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2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024

作者： Mao, Qinghua Lin, Xi Su, Xiu Li, Gaolei Chen, Lixing Li, Jianhua Shanghai Jiao Tong University China Big Data Institute Central South University China School of Computer Science Faculty of Engineering The University of Sydney Australia Shanghai Key Laboratory of Integrated Administration Technologies for Information Security China

ISBN: (纸本)9798350344851

Federated graph learning (FGL) enables the collaborative training of graph neural networks (GNNs) in a distributed manner. A critical challenge in FGL is label deficiency, which becomes more intricate due to non-IID decentralized data. Existing methods have focused on extracting knowledge from abundant unlabeled data, leaving few-shot labeled data unexplored. To this end, we propose ConFGL, a novel FGL framework to enhance label efficiency in federated learning with non-IID subgraphs. We formulate a semi-supervised objective to harness both unlabeled and labeled data, where self-supervised learning is achieved via a graph contrastive module. Additionally, a personalized federated learning (FL) strategy is adopted to concurrently train a global model and an individual model, which helps alleviate the representation disparities encoded by local models. Extensive experiments on four node-level datasets under non-IID settings have shown that ConFGL can consistently provide an average of 4.10% accuracy gains over supervised FL personalization methods while maintaining a higher GPU throughput. © 2024 IEEE

关键词： Graph neural networks

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Finding EFS1 Allocations for Indivisible Resources among two agents

Finding EFS1 Allocations for Indivisible Resources among two...

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2023 International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2023

作者： Pang, Xiaoyun Huang, Wei Yuan, Liang School of Computer Science and Information Security Guilin University of Electronics Technology Guilin541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronics Technology Guilin541004 China

ISBN: (纸本)9781510671881

We study the problem of fairly allocating a set of indivisible goods between two agents with additive valuations. Fair distribution has become an emerging research topic in computer science and artificial intelligence, and envy-free is the most widely studied concept of fairness. Envy-free allocation always exists when goods are divisible, but not always for indivisible goods, which motivates us to study envy-free relaxation. In this paper, we introduce a novel solution concept called EFS1, in which envy between two agents can be eliminated by swapping a good in the bundle of the two agents. Furthermore, we propose an efficient algorithm capable of finding such allocations in polynomial time, and we demonstrate the algorithm's execution through an example involving a dummy good. Our research offers promising solutions for addressing envy in allocation scenarios with the introduction of the EFS1 concept and an efficient algorithm. © 2023 SPIE. All rights reserved.

关键词： Polynomial approximation

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Image steganography based on generative implicit neural representation

arXiv

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arXiv 2024年

作者： Zhong, Yangjie Ke, Yan Liu, Meiqi Liu, Jia Engineering University of PAP Shanxi710000 China Key Laboratory of Network and Information Security of PAP Shanxi710000 China

In the realm of advanced steganography, the scale of the model typically correlates directly with the resolution of the fundamental grid, necessitating the training of a distinct neural network for message extraction. This paper proposes an image steganography based on generative implicit neural representation. This approach transcends the constraints of image resolution by portraying data as continuous functional expressions. Notably, this method permits the utilization of a diverse array of multimedia data as cover images, thereby broadening the spectrum of potential carriers. Additionally, by fixing a neural network as the message extractor, we effectively redirect the training burden to the image itself, resulting in both a reduction in computational overhead and an enhancement in steganographic speed. This approach also circumvents potential transmission challenges associated with the message extractor. Experimental findings reveal that this methodology achieves a commendable optimization efficiency, achieving a completion time of just 3 seconds for 64x64 dimensional images, while concealing only 1 bpp of information. Furthermore, the accuracy of message extraction attains an impressive mark of 100%.MSC Codes 68T07 Copyright © 2024, The Authors. All rights reserved.

关键词： Steganography

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Detecting Fake-Normal Pornographic and Gambling Websites through one Multi-Attention HGNN 26

Detecting Fake-Normal Pornographic and Gambling Websites thr...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Ma, Xiaoqing Zheng, Chao Li, Zhao Yin, Jiangyi Liu, Qingyun Chen, Xunxun Institute of Information Engineering Chinese Academy of Sciences Beijing China National Engineering Laboratory for Information Security Technologies Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Geedge Networks Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China

ISBN: (纸本)9798350331684

The rapid development of pornographic and gambling websites, fueled by the widespread abuse of information technology, has become a growing concern. They pose a serious threat to the physical and mental health of children and can also endanger personal property. Therefore, it is necessary to detect them. However, pornographic and gambling websites become more and more tricky, which shows fake-normal to evade censorship and challenges traditional content-based detection methods. Therefore, it is essential to rely on information about relationships between *** propose HMAN, one Multi-Attention Heterogeneous Graph Neural network (HGNN) model to detect pornographic and gambling websites by integrating content features and structural information, even if they present fake-normal. By one multi-attention mechanism consisting of explicit weight, self-attention and attention mechanism, content features can be selectively utilized with the assistance of structural information. The experimental results show that our method achieves the best 95.1% Macro-Avg-F1 and outperforms all baselines. We also illustrate that all extracted metapaths do contribute to the detection, where the hyperlink, title/meta terms and IP address are relatively important. © 2023 IEEE.

关键词： Websites

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A Poisoning Attack Based on Variant Generative Adversarial networks in Recommender Systems 9th

A Poisoning Attack Based on Variant Generative Adversarial ...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Cai, Hongyun Wang, Shiyun Zhang, Yu Zhang, Meiling Zhao, Ao School of Cyber Security and Computer Hebei University Hebei Baoding071000 China Key Laboratory on High Trusted Information System in Hebei Province Hebei University Hebei Baoding071000 China

ISBN: (纸本)9783031466731

The emergence of poisoning attacks brings significant security risks to recommender systems. Injecting a well-designed set of fake user profiles into these systems can severely impact the quality of recommendations. However, existing attack models against recommender systems struggle to balance the imperceptibility and harmfulness of the generated fake user profiles. In this paper, we propose a novel poisoning attack model based on variant GAN. Firstly, we construct a candidate item set and divide each user rating behavior sequence into multiple shorter sequences. By identifying high-impact short sequences and calculating the proportion of high-impact sequences in each user rating sequence, we can determine the template profiles. Secondly, we design two different generators to simulate the fake user profiles, and employ a discriminator to enhance the generation of higher-quality fake user profiles. Finally, experimental results on three real datasets demonstrate that the proposed method outperforms state-of-the-art attacks in terms of attack performance, and the generated fake profiles are more difficult to detect when compared to the baselines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Recommender systems

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Near-Lossless Compression of Two Independent Binary Sources with One Encoder

Near-Lossless Compression of Two Independent Binary Sources ...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Tingting Zhu Xiao Ma School of Computer Science and Engineering MoE Key Laboratory of Information Technology Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In this paper, we consider the near-lossless compression of two distinct but independent binary random sources $U$ and $V$ , both of which are asymmetrical independent and identically distributed (i.i.d.) sources. In particular, $V$ is assumed to be sparser than $U$ . We show that, to efficiently compress $U$ and $V$ , explicit source coding (compression) for $V$ (the sparser one) is not required. The basic idea is to encode $U$ with an error-correction code and then superimpose $V$ on the codeword. Hence, only one encoder-decoder pair (EDP) suffices to compress two sources. Theoretical analysis shows that the proposed coding scheme is entropy-approaching in terms of bit-error rate (BER) of both sources, and numerical results verify our analysis. Furthermore, we analyze the complexity of compression using the block Markov superposition transmission-repetition (BMST-R) codes to discuss the advantage of the proposed scheme. Simulation results demonstrate that the proposed scheme compresses the two sources simultaneously in a more compact way but causes no performance degradation.

关键词： Wireless communication Degradation Wireless sensor networks Source coding Simulation Watermarking Signal processing Complexity theory Error correction codes Noise measurement

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security situation awareness method of power mobile application based on big data architecture 5

Security situation awareness method of power mobile applicat...

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5th International Conference on Data Science and information Technology, DSIT 2022

作者： Yong, Li Mu, Chen Zaojian, Dai Lu, Chen State Grid Key Laboratory of Information & Network Security State Grid Smart Grid Research Enstitute Co. Ltd Nanjing210003 China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

According to the characteristics of security threats and massive users in power mobile applications, a mobile application security situational awareness method based on big data architecture is proposed. The method uses open-source big data technology frameworks such as Kafka, Flink, Elasticsearch, etc. to complete the collection, analysis, storage and visual display of massive power mobile application data, and improve the throughput of data processing. The security situation awareness method of power mobile application takes the mobile terminal threat index as the core, divides the risk level for the mobile terminal, and predicts the terminal threat index through support vector machine regression algorithm (SVR), so as to construct the security profile of the mobile application operation terminal. Finally, through visualization services, various data such as power mobile applications and terminal assets, security operation statistics, security strategies, and alarm analysis are displayed to guide security operation and maintenance personnel to carry out power mobile application security monitoring and early warning, banning disposal and traceability analysis and other decision-making work. The experimental analysis results show that the method can meet the requirements of security situation awareness for threat assessment accuracy and response speed, and the related results have been well applied in a power company. © 2022 IEEE.

关键词： Big data

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Weakly Supervised Gland Segmentation with Class Semantic Consistency and Purified Labels Filtration 39

Weakly Supervised Gland Segmentation with Class Semantic Con...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Feng, Siyang Wang, Huadeng Han, Chu Liu, Zhenbing Zhang, Hualong Lan, Rushi Pan, Xipeng School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guangdong Provincial Key Laboratory of Artificial Intelligence Medical Image Analysis and Application Guangzhou510080 China International Joint Research Laboratory of Spatio-temporal Information and Intelligent Location Services Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)157735897X

Image-level weakly supervised semantic segmentation (WSSS) reduces the dependence on high-quality data annotation, which plays a crucial role in computational pathology. Benefit from the ability to localize the objects with only binary labels, Class Activation Map (CAM) is a widely used method to initial pseudo masks. However, due to the low contrast among different tissues in histopathological images, most existing CAM-based methods perform poorly in gland segmentation. We retrospect this process and find that class consistency and semantic consistency can guide the network to effectively distinguish confusing pixels and generate fine-grained pseudo masks. Specifically, for class consistency, we propose Consistency Correlation Attention (CCA) to encourage the network to focus on the contribution of class features to semantic dependencies. For semantic consistency, we propose Multi-scale Pyramid Fusion Pooling (MPFP) to aggregate coarse-to-fine global semantic information from CAMs at multiple spatial resolutions, thus identifying class localization. Additionally, we introduce a Purified Labels Filtration (PLF) strategy during the segmentation phase to mitigate the noisy supervision signal and improve the segmentation quality of the model. Extensive experiments show that the our method achieves new state-of-the-art results on three publicly available gland datasets. Furthermore, our method demonstrates impressive domain adaptation capability, achieving satisfactory results with only a small portion of samples when faced with unseen domain data. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantic Segmentation

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Lithological Facies Classification Using Attention-Based Gated Recurrent Unit

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Tsinghua Science and Technology 2024年第4期29卷 1206-1218页

作者： Yuwen Liu Yulan Zhang Xingyuan Mao Xucheng Zhou Jingwen Chang Wenwei Wang Pan Wang Lianyong Qi College of Computer Science and Technology China University of Petroleum(East China)Qingdao 266580China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguang 262700China School of Mechanical Electrical and Information EngineeringShandong UniversityWeihai 264209China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinan 250022China School of Modern Posts Nanjing University of Posts and TelecommunicationsNanjing 210023China

Lithological facies classification is a pivotal task in petroleum geology, underpinning reservoir characterization and influencing decision-making in exploration and production operations. Traditional classification methods, such as support vector machines and Gaussian process classifiers, often struggle with the complexity and nonlinearity of geological data, leading to suboptimal performance. Moreover, numerous prevalent approaches fail to adequately consider the inherent dependencies in the sequence of measurements from adjacent depths in a well. A novel approach leveraging an attention-based gated recurrent unit (AGRU) model is introduced in this paper to address these challenges. The AGRU model excels by exploiting the sequential nature of well-log data and capturing long-range dependencies through an attention mechanism. This model enables a flexible and context-dependent weighting of different parts of the sequence, enhancing the discernment of key features for classification. The proposed method was validated on two publicly available datasets. Results demonstrate a considerably improvement over traditional methods. Specifically, the AGRU model achieved superior performance metrics considering precision, recall, and F1-score.

关键词： attention mechanism facies classification GRU MLP(multilayer perceptron)

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Research and Implementation of Electric Topology Intelligent Checking System Based on Graph Computing 7

Research and Implementation of Electric Topology Intelligent...

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7th International Conference on Intelligent, Interactive Systems and Applications, IISA2022

作者： Qiao, Junfeng Pan, Sen Yang, Pei State Grid Key Laboratory of Information and Network Security State Grid Smart Grid Research Institute Co.Ltd Nanjing210003 China

The information system of low-voltage distribution network correctly records the topological structure of distribution network, which is the premise of fine management and safe operation of electric power *** the topology, the most important element is the household change relationship [1].At present, the low-voltage distribution network topology data in the information system is manually entered, so the correctness of the information can not be *** the continuous expansion of the scale of power grid, the structure of low-voltage distribution network is complex, the amount of data of marketing measurement [2], GIS and other information systems is increasing rapidly, each information system operates independently, and the data circulation is poor, which makes it difficult to identify the topology data, so it is urgent to carry out topology *** method proposed in this paper effectively solves the problem of electric data verification, and carries out corresponding experiments and demonstrations on the results, with high accuracy. © 2022 The Authors. Published by Elsevier B.V.

关键词： information systems

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