to improve performances of both tasks. We conduct experiments in instrument recognition and timbre space regression to evaluate our model and multitask learning approach. Experimental results show that our proposed model outperforms previous algorithms, and the multitask approach can further improve the results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Music

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Data-Driven Crowd Motion Modeling With Group Properties Analysis 25

Data-Driven Crowd Motion Modeling With Group Properties Anal...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Jia, Chuanhua Lu, Dianjie Zhang, Guijuan Liu, Hong School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology State Key Laboratory of High-end Server and Storage Technology Shandong Jinan China

ISBN: (纸本)9781665405270

Traditional crowd evacuation simulation methods focus on the analysis of individual behavior in the crowd, but ignore the analysis of potential group properties in the process of crowd motion, which reduces the visual realism of the crowd evacuation simulation process. During crowd motion, crowds cause unconscious self-organization due to common destinations and social relationships, thus forming groups. Considering the influence of group properties on crowd motion is important to improve the visual realism of crowd motion simulation. To address this problem, we propose a crowd motion modeling method for group properties analysis (GPA-CMM). First, we build a data-driven group properties quantification (DGPQ) model to describe the characteristics of group motion accurately. In the model, we divide the crowd into several groups, extract motion properties of the crowd, and quantify the features of intra-group stability and inter-group conflict. Then, in order to analyze their influences of group properties, we build a stability and conflict based crowd motion analysis (SC-CMA) model. Finally, we implement a crowd simulation system based on SC-CMA, visualize the results of the theoretical analysis in a graphical way. The experimental results show that the method can simulate crowd motion more realistically. © 2022 IEEE.

关键词： Behavioral research

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Collaborative Teams Recruitment Based on Dual Constraints of Willingness and Trust for Crowd Sensing 25

Collaborative Teams Recruitment Based on Dual Constraints of...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Song, Nianyun Lu, Dianjie Shi, Yepeng Zhang, Guijuan Liu, Hong School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology State Key Laboratory of High-end Server and Storage Technology Shandong Jinan China

ISBN: (纸本)9781665405270

As crowd sensing tasks become more complex, it is increasingly needed for participants to form teams to collaborate interactively in order to complete tasks more efficiently. It is valuable to fully understand user behavior, the organizational principles of crowd collaboration teams and incorporate them into the user recruitment process. In fact, there is a two-way process of subjective and objective selection for building a team in crowd sensing. From the participant's perspective, the willingness of users to participate in the task is a subjective factor;from the platform's perspective, the trust relationship between users is an objective factor. However, existing work has not well recognized the value of including collaborative teams in the crowd sensing recruitment process and has not considered the willingness and trust relationships between participants simultaneously, which results in poor quality of service (QoS) among recruited users. To address this problem, we propose a willingness and trust-based collaborative team recruitment method (WT-CTRM). First, we construct a willingness network (WN) and a trust network (TN) to describe the interaction of willingness and trust among the collaborated users respectively. Based on these networks, we then predict the unknown willingness and trust relationships between users for collaboration by using graph convolutional networks (GCNs). Finally, we build collaborative teams for the user recruitment process of crowd sensing based on the consensus constraints of the predicted willingness network and trust network. Simulation results show that the recruitment scheme significantly improves the QoS in task scenarios with collaborative requirements. © 2022 IEEE.

关键词： Quality of service

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An SDN Traffic Engineering Approach Based on Traffic Unsupervised Contrastive Representation and Reinforcement Learning

An SDN Traffic Engineering Approach Based on Traffic Unsuper...

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Next Generation Data-driven Networks (NGDN), International Conference on

作者： Ruiyu Yang Qi Tang Yingya Guo College of Computer and Data Science Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

With the rapid development of Internet technology and the continuous explosive growth of network traffic, Traffic Engineering (TE), as a viable method for optimizing network traffic distribution and improving network performance, attracts widespread attention from both industry and academia. Software Defined Networks (SDN), which decouples the data plane and the control plane, realizes a flexible routing and improves the TE performance. Existing TE approaches in SDN mainly utilize Reinforcement Learning (RL) methods to learn the mapping relationship between network traffic and routing policies. However, due to the continuous expansion of network size and dynamic changes in traffic, the enlargement of traffic state space hinders RL from converging to the optimal routing policy, leading to a decline in network performance. To address these issues, this paper presents a TE method based on unsupervised contrastive representation and RL. This method first shrinks the original traffic state space by efficiently extracting traffic features through Contrastive Learning (CL), aiding quick convergence of RL. It then uses RL to directly learn the mapping from traffic features to traffic splitting policies. Finally, through numerous experiments on real network traffic and topology, it demonstrates that the proposed TE method can effectively achieve load balancing of network traffic under complex and volatile dynamic traffic demands, thereby enhancing network performance.

关键词： Training Network topology Telecommunication traffic Reinforcement learning Feature extraction Routing Load management Topology Software defined networking Next generation networking

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Uncertainty-Aware Modeling for Improving Prediction of Toxicity 23

Uncertainty-Aware Modeling for Improving Prediction of Toxic...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Zhou, Xinlei Wang, Xizhao Zhou, Xinpeng Tu, Siwu Ranwang School of Mathematical Sciences Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen518060 China College of Electronic Information Guangxi Minzu University 530000 China Jiangxi Drug Inspection Center Nanchang330001 China

ISBN: (纸本)9798331528041

Accurately predicting molecular toxicity is challenging in drug discovery and chemical safety assessment. Except for the representation of molecular structure, the inherent class imbalance and uncertainty often complicate the task. In this work, we present UnBGMT (Uncertainty-Aware Balanced Graph Multi-set Transformer), which integrates uncertainty modeling into the Graph Multiset Transformer (GMT) framework to improve the reliability and accuracy of molecular toxicity predictions. Our method employs Monte Carlo dropout (MC dropout) to evaluate uncertainty, which is then used to guide an adaptive loss function that adjusts weights in training. This approach guides the model to focus on the minority and samples with middle-level uncer-tainty, mitigating issues related to class imbalance and improving model stability. We conducted sufficient experiments on the Tox21 dataset, demonstrating the superiority of UnBGMT by comparing performance with baseline models and conducting ablation studies. Our results highlight the benefits of incorporating uncertainty in toxicity prediction, which provides a deeper insight into the im-pact of uncertainty on model performance. © 2024 IEEE.

关键词： Prediction models

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Cognitive Analysis of Medical Decision-Making: An Extended MULTIMOORA-Based Multigranulation Probabilistic Model with Evidential Reasoning

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Cognitive Computation 2024年第6期16卷 3149-3167页

作者： Bai, Wenhui Zhang, Chao Zhai, Yanhui Sangaiah, Arun Kumar Wang, Baoli Li, Wentao School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan School of Engineering and Technology Sunway University Selangor Petaling Jaya47500 Malaysia University Centre for Research and Development Chandigarh University Punjab Mohali140413 India School of Mathematics and Information Technology Yuncheng University Shanxi Yuncheng044000 China College of Artificial Intelligence Southwest University Chongqing400715 China

Cognitive computation has leveraged the capabilities of computer algorithms, rendering it an exceptionally efficient approach for addressing multi-attribute group decision-making (MAGDM) problems. Due to the stability of MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form) and the capability of evidential reasoning (ER) to combine information from multiple sources, the technique of multigranulation probabilistic rough sets (MG PRSs) holds great promise for solving MAGDM problems. Thus, a new and stable method for MAGDM is proposed. Initially, three forms of multigranulation Pythagorean fuzzy probabilistic rough sets (MG PF PRSs) are constructed using MULTIMOORA approaches. Next, the hierarchical clustering method is employed to cluster similar decision information and consolidate the decision-makers’ preferences. Representatives are chosen from each category to simplify information fusion calculations and reduce complexity by reducing the model’s dimensionality. Following that, the rankings obtained from the three methods are fused using ER. Ultimately, the validity of our method is revealed via a case analysis on chickenpox cases from the UCI data set by employing cognitive analysis. The paper outlines a method for MAGDM that provides significant advantages. Specifically, the use of MULTIMOORA improves the stability of decision results, while the incorporation of ER reduces the overall uncertainty of entire decision processes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Granular computing

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Exploring Patent Transformation Event: Forecasting Patent Transfer Time 26

Exploring Patent Transformation Event: Forecasting Patent Tr...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Huo, Jinchen Liu, Weidong Li, Yang Cao, Yan Institute of Scientific and Technical Information of China China College of Computer Science Inner Mongolia University China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350331684

As the largest source of technical information around the world, patents are regarded as an essential crystallization and carrier of knowledge and technological innovation. Patent transformation is conducive not only to enhancing economic efficiency, but also to improving productivity and the rational utilization of resources. There is an imbalance between high patent ownership and low transformation rates. We try to predict the occurrence of transformation events from the patent assignment. However, there are some challenges in predicting patent transformation: (1) how to capture transformation features of patents, especially combined with the transfer time factor. (2) how to predict patent transfer time effectively. To address these challenges, a Patent Transfer Time Forecasting Model (PTTFM) is proposed. The model includes: (1) extraction of time-varying features of patents. (2) the patent transfer time is forecast using a Neural Temporal Point Process. By testing the model on patents under different classifications, the experimental results are obtained to show that the proposed model is applicable to predict the timing of patent assignment within a certain time frame, especially one month. Our work may facilitate patent transformation while interpretability is ensured for transformation events. © 2023 IEEE.

关键词： Forecasting

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Hypergraph-enhanced Dual Semi-supervised Graph Classification 41

Hypergraph-enhanced Dual Semi-supervised Graph Classificatio...

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41st International Conference on Machine Learning, ICML 2024

作者： Ju, Wei Mao, Zhengyang Yi, Siyu Qin, Yifang Gu, Yiyang Xiao, Zhiping Wang, Yifan Luo, Xiao Zhang, Ming School of Computer Science National Key Laboratory for Multimedia Information Processing Peking University-Anker Embodied AI Lab Peking University China School of Statistics and Data Science Nankai University China Department of Computer Science University of California Los Angeles United States School of Information Technology & Management University of International Business and Economics China

In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unlabeled graphs. Despite the promising capability of graph neural networks (GNNs), they typically require a large number of costly labeled graphs, while a wealth of unlabeled graphs fail to be effectively utilized. Moreover, GNNs are inherently limited to encoding local neighborhood information using message-passing mechanisms, thus lacking the ability to model higher-order dependencies among nodes. To tackle these challenges, we propose a Hypergraph-Enhanced DuAL framework named HEAL for semi-supervised graph classification, which captures graph semantics from the perspective of the hypergraph and the line graph, respectively. Specifically, to better explore the higher-order relationships among nodes, we design a hypergraph structure learning to adaptively learn complex node dependencies beyond pairwise relations. Meanwhile, based on the learned hypergraph, we introduce a line graph to capture the interaction between hyperedges, thereby better mining the underlying semantic structures. Finally, we develop a relational consistency learning to facilitate knowledge transfer between the two branches and provide better mutual guidance. Extensive experiments on real-world graph datasets verify the effectiveness of the proposed method against existing state-of-the-art methods. Copyright 2024 by the author(s)

关键词： Knowledge graph

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On-chip terahertz orbital angular momentum demultiplexer

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Photonics Research 2024年第5期12卷 1044-1054页

作者： XIAOHAN JIANG WANYING LIU QUAN XU YUANHAO LANG YIKAI FU FAN HUANG HAITAO DAI YANFENG LI XUEQIAN ZHANG JIANQIANG GU JIAGUANG HAN WEILI ZHANG Center for Terahertz Waves College of Precision Instrument and Optoelectronics Engineeringand Key Laboratory of Optoelectronics Information and Technology(Ministry of Education)Tianjin UniversityTianjin 300072China Tianjin Key Laboratory of Low Dimensional Materials Physics and Preparing Technology School of ScienceTianjin UniversityTianjin 300072China Guangxi Key Laboratory of Optoelectronic Information Processing School of Optoelectronic EngineeringGuilin University of Electronic TechnologyGuilin 541004China School of Electrical and Computer Engineering Oklahoma State UniversityStillwaterOklahoma 74078USA

The terahertz regime is widely recognized as a fundamental domain with significant potential to address the demands of next-generation wireless *** parallel,mode division multiplexing based on orbital angular momentum(OAM)shows promise in enhancing bandwidth utilization,thereby expanding the overall communication channel *** this study,we present both theoretical and experimental demonstrations of an on-chip terahertz OAM *** device effectively couples and steers seven incident terahertz vortex beams into distinct high-quality focusing surface plasmonic beams,and the focusing directions can be arbitrarily *** proposed design strategy integrates space-to-chip mode conversion,OAM recognition,and on-chip routing in a compact space with subwavelength thickness,exhibiting versatility and superior performance.

关键词： demultiplexer momentum angular

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