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Frontiers Technology of information and computer (ICFTIC), IEEE International Conference on

作者： Mengshi Qi Xin Wang Yunpeng He Ming Yang Xiaoming Wu 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 Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Federated learning (FL) is a decentralized machine learning technique that protects data privacy by trainng models locally on devices and sharing only model updates instead of raw data. However, FL also faces multiple security challenges. Clientside poisoning attacks can compromise global model training by injecting malicious updates. Additionally, the server may infer private information from client model updates. Most existing studies address these two risks separately, lacking a systematic analysis and response to their combined threats. This paper proposes a method called Sliced-Clustering Guard (SCGuard), which slices gradients and performs clustering analysis on each segment, enabling the server to more accurately detect malicious updates and effectively differentiate malicious client updates with differential privacy (DP) protection. Experimental results demonstrate that SCGuard can mitigate poisoning attacks while effectively safeguarding clients' sensitive information.

关键词： Training Privacy Differential privacy Systematics Federated learning Scalability Data models Servers Protection Faces

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Cooperative Caching Strategy Based on Two-Layer Caching Model for Remote Sensing Satellite networks

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computers, Materials & Continua 2023年第5期75卷 3903-3922页

作者： Rui Xu Xiaoqiang Di Hao Luo Hui Qi Xiongwen He Wenping Lei School of Computer Science and Technology Changchun University of Science and TechnologyChangchun130022China School of Information Engineering Henan Institute of Science and TechnologyXinxiang453003China Jilin Province Key Laboratory of Network and Information Security Changchun University of Science and TechnologyChangchun130022China Information Center Changchun University of Science and TechnologyChangchun130022China Beijing Institute of Spacecraft System Engineering Beijing100094China Beijing Institute of Space Mechanic and Electricity Beijing100094China

In information Centric networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite *** up cache space at any node enables users to access data nearby,thus relieving the processing pressure on the ***,the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache *** address the issues mentioned,a cooperative caching strategy(CSTL)for remote sensing satellite networks based on a two-layer caching model is *** two-layer caching model is constructed by setting up separate cache spaces in the satellite network and the ground *** caching of popular contents in the region at the ground station to reduce the access delay of users.A content classification method based on hierarchical division is proposed in the satellite network,and differential probabilistic caching is employed for different levels of *** cached content is also dynamically adjusted by analyzing the subsequent changes in the popularity of the cached *** the two-layer caching model,ground stations and satellite networks collaboratively cache to achieve global planning of cache contents,rationalize the utilization of cache resources,and reduce the propagation delay of remote sensing *** results show that the CSTL strategy not only has a high cache hit ratio compared with other caching strategies but also effectively reduces user request delay and server load,which satisfies the timeliness requirement of remote sensing data transmission.

关键词： information centric networking caching strategy two-layer caching model hierarchical division

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PNRITR:Research on Positive and Negative Entity Region Perception Methods for Image-Text Retrieval 36

PNRITR:Research on Positive and Negative Entity Region Perce...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Zhang, Zhiping Sun, Tao Zheng, Hongyan Liu, Hao Liu, Gengchen Yang, Zhi Wang, Xiaoyu 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

ISBN: (纸本)1891706594

The aim of cross-modal image-text retrieval is to heighten comprehension and to create robust associations between visual and textual content. This process entails a mutual querying and synchronization across various data formats. While conventional techniques have seen enhancements via attention-based approaches, they frequently miss the dynamics between corresponding and non-corresponding entity areas within image-text pairs. These techniques, often fixated on aligning image entities with textual terms, can overlook the congruence of entity characteristics, leading to less accurate localization. This research introduces PNRITR, an innovative strategy for cross-modal retrieval that leverages label learning to discern attributes absent from images, broadening the scope beyond just positive entity areas. PNRITR has delivered notable advancements on the Flickr30K and MS-COCO datasets. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Knowledge based systems

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A BERT-Based Model for Legal Document Proofreading 13th

A BERT-Based Model for Legal Document Proofreading

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13th IFIP TC 12 International Conference on Intelligent information Processing, IIP 2024

作者： Liu, Jinlong Luo, Xudong School of Computer Science and Engineering Guangxi Normal University Guilin541004 China Key Lab of Education Blockchain and Intelligent Technology of Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin541004 China

ISBN: (纸本)9783031578076

Legal documents require high precision and accuracy in language use, leaving no room for grammatical and spelling errors. To address the issue, this paper proposes a novel application of the BERT pre-trained language model for legal document proofreading. The BERT-based model is trained to detect and correct legal texts’ grammatical and spelling errors. On a dataset of annotated legal documents, we experimentally show that our BERT-based model significantly outperforms state-of-the-art proofreading models in precision, recall, and F1 score, showing its potential as a valuable tool in legal document preparation and revision processes. The application of such advanced deep learning techniques could revolutionise the field of legal document proofreading, enhancing accuracy and efficiency. © IFIP International Federation for information Processing 2024.

关键词： Authentication

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Clothed Human Model Estimation from Unseen Partial Point Clouds with Meta-learning 1st

Clothed Human Model Estimation from Unseen Partial Point Cl...

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1st International Artificial Intelligence Conference, IAIC 2023

作者： Fang, Chenghao Wang, Kangkan Yin, Shihao Li, Shaoyuan 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 College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9789819712793

This work aims to address the problems of clothed human reconstruction from unseen partial point clouds. Existing methods focus on estimating vertex offsets on top of parametric models for clothing details but with the limitation of the fixed topology, or reconstructing non-parametric shapes using implicit functions which are lack of semantic information. Moreover, due to limited training data, large variations in clothing details, and domain gaps between training data and real data, these methods often have poor generalization ability on real data. In this paper, we propose a generalizable approach for estimating dressed human models from single-frame partial point clouds based on meta-learning. Specifically, we first learn a meta-model that can efficiently estimate the parameters of unclothed human models from unseen data by a fast fine-tuning. Based on the unclothed human models, we further meta-learn the point-based human models in clothing with local geometric features, which is topologically flexible and rich in human details. Our approach outperforms previous work in terms of reconstruction accuracy, as evidenced by qualitative and quantitative results obtained from a variety of datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Semantics

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RLGNN: Robust Graph Neural networks with Noisy label Learning 22

RLGNN: Robust Graph Neural Networks with Noisy Label Learnin...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Qian, Haodong Li, De Li, Qiyu Wang, Jinyan Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China Guangxi Normal University School of Computer Science and Engineering Guilin541004 China

ISBN: (纸本)9798331509712

Graph Neural networks (GNNs) have become a powerful tool for processing graph-structured data. However, real-world data often faces the problem of label noise and sparsity, which will greatly affect the application of graph neural networks in the real world. Hence, we introduce Robust Graph Neural networks with Noisy label Learning (RLGNN) to tackle the challenges posed by label noise and sparsity in graph-structured data. Its key components include unsupervised contrastive learning for deriving robust node representations, a two-stage clean sample selection mechanism, and a semi-supervised loss function to harness information from unlabeled nodes. RLGNN ingeniously merges contrastive learning with the prediction space, refining the label selection process and boosting the model's robustness and generalizability. Comprehensive experiments indicate that RLGNN significantly surpasses existing models in managing label noise in semi-supervised node classification scenarios. © 2024 IEEE.

关键词： Contrastive Learning

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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Trusted Auditing of Data Operation Behaviors in Cloud based on Blockchain and TEE 21

Trusted Auditing of Data Operation Behaviors in Cloud based ...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xu, Shujiang Wang, Fansheng Wang, Lianhai Mihaljevic, Miodrag J. Zhang, Shuhui Shao, Wei Huang, Qihao Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Mathematical Institute The Serbian Academy of Sciences and Arts Belgrade Serbia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350329223

With the rapid development of the new generation of information technologies, including cloud computing, 5G, and the Internet of Things, data outsourcing storage in the cloud has brought great convenience to data storage or access anytime and anywhere. Due to the separation of ownership and management of outsourced stored data, it faces data security risks, including data loss and personal privacy disclosure. The third-party audit of data operational behavior by logs is often employed to strengthen the security of outsourced data. However, some privacy breach incidents in recent years show that third parties who may leak information about data and users' identities are not always trustworthy. To achieve data security and privacy protection, an operation behavior auditing scheme for outsourced data is proposed with the blockchain rather than third parties. The data operation behavior audit is carried out from three dimensions: integrity of the operation behavior log, the credibility of the operation behavior information, and compliance of operation behavior. The security analysis and experiment results indicate that this scheme is secure and feasible. © 2023 IEEE.

关键词： Blockchain

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Research on the seismic signal classification algorithm based on SE-DenseNet 4

Research on the seismic signal classification algorithm base...

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4th International Conference on Machine Learning and computer Application, ICMLCA 2023

作者： Wang, Yuan Huang, Hanming Ding, Weiwei Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China School of Computer Science and Engineering Guangxi Normal University Guilin541004 China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Addressing the issue of seismic signal classification and recognition for natural earthquakes and artificial blasting, a deep learning model suitable for seismic signal classification and recognition is proposed. In this study, a dataset is created by selecting 14,001 natural earthquake waveforms and 12,870 artificial blasting waveforms from the seismic waveform data provided by the China National Earthquake Science Data Sharing Centre for the years 2010-2016. Based on the DenseNet architecture, an attention mechanism is introduced into the original Dense Block, adaptively enhancing valuable feature channels, thereby improving the model's perception and expression capabilities for important information. The model achieves an accuracy of 96.25% on the test set, surpassing the compared methods. Experimental results demonstrate that the attention-enhanced DenseNet exhibits excellent classification effectiveness and generalization ability when applied to seismic signals. © 2024 SPIE.

关键词： Earthquakes

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FedGAC: Graph Federated Learning with Gradients Aggregation Calibration for Non-IID and Long-Tailed Data 22

FedGAC: Graph Federated Learning with Gradients Aggregation ...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Li, Jie Wang, Jinyan Deng, Rongbin Yan, Dongqi Li, Qiyu Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China Guangxi Normal University School of Computer Science and Engineering Guilin541004 China

ISBN: (纸本)9798331509712

Federated graph learning (FGL) combines the advantages of graph neural networks and federated learning, extracting valuable information from decentralized graph data while preserving data privacy. Existing FGL methods typically consider mitigating the effect of non-IID under the class balanced assumption. However, in many real-world scenarios, the data class distribution often exhibits a long-tailed distribution, which can lead to the model being severely biased towards the head classes, thus affecting the overall performance of the global model. To address the challenges posed by non-IID and long-tailed distribution data in FGL, this paper proposes a method called FedGAC to train a globally balanced model. In the local stage, FedGAC employs feature-structure decoupled graph attention network to train client models, introduces class-importance balanced strategy to mitigate class imbalance issues, and leverages knowledge relearning strategy to enhance model generalization. In the global stage, FedGAC employs an adaptive gradients aggregation strategy that dynamically adjusts balancing factors based on the degree of class imbalance in each client's data, thereby optimizing the aggregation process. Additionally, it utilizes global class gradients to calibrate learnable features. Experimental results demonstrate that, compared to the state-of-the-art federated learning methods, FedGAC achieves superior performance on highly non-IID and long-tailed distribution datasets. © 2024 IEEE.

关键词： Federated learning

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