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International Conference on computer Supported Cooperative Work in Design

作者： Yanzhi Zhang Xiaoming Wu Xiangzhi Liu Huomin Dong 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 Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Strengthening the supervision of bidding business is an important measure to optimise the business environment of bidding and ensure fair competition among enterprises. The existing blockchain-based bidding platform has already achieved the uploading of simple business, but with the development of the bidding industry, single-modal data storage is not enough to support the credible traceability of business processes. Therefore, to address the problems of multimodal data uploading, storage space limitation and performance in the bidding field, and combined with the node complexity of the bidding scenario, we design and propose a bidding management platform for cloud-chain convergence(BMPC 3 ), in which ChainMaker serves as the implementation platform. In this alliance chain, we meet different business logic requirements by designing specific smart contracts and storage structures. According to different business data types, the ledger storage method is optimised to meet the on-chain storage requirements of multimodal data such as images, videos, PDFs, etc. Meanwhile, we explore the blockchain architecture and system configuration, and propose an optimisation scheme, which effectively improves the system transaction throughput. The final experiment shows that the transaction throughput of the system can reach 3300TPS, which can meet the application requirements.

关键词： Industries Federated learning Smart contracts Memory Throughput Logic Optimization

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A Traceable Privacy-Preserving Transaction Protocol With Evolutionary Threshold Authentication

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IEEE Internet of Things Journal 2025年第12期12卷 20787-20799页

作者： Xie, Ninghai Li, Jiguo Lin, Chao Zhang, Yichen Shen, Jian Fujian Normal University College of Computer and Cyber Security Fuzhou350117 China School of Mathematics and Statistics Key Laboratory of Analytical Mathematics and Applications Ministry of Education Fujian Fuzhou350117 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China Guangzhou University Guangdong Key Laboratory of Blockchain Security Guangzhou510006 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China

Decentralized payment systems, such as Bitcoin, enable immutable and transparent payments in a distributed manner. To address the issue of user privacy leakage due to transaction transparency, some efforts have enhanced privacy protection in decentralized payment systems. However, the lack of regulatory functions in these systems allows malicious users to engage in illegal activities. To balance user privacy protection and the regulation of malicious users, some efforts have attempted to introduce decentralized agencies. However, they have not considered the issue of agency corruption. In this paper, we propose a new traceable privacy-preserving transaction protocol, which tracks the addresses and transaction amounts of anonymous parties through decentralized institutions. To address the problem of committee corruption, we present a traceable privacy-preserving protocol with evolving threshold authentication based on a distributed random beacon (TPETA-to-DRB), enabling committee member updates. Furthermore, we prove that the protocol is secure under the random oracle model and conduct a comprehensive performance evaluation. © 2014 IEEE.

关键词： Authentication Protocol

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Collaborative Computation Model Based on Dependency Types and Constituent Trees for Aspect-Based Sentiment Analysis

Collaborative Computation Model Based on Dependency Types an...

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International Conference on computer Supported Cooperative Work in Design

作者： Jia Yi Xiaoming Wu Yunfeng Dong Bei Qi Xiangzhi Liu 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 Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Aspect-based sentiment analysis(ABSA) is a fine-grained sentiment classification task that aims to identify the sentiment polarity of specific aspects in a sentence. Graph convolutional networks(GCN) have recently been widely applied for modeling the associations between aspects and opinion words. However, most studies treat the relationships between all words in the graph equally through dependency analysis, without considering their dependency types, which may lead to the inability to distinguish important relationships. On the other hand, dependency trees can only reveal relationships between words and cannot simulate complex sentence relationships, such as conditional, parallel, and contrast relationships, which are crucial for capturing more accurate sentiment relationships for different aspects. To address these challenges, this paper proposes a Collaborative computation model based on Dependency Types and Constituent trees(CDTC) for aspect-based sentiment analysis. Specifically, collaborative computation of dependency type graph convolutional networks and syntax encoders enhances task efficiency. Utilizing dependency types helps distinguish important relationships, while the phrase segmentation and hierarchical structure of constituent trees provide richer syntactic information. Extensive experiments conducted on three datasets demonstrate that our proposed CDTC model achieves state-of-the-art performance.

关键词： Analytical models Sentiment analysis Graph convolutional networks Federated learning Computational modeling Semantics Collaboration

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Collaborative Prediction of Drug-target Interaction using Sequence-based CNN and Transformer

Collaborative Prediction of Drug-target Interaction using Se...

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International Conference on computer Supported Cooperative Work in Design

作者： Huiting Li Weiyu Zhang Yong Shang Wenpeng Lu 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 Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Drug-target interaction (DTI) prediction is a critical step in drug discovery. Deep learning has shown great potential in DTI prediction. In recent years, many sequence-based DTI prediction methods have been proposed. However, the current methods do not fully represent drugs and proteins and ignore the local and global features of these molecules. In response to these challenges, we propose a sequence-based CNN and Transformer collaborative prediction method (SCTDTI) for drug-target interaction. We train and evaluate our proposed approach on two public drug-target datasets, and experimental results show that SCTDTI improves DTI prediction performance compared to state-of-the-art baselines.

关键词： Proteins Drugs Deep learning Federated learning Computational modeling Neural networks Collaboration

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Android Malware Detection Method Based on Graph Convolutional networks

Android Malware Detection Method Based on Graph Convolutiona...

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Artificial Intelligence and Power Systems (AIPS), International Conference on

作者： Qingling Xu Shumian Yang Lijuan Xu Dawei Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) (Shandong Academy of Sciences) Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350392210

ISBN: (纸本)9798350392227

Facing the serious security threats posed by Android malware amidst the rapid development of mobile internet, there is an urgent need to develop more accurate and efficient detection technologies. This paper introduces an innovative Android malware detection solution based on graph neural networks(GCN). The solution centers around function call graphs as core features, incorporating sensitive API identification and node importance analysis to streamline redundant information and reinforce key feature representation. Leveraging graph neural network models, complex call graphs are efficiently encoded into low-dimensional features, revealing the deep structure of malicious code. This method not only significantly improves detection accuracy to 99% but also drastically reduces detection time while ensuring performance, demonstrating its strong advantage in Android malware detection and classification tasks.

关键词： Training Accuracy Costs Graph convolutional networks Biological system modeling Feature extraction Malware Power systems Complexity theory Security

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An improved Schnorr-based multi-signature scheme with application to blockchain 3

An improved Schnorr-based multi-signature scheme with applic...

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3rd IEEE International Conference on Civil Aviation Safety and information Technology, ICCASIT 2021

作者： Li, Chunlan Wu, Yulong Yu, Feng Computer Science and Engineering School of Software Guangxi Normal University Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

ISBN: (纸本)9781665425186

A multi-signature scheme allows a group of signers to jointly generate a signature on a common message, which can be verified by anyone, making the signature more compact. Multi-signature has the advantage of fast verification, and it helps reduce bandwidth and the cost of validation and storage. Recently, the multi-signature has drawn much attention because its advantages are desirable for blockchain and cryptocurrency. However, the traditional Schnorr-based multisignature scheme suffers from an excessive number of communications. This paper proposes an improved Schnorr-based multi-signature scheme ASMS, which reduces one round of communication in a traditional scheme, supporting public key aggregation. Thus, it is suitable for e-business and e-government affairs scenarios. As an application, we implemented our solution in Fabric, an enterprise blockchain platform, using chaincode technology. © 2021 IEEE

关键词： Blockchain

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Knowledge Distillation Using Global Fusion and Feature Restoration for Industrial Defect Detectors

Knowledge Distillation Using Global Fusion and Feature Resto...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zhengqian Feng Xiyao Yue Wang Li Mingle Zhou Delong Han Gang Li SHANDONG SCICOM Information and Economy Research Institute CO. . Ltd 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 Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Deep learning technology has been widely applied in industrial quality inspection tasks to improve the accuracy of defect detection, object recognition, and classification. However, in the task of object detection, both feature-based and regression-based traditional knowledge distillation methods impose overly strict constraints on the student model. To address the above issues, this article proposes Global Fusion and Feature Restoration Knowledge Distillation(FRD). FRD integrates contextual information into the channel through a Global Fusion Module (GFM), and further utilizes attention mechanisms to adaptively focus on the distillation region after separating the foreground background. FRD also uses a Mask Feature Restoration (MFR) to mask and restore a portion of student features, improving the learnability of the model. At the same time, FRD adopts a comprehensive approach of multiple loss superposition constraints, rather than simply using MSE losses to imitate the features of teachers. Experiments have shown that FRD can effectively improve model performance in industrial defect detection tasks. On the aluminum surface defect dataset, FRD increased the mAP index of RetinaNet-Res50 from 57.7% to 61.7%. We also confirmed the effectiveness of FRD for general object detection on the Coco dataset. On a randomly selected COCO dataset containing 4000 images, FRD increased the mAP metric of RetinaNet-Res50 from 21.5% to 43.6%.

关键词： Measurement Deep learning Adaptation models Object detection Inspection Feature extraction Image restoration Object recognition Cybernetics Defect detection

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Reversible Essential Image Secret Sharing

SSRN

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SSRN 2023年

作者： Xie, Dong Hu, Zebang Zhang, Yibo Wang, Taochun Chen, Fulong Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal University Wuhu241002 China School of Computer and Information Anhui Normal University Wuhu241002 China

Reversible image secret sharing (RISS) is an image data sharing scheme that can recover cover image to a certain extent. In general, all shadows in a traditional RISS scheme have the same authority level. However, in specific some applications (e.g., corporate boards), certain shadows may be given special privileges depending on factors such as the status or authority level of the participants. To solve this defect, this paper proposes a reversible essential image secret sharing (REISS) scheme based on Lagrange interpolation polynomial, which divides the generated shares into two groups according to their priority, the more important group of shares is called essential shares, and the other group of shares is called non-essential shares. The absence of an essential participant in any group of participants reconstructing the secret image does not reveal any useful information related to the secret image. When shares are noisy images, it affects the administrator's ability to distinguish between priority shares. In addition, visually, noise-like shares resemble encrypted images, which makes them more susceptible to censorship suspicion during transmission, so it is crucial to generate a set of comprehensive cover images and recover them losslessly. In the proposed scheme, the shares generated by the secret image are comprehensible and are the same size combined shares, without size expansion or crosstalk of sub-shares. Moreover, since all details in the image are critical, the original secret image can be losslessly reconstructed by polynomial and the binary cover image can be losslessly recovered by binarization operation. Finally the theoretical analysis and experimental results validate the proposed algorithm. © 2023, The Authors. All rights reserved.

关键词： Interpolation

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CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation

CoinSeg: Contrast Inter- and Intra- Class Representations fo...

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International Conference on computer Vision (ICCV)

作者： Zekang Zhang Guangyu Gao Jianbo Jiao Chi Harold Liu Yunchao Wei School of Computer Science Beijing Institute of Technology School of Computer Science University of Birmingham WEI Lab Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network

Class incremental semantic segmentation aims to strike a balance between the model’s stability and plasticity by maintaining old knowledge while adapting to new concepts. However, most state-of-the-art methods use the freeze strategy for stability, which compromises the model’s plasticity. In contrast, releasing parameter training for plasticity could lead to the best performance for all categories, but this requires discriminative feature representation. Therefore, we prioritize the model’s plasticity and propose the Contrast inter- and intra-class representations for Incremental Segmentation (CoinSeg), which pursues discriminative representations for flexible parameter tuning. Inspired by the Gaussian mixture model that samples from a mixture of Gaussian distributions, CoinSeg emphasizes intra-class diversity with multiple contrastive representation centroids. Specifically, we use mask proposals to identify regions with strong objectness that are likely to be diverse instances/centroids of a category. These mask proposals are then used for contrastive representations to reinforce intra-class diversity. Meanwhile, to avoid bias from intra-class diversity, we also apply category-level pseudo-labels to enhance category-level consistency and inter-category diversity. Additionally, CoinSeg ensures the model’s stability and alleviates forgetting through a specific flexible tuning strategy. We validate CoinSeg on Pascal VOC 2012 and ADE20K datasets with multiple incremental scenarios and achieve superior results compared to previous state-of-the-art methods, especially in more challenging and realistic long-term scenarios. Code is available at https://***/zkzhang98/CoinSeg.

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From Data Integrity to Global ModeI Integrity for Decentralized Federated Learning: A Blockchain-based Approach

From Data Integrity to Global ModeI Integrity for Decentrali...

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International Joint Conference on Neural networks (IJCNN)

作者： Na Wang Yao Zhao Youyang Qu Lei Cui Bai Li Longxiang Gao 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of IT Deakin University VIC Australia Zhejiang Tianheng Information Technology Company China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Decentralized Federated Learning (DFL) is extensively applied in various areas, e.g., healthcare, finance, and Internet of Things (loT), offering practical solutions for distributed intelligent applications and data collaboration. In DFL systems, participants, e.g., edge devices, organizations, or nodes, collaborate in the training of a shared global model by aggregating local models from various participants. During this process, participants need to communicate frequently with a central authority/node/server to share model parameters. Such communication is vulnerable to malicious attacks or tampering, posing a significant threat to the integrity of model training. The integrity verification method can provide an integrity guarantee for the global model of DFL. However, most of the existing integrity verification schemes are centralized and not suitable for resource-constrained DFL scenarios. Therefore, how to verify the integrity of the global model becomes an important issue in DFL. To address it, we devise a global model integrity verification method for DFL. Specifically, we generate a digital signature for each global model parameter as proof of integrity, while improving the efficiency of integrity verification by electing delegates to conduct the verification process. A series of experiments is conducted to validate the performance of the proposed method. The experimental results demonstrate that our approach not only effectively ensures the integrity of the global model but also functions well under limited resources.

关键词： Training Federated learning Data integrity Neural networks Finance Distributed databases Organizations

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