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DTGCN: a method combining dependency tree and graph convolutional networks for Chinese long-interval named entity relationship extraction

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13361-13373页

作者： Li, Taoyuan Ma, Liangli Qin, Jiwei Ren, Wei College of Electronic Engineering Naval University of Engineering Wuhan China School of Computer Science China University of Geosciences Wuhan China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China

The extraction of relationships between military entities and descriptive entities is a key task for automated construction of military knowledge graphs. This category of text unstructured imposed problems such as unfixed entity length, possibly overlong intervals between entities, and excessive pronouns. Many studies rely on traditional pattern matching and statistical learning methods. Thanks to the expansion of data volume, deep learning methods are generally used to address similar problems. However, the traditional single neural network model lacks the ability to extract the relationship information of long-interval entities and cannot tackle the above problems well. To solve these problems, a new named entity relationship extraction model called DTGCN is proposed combining dependency tree and GCN (Graph Convolutional networks). By comparing with traditional models, the experimental results demonstrate that the method proposed in this paper can enhance F1-value by 2.28–11.11% and can provide support for the extraction of Chinese long-interval relationships. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Knowledge graph

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Loc4Plan: Locating Before Planning for Outdoor Vision and Language Navigation 24

Loc4Plan: Locating Before Planning for Outdoor Vision and La...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Tian, Huilin Meng, Jingke Zheng, Wei-Shi Li, Yuan-Ming Yan, Junkai Zhang, Yunong School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangdong Provincial Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9798400706868

Vision and Language Navigation (VLN) is a challenging task that requires agents to understand instructions and navigate to the destination in a visual environment. One of the key challenges in outdoor VLN is keeping track of which part of the instruction was completed. To alleviate this problem, previous works mainly focus on grounding the natural language to the visual input, but neglecting the crucial role of the agent's spatial position information in the grounding process. In this work, we first explore the substantial effect of spatial position locating on the grounding of outdoor VLN, drawing inspiration from human navigation. In real-world navigation scenarios, before planning a path to the destination, humans typically need to figure out their current location. This observation underscores the pivotal role of spatial localization in the navigation process. In this work, we introduce a novel framework, Locating before Planning (Loc4Plan), designed to incorporate spatial perception for action planning in outdoor VLN tasks. The main idea behind Loc4Plan is to perform the spatial localization before planning a decision action based on corresponding guidance, which comprises a block-aware spatial locating (BAL) module and a spatial-aware action planning (SAP) module. Specifically, to help the agent perceive its spatial location in the environment, we propose to learn a position predictor that measures how far the agent is from the next intersection for reflecting its position, which is achieved by the BAL module. After this locating process, we propose the PSA module to associate visual observations After the locating process, we propose the SAP module to incorporate spatial information to ground the corresponding guidance and enhance the precision of action planning. Extensive experiments on the Touchdown and map2seq datasets show that the proposed Loc4Plan outperforms the SOTA methods. © 2024 ACM.

关键词： Visual languages

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Decoy-state quantum-key-distribution-based quantum private query with error tolerance bound

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Physical Review A 2024年第5期109卷 052442-052442页

作者： Bin Liu Zhonghao Liang Wei Huang Fei Gao Jiayi Yuan Bingjie Xu College of Computer Science The State Key Laboratory of Networking and Switching Technology Center for Network and Information Science and Technology on Communication Security Laboratory Institute of Southwestern Communication Chengdu 610041 China

Quantum private query (QPQ) faces many challenges in practical applications. At present, some scholars have made substantial work on overcoming channel loss, channel noise, and nonideal light sources, respectively. However, a protocol that would overcome all three of these problems has yet to materialize. We review a practical QPQ protocol that can actually work in noisy channels. This protocol has an upper bound on tolerable errors based on the required level of security and reliability. Then, we study the security of the above QPQ protocol under weak coherent pulses. The results indicate that the multiphoton pulses have induced significant vulnerabilities, which seriously threatens the privacy of users. Finally, we propose a decoy-state method to solve the serious threat to user security caused by multiphoton pulses. The analysis shows that the decoy-state method significantly improves the security of the QPQ protocol under weak coherent pulses. The improved protocol can not only tolerate transmission losses and channel noise, but also overcome the security vulnerability caused by a nonideal light source.

关键词： Quantum communication Quantum error correction Quantum information theory Quantum protocols Quantum state transfer

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A Fine-Grained Ownership Transfer Protocol for Cloud EMRs Auditing 24th

A Fine-Grained Ownership Transfer Protocol for Cloud EMRs A...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wang, Yamei Zhang, Yuexin You, Weijing Ma, Yunru Wang, Derui Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou China School of Health Fujian Medical University Fuzhou China CSIRO’s Data61 Melbourne Australia

ISBN: (纸本)9789819615445

In cloud Electronic Medical Records (EMRs), a user’s medical records typically comprise various types of health-related sensitive data. When data ownership needs to be transferred, it is preferred to transfer the required data to target users, instead of transferring the entire records. Existing protocols cannot adequately address this issue. Therefore, we leverage the searchable encryption and propose a fine-grained ownership transfer protocol. Specifically, users in our protocol determine data blocks for ownership transfer based on keywords. Additionally, our protocol is designed for single-owner to multi-owner scenario, it is achieved by leveraging characteristics of threshold signatures and multi-signatures. The employment of threshold signatures and multi-signatures realizes flexible and less resource-intensive ownership transfer. Furthermore, we experimentally analyse the performance of our protocol, and compare it with relevant protocols. According to the comparisons, our protocol has more features with moderate resource consumptions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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PixelFade: Privacy-preserving Person Re-identification with Noise-guided Progressive Replacement 24

PixelFade: Privacy-preserving Person Re-identification with ...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Delong Peng, Yi-Xing Wu, Xiao-Ming Wu, Ancong Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9798400706868

Online person re-identification services face privacy breaches from potential data leakage and recovery attacks, exposing cloud-stored images to malicious attackers and triggering public concern. The privacy protection of pedestrian images is crucial. Previous privacy-preserving person re-identification methods are unable to resist recovery attacks and compromise accuracy. In this paper, we propose an iterative method (PixelFade) to optimize pedestrian images into noise-like images to resist recovery attacks. We first give an in-depth study of protected images from previous privacy methods, which reveal that the chaos of protected images can disrupt the learning of recovery models. Accordingly, Specifically, we propose Noise-guided Objective Function with the feature constraints of a specific authorization model, optimizing pedestrian images to normal-distributed noise images while preserving their original identity information as per the authorization model. To solve the above non-convex optimization problem, we propose a heuristic optimization algorithm that alternately performs the Constraint Operation and the Partial Replacement Operation. This strategy not only safeguards that original pixels are replaced with noises to protect privacy, but also guides the images towards an improved optimization direction to effectively preserve discriminative features. Extensive experiments demonstrate that our PixelFade outperforms previous methods in resisting recovery attacks and Re-ID performance. © 2024 ACM.

关键词： Differential privacy

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FLEER: A Federated Learning Framework for EEG Emotion Recognition 5

FLEER: A Federated Learning Framework for EEG Emotion Recogn...

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5th International Seminar on Artificial Intelligence, networking and information Technology, AINIT 2024

作者： Li, Mingming Tang, Xianglei School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Zhejiang Electronic Information Products Inspection and Research Institute Key Laboratory of Information Security of Zhejiang Province Hangzhou China

ISBN: (纸本)9798350385557

The success of deep learning (DL) methods in the Brain-computer Interface (BCI) field for emotion recognition of electroencephalographic (EEG) recordings has been limited due to the scarcity of large datasets. Individual difference and privacy concern restrict the feasibility of creating a large EEG-BCI dataset through merging multiple small datasets for the collaborative training of machine learning models. To solve this problem, we propose a personalized federated learning framework based on the common feature for EEG emotion recognition classification network (FLEER). FLEER learns user's common feature representation through deep learning methods and generates the personalized model for each user. We assess the effectiveness of the proposed architecture on the DEAP dataset for 4-class emotion recognition classification. The experimental results show FLEER provides an effective method for EEG emotion recognition classification while avoiding the actual data sharing and its results are comparable to the baseline in intra-subject. Additionally, our model provides 2 % better accuracy compared to other DL architectures in inter-subject. © 2024 IEEE.

关键词： Emotion Recognition

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GHTO: Improving Byzantine Robustness in Distributed Machine Learning Through Gradient Heterogeneity Twofold Optimization 6

GHTO: Improving Byzantine Robustness in Distributed Machine ...

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6th International Conference on Frontier Technologies of information and computer, ICFTIC 2024

作者： Li, Caiyun Yang, Ming Cao, Jian Wu, Xiaoming He, Yunpeng Wang, Xin Jinan250014 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China Shandong Evayinfo Technology Co. Ltd Jinan250101 China

ISBN: (纸本)9798331541750

Heterogeneous datasets induce local gradient variance and global gradient variance in distributed learning, collectively referred to as gradient heterogeneity. Gradient heterogeneity increases the system's vulnerability to Byzantine attacks, affecting performance. To address this issue, the paper proposes a Gradient Heterogeneity Twofold Optimization (GHTO) algorithm to enhance the Byzantine robustness of the system in non-independent and identically distributed environments. Specifically, a Local Gradient Correction (LGC) algorithm is proposed, which aims to reduce the local gradient variance. By further integrating LGC with momentum and bucketing techniques to achieve dual optimization of local and global gradient variance. Experimental results demonstrate the proposed LGC algorithm diminishes local gradient variance, and the GHTO method optimizes both local and global gradient variance, which further strengthens the robustness of the system against Byzantine attacks. © 2024 IEEE.

关键词： Adversarial machine learning

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A Trusted Privacy-Preserving Model for Cross-Chain Transactions Based on zk_SNARKs

A Trusted Privacy-Preserving Model for Cross-Chain Transacti...

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2023 International Conference on networking and network Applications, NaNA 2023

作者： Wang, Yichuan Tie, Jianhuan Hei, Xinhong Zhao, Li Zhang, Xiaohui School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technolog Xi'an China

ISBN: (纸本)9798350327380

Currently, the privacy protection technology of blockchain is not mature enough, such as user data leakage and lack of anonymity exist, and these problems are especially serious when conducting cross-chain transactions. In this paper, we propose a method of cross-chain transactions based on zk_SNARKs in a trusted environment for cross-chain transactions for privacy protection. Cross-chain transactions are performed through the features of zk_SNARKs such as efficiency, privacy, and verifiability. zk_SNARKs needs to generate trusted settings with the help of a third party during the transaction, and the process has serious privacy issues, and the private parameters generated during the transaction may be maliciously attacked and thus obtained by attackers. To address this problem, we use a hardware technology SGX for trusted computing to encrypt and store some private parameters generated by zk_SNARKs, thus ensuring the security of cross-chain transactions. The experimental results show that the privacy protection scheme using SGX for cross-chain transactions takes less time than the traditional transaction privacy protection, with a verification time of 1.2s for a number of 10 sidechains, and is more efficient than the traditional cross-chain transaction privacy protection. © 2023 IEEE.

关键词： Blockchain

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Confidence-Guided Feature Alignment for Cloth-Changing Person Re-identification 27th

Confidence-Guided Feature Alignment for Cloth-Changing Pers...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Huang, Sirong Zheng, Huicheng School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9783031781094

Much progress has been made in the field of person re-identification, but changes in clothing have hindered the practical application of long-term person re-identification. Cloth-changing person re-identification (CC-ReID) aims to address this problem, with the main challenge being the extraction of discriminative features unrelated to clothing. Existing methods, which mainly focus on introducing clothing-irrelevant cues such as key points, contours, and 3D shapes, require additional modules for feature extraction, resulting in increased complexity and potential inaccuracies due to dependence on the performance of external models. Few studies directly use the original RGB images to make the model constantly focus on clothing-independent information. In this paper, we propose a Confidence-Guided Feature Alignment network (CGFA) for CC-ReID. Specifically, we design a confidence module that automatically learns to make confidence adjustments to fine-grained information, prompting the model to mine clothing-independent discriminative features without introducing other modal cues. By transferring knowledge, we encourage the model to learn discriminative identity features that are independent of clothing bias. As a result, the confidence module can be removed during the inference phase. The proposed simple but efficient method uses only RGB modality without additional cues, and can serve as a powerful baseline for CC-ReID to drive future research. Extensive experiments on the CC-ReID datasets demonstrate the effectiveness of the proposed method, which achieves state-of-the-art performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Feature extraction

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A Short-Term Electric Load Forecasting Method Based on the Fusion of Global and Local Features 9

A Short-Term Electric Load Forecasting Method Based on the F...

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

作者： Gu, Haozhan Tian, Hangwei Wang, Jingchun Li, Yumei Shandong University Of Arts Academy of Fine Arts China 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 Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

Accurate short-term electric load forecasting is crucial for ensuring the stability and efficiency of the power grid. This study proposes an innovative dual-branch electric load forecasting model that enhances the accuracy of predictions by processing global and local features in parallel. Global features are extracted through the Savitzky-Golay filter and a linear model to capture the overall trends of the load data. Local features are identified using the Convolutional Self-Attention Module, which integrates the local perception capabilities of Convolutional neural network with the self-attention mechanism of Transformers. By employing a Weighted Feature Fusion network, we effectively integrate global and local information to enhance the performance of load forecasting. Experimental results demonstrate that the proposed model significantly outperforms existing models in predictive performance. The model presented in this study offers a new perspective on electric load forecasting and has the potential to become an effective tool for power system planning and scheduling. © 2024 IEEE.

关键词： Deep learning Feature fusion Load prediction Time series data analysis

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