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2nd International Conference on Artificial Intelligence, Systems and Network Security, AISNS 2024

作者： Zhu, Hui Huang, Zihao Zhong, Weijie Wang, Zhenyu School of Economics and Trade Guangdong Mechanical &Electrical Polytechnic Guangzhou China School of Software Engineering South China University of Technology Guangzhou China School of Software Engineering South China University of Technology Guangdong Guangzhou China

ISBN: (纸本)9798400711237

In recent years, graph-based E-commerce Fraud detection methods have received more and more attention, but there are still some problems. Firstly, fraudulent users only account for a small part of active users, and the imbalance between classes in the graph leads to model deviation and affect the effectiveness of E-commerce Fraud detection. At the same time, the existing methods have ignored the sparse connections between fraudulent entities. In addition, most existing methods rely on the cost-sensitive matrix given by the expert experience and cannot adapt to learn the optimal cost transformation according to the data distribution. In this paper, we propose a cost sensitive graph neural network based on adaptive neighbor sampling, termed as CNSGNN. Firstly, the model designs a label-aware adaptive neighbor selector, using reinforcement learning to train a suitable sampling threshold, sampling neighbors by label similarity, thus alleviating the imbalance in the graph initially. Then, similarity scores are used to enhance the connection between fraudulent entities and help the sharing and transmission of characteristic information of fraudulent nodes. After that, GNN is used for information aggregation, and the cost-sensitive matrix optimizer is used to learn the optimal cost transformation to improve the classification effect. The experimental results show that the proposed model is superior to the current advanced baseline methods. © 2024 Copyright held by the owner/author(s).

关键词： Graph neural networks

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Efficient Low-Polygon Building Model Generation with Opening Detection 41st

Efficient Low-Polygon Building Model Generation with Openin...

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41st Computer Graphics International Conference, CGI 2024

作者： Wang, Yixian Lin, Boheng Chen, Weiya Wan, Lin School of Software and Engineering Huazhong University of Science and Technology Wuhan China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031820236

With the rapid increase of complexity and volume of 3D building models, industries such as digital games and computer-aided design face considerable challenges. One feasible solution is to generate low-polygon models for complex building meshes in a relatively short time to apply in a level of detail (LOD) algorithm. We propose an efficient building model simplification algorithm with opening detection, aiming to realize an extremely low polygon count while preserving their visual features. Our algorithm has three steps. We detect and construct the opening structures presented in the building model to incorporate in the subsequent carving process;improve the approach to generate a visual hull that represents the fundamental boundary of the building model and carve out details with high efficiency;and apply planarization and simplification algorithms to obtain the simplified model. Our algorithm can generate highly simplified building models while preserving important opening details to serve as the roughest level in the LOD hierarchy. Experimental results demonstrate that our algorithm efficiently handles building models with complex opening structures and outperforms other recent simplification algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mesh generation

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Advancing Evasion: Distributed Backdoor Attacks in Federated Learning 25th

Advancing Evasion: Distributed Backdoor Attacks in Federate...

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25th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024

作者： Wang, Jian Shen, Hong Ke, Wei Faculty of Applied Sciences Macao Polytechnic University China School of Engineering and Technology Central Queensland University Rockhampton Australia School of Software Technology Guangzhou Institute of Software Guangzhou China

ISBN: (纸本)9789819642069

Federated Learning (FL) is vulnerable to backdoor attacks through data poisoning if the data is not scrutinized, as malicious participants can inject backdoor triggers in normal samples, leading to poisoned updates. Distributed backdoor attacks pose a greater threat than centralized ones, as they often use fixed pixel blocks as triggers, increasing the risk of detection. This paper presents a novel distributed backdoor attack strategy that leverages edge structure poisoning to circumvent existing defense mechanisms, employing a distributed poisoning strategy to evade current defense mechanisms, thereby enhancing the stealth of the attack. Experimental results on multiple benchmark datasets demonstrate that this method is more effective and stealthy compared to other backdoor attack methods. Furthermore, this paper also proposes targeted defense strategies based on the experimental results, offering a new perspective on the security of FL systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Two Compact Hybrid Band-Pass Filters Using Eighth-Mode Substrate-Integrated Waveguide and Microstrip Resonators

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Progress in Electromagnetics Research C 2025年 151卷 167-175页

作者： Tang, Luyao Han, Wei Wei, Hao Li, Yanbin The 54th Research Institute of China Electronics Technology Group Corporation China National Engineering Reasearch Center of Communication Software and Asic Design China

Two compact substrate-integrated waveguide (SIW) filters with hybrid coupling of eighth-mode substrate integrated waveg-uide (EMSIW) resonators and microstrip are proposed in this paper. Hybrid coupled filters were achieved by etching two half-wavelength microstrip resonators (BPF I) or two quarter-wavelength microstrip resonators (BPF II) on the top of traditional second-order EMSIW filters. The topology of the two filters was analyzed. Due to the cross coupling between resonator 1 and resonator 4, two transmission zeros were achieved outside the band of BPF I, which increased the selectivity of the filter. Due to the mixed electromagnetic coupling between resonator 2 and resonator 3, a transmission zero is realized in the stopband of BPF II. To confirm the validity of the two filter models, two filters were designed, produced, and measured. Based on the findings of the measurements, the central frequency of BPF I is recorded at 7.7 GHz, with a fractional bandwidth (FBW) of 18.2%. The insertion loss (IL) within the passband is minimal at 0.8 dB, and the size of the filter is only 8 mm * 4 mm (0.53λg *0.26λg). The filter exhibits enhanced out-of-band suppression due to the presence of two transmission zeros located at frequencies of 6.4 GHz and 10 GHz. The center frequency of BPF II is 19.5 GHz,; the FBW is 20.5%; the IL within the passband is only 0.49 dB; and the size of the filter is only 2 mm ∗ 2 mm (0.34λg *0.34λg). As a result of the mixed electromagnetic coupling effects, a transmission zero occurs at a frequency of 26.7 GHz. The simulation outcomes are consistent with the experimental findings. Compared with other reported SIW filters, the two filters introduced in this study exhibit favorable characteristics such as reduced insertion loss and compact dimensions. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Bandpass filters

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SQUMUTH squirrel search based algorithm for high order mutant generation in mutation testing

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Discover Computing 2025年第1期28卷 1-17页

作者： Mohanty, Subhasish Mishra, Jyotirmaya Mohapatra, Sudhir Kumar Bejo, Seifu Detso Deferisha, Aliazar Deneke Computer Science and Engineering GIET University Odisha Gunupur India Faculty of Engineering and Technology Sri Sri University Cuttack India Department of Software Engineering CCI Wolkite University Wolkite Ethiopia Faculty of Computing and Software Engineering Arba Minch University Arba Minch Ethiopia

In today's software testing community, quality assessment remains critical, with mutation testing standing as a cornerstone technique for evaluating the effectiveness of test cases. This method involves introducing faulty code, or mutants, into the program to assess the quality of the test suite and other testing methods. However, mutation testing faces challenges such as the generation of numerous mutants, the presence of equivalent mutants that are difficult to detect through testing, and the lack of realistic mutant creation. Literature reviews indicate significant efforts to address these issues through formal solutions and heuristic methods. Recent optimization-based methods are now recognized as cost-effective result in an optimized solution. Hence, to overcome these limitations, this study introduces SQUMUTH, a novel approach for high-order mutant generation based on the Squirrel Search Algorithm (SSA). Inspired by the foraging behavior of squirrels, SSA offers a promising solution for enhancing the efficiency and effectiveness of mutation testing. Experimental evaluations on eight well-known Java benchmark programs demonstrate that SQUMUTH outperforms existing methods. Comparative analyses of mutation scores and the rates of realistic mutants consistently show its better performance across all subject programs compared to other state-of-the-art methods such as Social Group Optimization, Binary Genetic Algorithm, and random testing. The experimental results underscore its effectiveness in generating more realistic mutants. The experimental results indicated that the proposed approach has the potential to advance software testing by improving the cost-effectiveness of mutation analysis and the quality of software systems. © The Author(s) 2025.

关键词： First-order mutants Higher-order mutants Mutation score Mutation testing software testing Squirrel search algorithm

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Credible Negotiation for Multi-agent Reinforcement Learning in Long-term Coordination

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ACM Transactions on Autonomous and Adaptive Systems 2025年第1期20卷 1-27页

作者： Gu, Tianlong Zhi, Taihang Bao, Xuguang Chang, Liang Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Engineering Research Center of Trustworthy Ai Jinan University Guangzhou China

The coordination of multi-agent is one of the critical problems in Multi-agent Reinforcement Learning (MARL). The traditional methods of MARL focus on finding a stochastically acceptable solution called Nash Equilibrium (NE) for all agents from the Markov Game in which multiple equilibria exist. However, learning a fair equilibrium is crucial for the sustainability and stability of collaboration in the long-term coordination game, especially when the leadership competition exists. In this article, we propose the bi-level reinforcement learning method N-Bi-AC, whose solution is a Pareto improvement for traditional NE, to choose a fair Equilibrium. There are two parts in our method, the first is that we propose the Negotiator to determine the leader in stage game, and the other is to update the Q-value of agents in the game by using a bi-level actor-critic learning method based on the Joint Mixed Strategy Equilibrium Q-learning algorithm (JMSE Q-learning). The convergence proof is given, and the learning algorithm is compared with the state-of-the-art algorithms. We found that the proposed N-Bi-AC method successfully converged to a fair NE, which guarantees the fairness of agents in different matrix game environments. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Stochastic systems

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Learning Hierarchical Adaptive Code Clouds for Neural 3D Shape Representation

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Machine Intelligence Research 2025年第2期22卷 304-323页

作者： Yuanxun Lu Xinya Ji Hao Zhu Xun Cao State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China School of Intelligence Science and Technology Nanjing UniversitySuzhou 215163China

Neural implicit representation(NIR)has attracted significant attention in 3D shape representation for its efficiency,generalizability,and flexibility compared with traditional explicit *** works usually parameterize shapes with neural feature grids/volumes,which prove to be inefficient for the discrete position constraints of the *** recent advances make it possible to optimize continuous positions for the latent codes,they still lack self-adaptability to represent various kinds of shapes *** this paper,we introduce a hierarchical adaptive code cloud(HACC)model to achieve an accurate and compact implicit 3D shape ***,we begin by assigning adaptive influence fields and dynamic positions to latent codes,which are optimizable during training,and propose an adaptive aggregation function to fuse the contributions of candidate latent codes with respect to query *** addition,these basic modules are stacked hierarchically with gradually narrowing influence field thresholds and,therefore,heuristically forced to focus on capturing finer structures at higher *** formulations greatly improve the distribution and effectiveness of local latent codes and reconstruct shapes from coarse to fine with high *** qualitative and quantitative evaluations both on single-shape reconstruction and large-scale dataset representation tasks demonstrate the superiority of our method over state-of-the-art approaches.

关键词： Representation learning shape analysis deep implicit function 3D reconstruction 3D modeling

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Heterogeneous signcryption scheme from CLC to IBC for IIoT

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Peer-to-Peer Networking and Applications 2025年第4期18卷 1-14页

作者： Jin, Chunhua Qin, Wenyu Chen, Zhiwei Li, Chenhao Chen, Xiaobing Chen, Guanhua Zhang, Hao Weng, Jian Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai’an 233003 China The College of Information Science and Technology Jinan University Zhuhuai 510632 China

The Industrial Internet of Things (IIoT) involves the real-time gathering of information from physical devices and technologies for improved perception and management. To ensure information transmission security, an efficient and secure cryptography scheme is needed, especially for devices under resource constraints. In this paper, a heterogeneous signcryption (HSC) scheme is designed to meet security requirements in the IIoT environment. This construction employs a heterogeneous system, with the sender using certificateless cryptography (CLC) to effectively solve the key escrow problem in identity-based cryptography (IBC) and the certificate management problem in public key infrastructure (PKI). The receiver, on the other hand, uses IBC to settle the certificate management issue in PKI. The scheme supports formal security proof in the random oracle model (ROM). Furthermore, the detailed performance analysis demonstrates that the scheme has advantages in terms of energy consumption and computation overhead, making it suitable for the IIoT environment. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Heterogeneous IIoT ROM Security Signcryption

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Blockchain-enabled IoT access control model for sharing electronic healthcare data

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Multimedia Tools and Applications 2025年第10期84卷 8127-8148页

作者： Ilyas, Benkhaddra Kumar, Abhishek Ali, Setitra Mohamed Lei, Hang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China Chandigarh University Gharuan Mohali Punjab140413 India School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

Deploying the Internet of Things (IoT) in the transfer of enormous medical data often promotes challenges with the security, confidentiality, and privacy of the user’s sensitive data. In addition, the access control for prohibiting unauthorized access has been facing challenges with single-point-of-failure, collusion, and other security issues. To address these challenges associated with IoT access control, this research proposes a Blockchain-enabled IoT access control for sharing Electronic health records (EHR). The proposed authentic access control scheme transfers the data to the authenticated user without discrepancies. The proposed technique utilizes the hyperledger composer fabric blockchain that improves the transparency and traceability of the network, and the intelligent contract keeps track of the information, which reduces the possibility of unauthorized access. The proposed access control scheme validates the request effectively and transmits the data efficiently. The effectiveness of the proposed access control scheme is evaluated by analyzing the blocks in terms of metrics such as responsiveness of 185.47 secs, 95% Genuine User Detection (GUD), 96.50% Privacy, and 3.50% Information loss, for 100 blocks and proposed access control scheme works more efficiently in transmitting the data. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Smart contract

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DA-FR-UNet: A Dual Attention-Enhanced Approach for Retinal Vessel Image Segmentation

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第5期52卷 1325-1335页

作者： Li, Yuanyuan Wu, Jiansheng Liu, Yang School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China University of Science and Technology Liaoning Anshan114051 China

Retinal vessel image segmentation is crucial for the early diagnosis and treatment of ophthalmic diseases. However, accurate segmentation remains challenging due to the complexity and diversity of retinal images, as well as the intricate nature of vascular structures. Existing methods often underperform when dealing with fine vessels and complex backgrounds, limiting their effectiveness in clinical applications. This study proposes a novel retinal vessel image segmentation method, Dual Attention FR-UNet (DA-FR-UNet), which incorporates two innovative attention mechanisms: Enhanced Channel Prior Attention (ECPA) and Hard Triplet Attention (HTA). The ECPA module enhances the model's ability to recognize fine blood vessels by integrating global average pooling and global maximum pooling to effectively capture multi-scale features. In contrast, the HTA module improves the model's understanding of complex vascular structures by simultaneously considering attention information across three dimensions: channel, spatial height, and width. Additionally, the study introduces the testtime augmentation (TTA) technique during the experimental phase to enhance the model's generalizability and robustness. To validate the performance of the DA-FR-UNet model in retinal vessel image segmentation tasks, a series of comparative experiments were conducted on the FIVES and RVD datasets. The experimental results demonstrate that the DA-FR-UNet model, combining the dual-attention mechanism and the TTA strategy, outperforms existing state-of-the-art methods across several key evaluation metrics. On the FIVES dataset, DAFR- UNet achieves a sensitivity of 88.42%, an IoU value of 82.27%, and a Dice coefficient of 89.72%. On the RVD dataset, these metrics are 65.46%, 44.53%, and 61.43%, respectively. These findings highlight the model's significant advantages in processing fine vessels and complex backgrounds, improving its sensitivity and robustness in blood vessel image segmentation. © (2025)

关键词： Image segmentation

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