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

作者： Luo, Hui Lu, Lu Liang, Zhihong Suo, Siliang School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China PengCheng Laboratory Shenzhen China Electric Power Research Institute CSG Guangdong Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangdong Guangzhou China

ISBN: (纸本)1891706594

Pre-trained code models have achieved promising results in the vulnerability detection field. The prevailing approach is to adapt these models with vulnerability datasets using inefficient full-model fine-tuning. These pre-trained models primarily capture the semantic features of code, while neglecting its structural characteristics. To address these issues, this paper proposes a vulnerability detection method with adapter tuning and enhanced feature learning. First, adapter modules are introduced to UniXcoder and tune only the parameters in the adapters to extract semantic features. This significantly reduces the number of training parameters and better adapts the model to downstream tasks. Then, structural features, including control and data flow information, are extracted from the Program Dependence Graph (PDG) to compensate for the limitations of pre-trained models that rely solely on semantic features. Finally, the semantic and structural features are integrated to train the detection model. The experimental results demonstrate that our proposed method outperforms state-of-the-art approaches and is highly efficient in terms of both training parameters and training data. © 2024 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Data flow graphs

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Achieving Distributed and Privacy-Preserving Cross-Chain Transactions in Account-Model Blockchain Systems

Achieving Distributed and Privacy-Preserving Cross-Chain Tra...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Zhang, Chuan Wang, Weijie Zhang, Weiting Nie, Jiangtian Liang, Jinwen Zhu, Liehuang Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Guangdong China Beijing Institute of Technology School of Computer Science & Technology Beijing China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China Nanyang Technological University School of Computer Science and Engineering Singapore Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350333336

Cross-Chain is an emerging technology to enable transaction circulation across different blockchain systems, breaking down transaction isolation among decentralized ecosystems. Existing cross-chain solutions are often constructed based on centralized trusted third parties, which introduce additional threats to users' identities due to potentially compromised risks on the centralized third parties. To exclude the single-point compromised risk in centralized systems, this paper focuses on a fully decentralized scenario and develops a distributed cross-chain transaction scheme, which enables cross-chain transaction exchanges while ensuring the unlinkability between payees and payers. Specifically, we first propose a cross-chain cryptocurrency exchange scheme by leveraging Ethereum's smart contracts, which achieves transaction exchanges in decentralized settings. Then, we leverage zero-knowledge succinct non-interactive argument of knowledge (i.e., zk-SNARKs) to explore a privacy-preserving method for cross-chain transactions in account-model blockchain systems, such that the unlinkability between payees and payers and the privacy of cross-chain participants can be guaranteed. Security analysis and experimental evaluations demonstrate that the proposed scheme achieves the security goal at acceptable costs. © 2023 IEEE.

关键词： Smart contract

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ComGPT: Detecting Local Community Structure with Large Language Models

arXiv

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arXiv 2024年

作者： Ni, Li Shen, Haowen Mu, Lin Zhang, Yiwen Luo, Wenjian School of Computer Science and Technology Anhui University Hefei China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

Large Language Models (LLMs), like GPT, have demonstrated the ability to understand graph structures and have achieved excellent performance in various graph reasoning tasks, such as node classification. Despite their strong abilities in graph reasoning tasks, they lack specific domain knowledge and have a weaker understanding of communityrelated graph information, which hinders their capabilities in the community detection task. Moreover, local community detection algorithms based on seed expansion, referred to as seed expansion algorithms, often face the seed-dependent problem, community diffusion, and free rider effect. To use LLMs to overcome the above shortcomings, we explore a GPT-guided seed expansion algorithm named ComGPT. ComGPT iteratively selects potential nodes by local modularity M from the detected community's neighbors, and subsequently employs LLMs to choose the node to join the detected community from these selected potential nodes. To address the above issues faced by LLMs, we improve graph encoding method, called Incident, by incorporating community knowledge to improve LLMs's understanding of community-related graph information. Additionally, we design the NSG (Node Selection Guide) prompt to enhance LLMs' understanding of community characteristics. Experimental results demonstrate that ComGPT outperforms the comparison methods, thereby confirming the effectiveness of the improved graph encoding method and prompts. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Multi-Channel Hypergraph Convolution Group Recommendation with Member Information Enhancement 25

Multi-Channel Hypergraph Convolution Group Recommendation wi...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Tianhao Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach. © 2023 IEEE.

关键词： Recommender systems

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A Study on the Influence of American Macroeconomic Indicators on Fed Interest Rate Empirical Test Based on VAR and VECM Models

A Study on the Influence of American Macroeconomic Indicator...

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Information and Communication Engineering (JCICE), International Joint Conference on

作者： Lin Xiao Chengxi Huang Fangxu Rao School of Computer Science and Mathematics Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Fuzhou China School of Computer Science and Mathematics Fujian University of Technology Fuzhou China

ISBN: (数字)9798350388725

ISBN: (纸本)9798350388732

Based on the monthly data of U.S. macroeconomic indicators from 1996 to 2022, this paper explores the interdynamic relationship with the Federal Reserve's interest rate. The empirical analysis results show that the impact on the Fed interest rate can be divided into two aspects: positive and negative. Variables that have a positive impact include GDP growth rates and the impact of the Fed interest rates on itself. The variables with negative effects include the short-term negative impact of the unemployment rate, and the long-term negative impact of total reserves, broad money growth rate, and inflation rate. In addition, this paper finds that the unemployment rate and GDP growth rate play a significant positive role in the Fed interest rate in the long-term dynamic adjustment process between various variables, and the Fed interest rate has a significant negative correction effect on itself.

关键词： Exchange rates Reactive power Solid modeling Economic indicators Biological system modeling Employment Market research Stability analysis Macroeconomics Unemployment

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Social-enhanced recommendation using graph-based contrastive learning 25

Social-enhanced recommendation using graph-based contrastive...

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作者： Xue, Peng Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation algorithms, the social neighbors' contributions are difficult to distinguish from the central user's, neglecting hidden correlations in social infor-mation. In order to solve this problem, this paper presents a graph-based contrastive learning framework for social-enhanced recommendation (SoGCLR) that utilizes implicit social information captured by a new social relation attention mechanism, en-rich user representations, and improve model robustness through graph-based contrastive learning. Specifically, the paper captures the degree of contribution of each neighbor in the social graph to the central user through a social relation attention layer, thus obtaining hidden correlations in social information, and further integrates this with user information in the user-item interaction graph to enrich user representations. In addition, the paper incorporates graph-based contrastive learning into the recommendation task using cross-layer contrastive learning, calculating contrastive loss and mapping nodes with similar but different exposure rates to nearby regions to mitigate exposure bias issues. Results from Ciao and Epinions demonstrate that SoGCLR reduces RMSE and MAE by 1.33% to 1.84% compared with baseline models. © 2023 IEEE.

关键词： Recommender systems

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Finger Vein Image Quality Assessment Based on Stochastic Embedding Robustness 42

Finger Vein Image Quality Assessment Based on Stochastic Emb...

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42nd Chinese Control Conference, CCC 2023

作者： Yang, Chen Guo, Jian Zhang, Yibo Huang, Yanan Guo, Zijie School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Nanjing University of Posts and Telecommunications Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210023 China

ISBN: (纸本)9789887581543

Finger vein image quality assessment is used to evaluate the quality of images, namely to evaluate the applicability of the finger vein images to the recognition system. The work of quality assessment will largely affects the performance of the finger vein recognition system. In previous work, people usually design quality indicators manually, construct quality functions or perform supervised learning based on visual inspection, which has limitations of subjectivity. In order to solve these problems, this paper proposes a finger vein image quality assessment algorithm based on Stochastic Embedding Robustness (SER), which is called SER algorithm in this paper. The algorithm generates random subnetworks from the deployed finger vein recognition system, and indirectly evaluates the image quality by calculating the variation of different subnetworks. The simulation results show that, SER algorithm can effectively filter out low-quality images and improve the performance of the recognition system. Compared with other methods, it is more efficient and stable. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Image quality

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Verifier-local revocation group signatures with backward unlinkability from lattices

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Frontiers of Information technology & Electronic Engineering 2022年第6期23卷 876-892页

作者： Yanhua ZHANG Ximeng LIU Yupu HU Yong GAN Huiwen JIA College of Computer and Communication Engineering Zhengzhou University of Light IndustryZhengzhou 450001China College of Mathematics and Computer Science Fuzhou UniversityFuzhou 350108China Stale Key Laboratory of Integrated Service Networks Xidian UniversityXi’an 710071China College of Information Engineering Zhengzhou University of TechnologyZhengzhou 450044China School of Mathematics and Information Science Guangzhou UniversityGuangzhou 510006China

For group signature(GS)supporting membership revocation,verifier-local revocation(VLR)mechanism seems to be a more flexible choice,because it requires only that verifiers download up-to-date revocation information for signature verification,and the signers are not *** a post-quantum secure cryptographic counterpart of classical number-theoretic cryptographic constructions,the first lattice-based VLR group signature(VLR-GS)was introduced by Langlois et al.(2014).However,none of the contemporary lattice-based VLR-GS schemes provide backward unlinkability(BU),which is an important property to ensure that previously issued signatures remain anonymous and unlinkable even after the corresponding signer(i.e.,member)is *** this study,we introduce the first lattice-based VLR-GS scheme with BU security(VLR-GS-BU),and thus resolve a prominent open problem posed by previous *** new scheme enjoys an O(log N)factor saving for bit-sizes of the group public-key(GPK)and the member’s signing secret-key,and it is free of any public-key *** the random oracle model,our scheme is proven secure under two well-known hardness assumptions of the short integer solution(SIS)problem and learning with errors(LWE)problem.

关键词： Group signature Lattice-based cryptography Verifier-local revocation Backward unlikability Short integer solution

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AlignFuse: Multi-feature Alignment and Fusion for Medical Image Segmentation 8

AlignFuse: Multi-feature Alignment and Fusion for Medical Im...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Chao, Shengyang Liu, Zijun Huang, Lili Fu, Yanping Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China

ISBN: (纸本)9798350302455

U-Net, which is a U-shaped network, has made great progress on numerous medical image segmentation tracks. However, even if convolutional neural network (CNN) has a talent for extracting local information, it falls short in capturing global information due to the inherent drawback of the convolution operation. In contrast, whereby the design of tokenized images and self-attention mechanism, Transformer has a brilliant performance in modeling global contextual information but has a poor performance in grasping low-level details on account of the patch division which leads to ignorance of local correlation in each patch. In this paper, we develop a hybrid network structure, AlignFuse, which is in possession of the advantages of both CNN and Transformer. AlignFuse fits CNN and Transformer together in a parallel style, where Transformer leverages the high-level features and CNN pays more attention to details. Moreover, owing to the downsampling operation in CNNs, traditional context-free feature aggregation operations lead to misalignment issues between feature maps. To alleviate this problem, we propose to use Feature Aligned Fusion Module (FAFM) to effectively align and fuse the feature maps from CNN and Transformer in each decoder stage. We conduct experiments on Gland, breast cancer, and automatic cardiac segmentation, with competitive results obtained. © 2023 IEEE.

关键词： Convolution

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VRLVMix: Combating Noisy Labels with Sample Selection based on Loss Variation 4

VRLVMix: Combating Noisy Labels with Sample Selection based ...

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4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2023

作者： Wang, Hailun Tu, Zhengzheng Jiang, Bo Ding, Yuhe Anhui University Institute of Artificial Intelligence Hefei Comprehensive National Science Center AHU-IAI Ai Joint Laboratory Hefei China Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350343618

Since deep neural networks can fully fit all data, including noisy labels, that is, mislabeled data, this will be detrimental to the robustness and generalization ability of the network. To address this problem, existing methods usually use small loss tricks to select clean samples for training. However, sample selection using only small loss techniques cannot distinguish between large loss 'hard' samples and noise samples. In this work, we analyze the reasons why clean samples are misidentified as noise samples and propose a balanced selection mechanism based on loss variation. This method sorts the amount of loss change, uses variance to count the rankings of multiple epochs to describe the stability of loss variation, and selects samples with stable loss as clean samples to reduce the misclassification of 'hard' samples. At the same time, semantic clustering is used to assist resampling and reweighting, thereby alleviating the negative impact 1 of class imbalance on sample selection. We conduct comparative experiments and ablation experiments on synthetic noise datasets and real-world datasets such as CIFAR-10/100 and Clothing1M. Our VRLVMix has been shown to outperform numerous state-of-the-art methods, as evidenced by the experimental results. Moreover, it demonstrates the ability to extract clean samples even from large loss sample. © 2023 IEEE.

关键词： Semantics

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