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

作者： Cheng, Haibo Liu, Wenjian Qu, Youyang Fu, Kexue Wang, Qinyuan City University of Macau Faculty of Data Science 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

ISBN: (纸本)9798350391367

Federated learning (FL) is a distributed learning method that reduces data transmission and privacy risks. However, the existence of a central server may become a target of attackers, resulting in an increased risk of user privacy leakage. The emergence of Decentralized Federated Learning (DFL) is posited to enhance data privacy and system reliability by reducing reliance on central servers and enhancing local data control, especially in the medical field, where personal health data is protected. This research investigates the application of (DFL) in the medical domain, focusing on its limitations. This study employs a systematic literature review, adhering to PRISMA guidelines, to comprehensively understand DFL's current landscape and challenges in healthcare. The key findings highlight four principal obstacles faced by DFL: security and privacy, communication efficiency, data and model heterogeneity, and incentive mechanisms. It also explores the potential for DFL to facilitate personalized healthcare treatments by large datasets distributed across various medical institutions. This research fills a significant void by systematically reviewing DFL technologies in healthcare. It provides insights into applications, challenges, and future directions for participants seeking secure and efficient healthcare data handling. © 2024 IEEE.

关键词： Federated learning

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Optimization Research of Er-doped Magneto-refractive Photonic Crystal Fiber Based on Surface Plasmon Resonance

Optimization Research of Er-doped Magneto-refractive Photoni...

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2022 International Conference on information Processing in Ocean Science and Technology, ICIPOST 2022

作者： Yao, Siyu Wang, Dongying Yu, Yang Zhang, Zhenrong Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronic and Information Guangxi University Nanning530004 China College of Liberal Arts and Sciences National University of Defense Technology Changsha410073 China State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China

In order to meet the demand for large-scale magnetic field testing, this paper proposes a D-shaped magneto-refractive photonic crystal fiber (MRPCF) based on surface plasmon resonance (SPR) by using the erbium-doped material. By varying the diameter, location, and number of layers of the air holes, the four different Models A, B, C, and D structures are created, and the corresponding magnetic field sensing properties are analyzed. The simulation findings demonstrate that the magnetic field sensitivities of Models A, B, C, and D are 28 pm/mT, 48 pm/mT, 36 pm/mT, and 21 pm/mT, respectively when the magnetic field strength is 5 - 405 mT. Meanwhile, the figure of merit (FOM) of the four MRPCF-SPR sensors is investigated, which have FOMs of 4.8×10-4 mT-1, 6.4×10-4 mT-1, 1.9×10-4 mT-1, 0.9×10-4 mT-1. Model B has high sensitivity and large FOM. It shows that altering the structural design of the fiber can significantly improve the magneto-refractive effect of MRPCF. The proposed MRPCF has numerous uses in the areas of geological research, earthquake and tsunami monitoring, and military navigation. It can also be expected to enable quasi-distributed magnetic field sensing. © Published under licence by IOP Publishing Ltd.

关键词： Magnetos

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Multi-Signal Fusion of Social Diffusion Graph with Bi-Directional Semantic Consistency

Multi-Signal Fusion of Social Diffusion Graph with Bi-Direct...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Huacheng Li Chunhe Xia Tianbo Wang Wanshuang Lin Changnan Jiang Chen Chen Yuan Zhao Key Laboratory of Beijing Network Technology Beihang University Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University School of Cyber Science and Technology Beihang University Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai

Devising diffusion graph to learn user representations is a crucial step in studying information propagation prediction. However, previous works mainly focused on structural and temporal features. To better incorporate content features, we introduce the Backward Decomposition and Forward Preservation mechanisms. The former involves decomposing content features for initializing node signals in the diffusion graph, thus fusing user features with content features. The latter aims to maintain node features generated by graph encoder consistent with the original content features. A series of experiments demonstrate that our model outperforms state-of-the-art models, and both mechanisms significantly enhance the prediction performance. Furthermore, our methods enables the features generated by the diffusion graph to more effectively incorporate features from various semantic spaces, whether encoded by language models or generated by graph embedding algorithms.

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Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning

arXiv

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arXiv 2023年

作者： Ye, Bo Gan, Kai Wei, Tong Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing211189 China Key Lab. of Computer Network and Information Integration Southeast University MoE China

In open-world semi-supervised learning, a machine learning model is tasked with uncovering novel categories from unlabeled data while maintaining performance on seen categories from labeled data. The central challenge is the substantial learning gap between seen and novel categories, as the model learns the former faster due to accurate supervisory information. Moreover, capturing the semantics of unlabeled novel category samples is also challenging due to the missing label information. To address the above issues, we introduce 1) the adaptive synchronizing marginal loss which imposes class-specific negative margins to alleviate the model bias towards seen classes, and 2) the pseudo-label contrastive clustering which exploits pseudo-labels predicted by the model to group unlabeled data from the same category together in the output space. Extensive experiments on benchmark datasets demonstrate that previous approaches may significantly hinder novel class learning, whereas our method strikingly balances the learning pace between seen and novel classes, achieving a remarkable 3% average accuracy increase on the ImageNet dataset. Importantly, we find that fine-tuning the self-supervised pre-trained model significantly boosts the performance, which is overlooked in prior literature. Our code is available at https://***/yebo0216best/LPS-main. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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A Simple Semi-Supervised Joint Learning Framework for Few-shot Text Classification 5

A Simple Semi-Supervised Joint Learning Framework for Few-sh...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Lu, Shaoshuai Chen, Long Wang, Wenjing Xu, Cai Zhao, Wei Guan, Ziyu Lu, Guangyue Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications China School of Computer Science and Technology Xidian University China

ISBN: (纸本)9781450396899

The lack of labeled data is the bottleneck restricting deep text classification algorithm. State-of-the-art for most existing deep text classification methods follow the two-step transfer learning paradigm: pre-training a large model on an auxiliary task, and then fine-tuning the model on a labeled data. Their shortcoming is the high cost of training. To reduce training costs as well as alleviate the need for labeled data, we present a novel simple Semi-Supervised Joint Learning (SSJL) framework for few-shot text classification that captures the rich text semantics from large user-tagged data (referred to as weakly-labeled data) with noisy labels while also learning correct category distributions in small labeled data. We refine the contrastive loss function to better exploit inter-class contrastive patterns, making contrastive learning more applicable to the weakly-labeled setting. Besides, an appropriate temperature hyper-parameter can improve model robustness under label noise. The experimental results on four real-world datasets show that our approach outperforms the other baseline methods. Moreover, SSJL significantly boosts the deep models' performance with only 0.5% (i.e. 32 samples) of the labeled data, showing its robustness in the data sparsity scenario. © 2022 ACM.

关键词： Semantics

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Continuous Verification of Catastrophic Recalling in Machine Unlearning via Adversarial Testing 9

Continuous Verification of Catastrophic Recalling in Machine...

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

作者： Qu, Y. Neil Ji, Zonghao Cui, Lei Zhang, Chong Liu, Lei Tian, Zhiyi Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250031 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250031 China Deakin University School of IT VIC3125 Australia Xidian University Guangzhou Institute of Technology Guangzhou510555 China University of Technology School of Computer Science SydneyNSW2007 Australia

ISBN: (纸本)9798350391367

Throughout the process of machine unlearning, in order to fulfill the requirements of machine unlearning and to comply with the rules of the right to be forgotten, it is natural to verify whether the unlearning model actually removes data points. Therefore, machine unlearning verification plays a crucial role. However, existing validation methods only perform one validation, and it is still challenging to determine whether subsequent updates of the model still maintain the unlearning effect. To address this challenge, we propose a novel continuous verification of catastrophic recalling in machine unlearning framework that aims to continuously evaluate whether a machine learning model effectively forgets specified data after an update. In this work, we first introduce adversarial testing as a crucial part of the overall framework. Verification then continues as the unlearned model is updated (adding or continuing to forget data points). We also analyzed the possibility that learning new data after unlearning may diminish the effect. Overall, our continuous unlearning verification framework provides a proactive approach to protecting individual privacy in the machine learning domain, thus facilitating the generation of more robust unlearning algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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Security analysis against the Trojan horse attack on practical polarization-encoding quantum key distribution systems

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Physical Review A 2024年第4期109卷 042608-042608页

作者： Tingting Luo Qiang Liu Xiaoran Sun Chunfeng Huang Ye Chen Zhenrong Zhang Kejin Wei Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and Technology Guangxi University Nanning 530004 China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information Guangxi University Nanning 530004 China

Quantum key distribution (QKD), which theoretically provides unconditional secure communication, has developed rapidly in the past decades. However, the practical QKD systems still have vulnerabilities due to device imperfections. Polarization-encoding systems are one of the most important branches in the field of QKD. To date, a variety of polarization-encoding schemes have been proposed to meet the requirements of high speed and robust modulation, but the security analysis of these schemes is often ignored. In this paper, we experimentally and theoretically evaluated the security of three typical polarization encoders against the Trojan horse attack (THA), which is recognized as one of the critical quantum attacks. We built a system to implement the THA and collected the reflection from QKD configuration with different encoders. According to the experimental results, we further analyzed the secure key rate and the resistance against THAs of the system while BB84 or measurement-device-independent QKD was applied. Our paper plays an important guiding role in the design of polarization encoders and the construction of secure QKD systems with realistic devices.

关键词： Quantum communication Quantum communication, protocols & technology

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Exploiting Conjugate Label information for Multi-Instance Partial-Label Learning

arXiv

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arXiv 2024年

作者： Tang, Wei Zhang, Weijia Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Lab. of Computer Network and Information Integration Southeast University MoE China School of Information and Physical Sciences The University of Newcastle NSW2308 Australia

Multi-instance partial-label learning (MIPL) addresses scenarios where each training sample is represented as a multi-instance bag associated with a candidate label set containing one true label and several false positives. Existing MIPL algorithms have primarily focused on mapping multi-instance bags to candidate label sets for disambiguation, disregarding the intrinsic properties of the label space and the supervised information provided by non-candidate label sets. In this paper, we propose an algorithm named ELIMIPL, i.e., Exploiting conjugate Label information for Multi-Instance Partial-Label learning, which exploits the conjugate label information to improve the disambiguation performance. To achieve this, we extract the label information embedded in both candidate and non-candidate label sets, incorporating the intrinsic properties of the label space. Experimental results obtained from benchmark and real-world datasets demonstrate the superiority of the proposed ELIMIPL over existing MIPL algorithms and other well-established partial-label learning algorithms. Copyright © 2024, The Authors. All rights reserved.

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Practical decoy-state quantum secure direct communication

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Science China(Physics,Mechanics & Astronomy) 2021年第12期64卷 68-75页

作者： Xin Liu Zijian Li Di Luo Chunfeng Huang Di Ma Minming Geng Junwei Wang Zhenrong Zhang Kejin Wei Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of ComputerElectronicsand InformationGuangxi UniversityNanning 530004China CAS Quantum Network Co. LtdShanghai 201315China

Quantum secure direct communication(QSDC) has been demonstrated in both fiber-based and free-space channels using attenuated lasers. Decoy-state QSDC by exploiting four decoy states has been proposed to address the problem of photon-numbersplitting attacks caused by the use of attenuated lasers. In this study, we present an analysis of the practical aspects of decoy-state QSDC. First, we design a two-decoy-state protocol that only requires two decoy states, thereby significantly reducing experimental complexity. Second, we successfully perform full parameter optimization for a real-life QSDC system by introducing a genetic algorithm. Our simulation results show that the two-decoy-state protocol could be the best choice for developing a practical QSDC system. Furthermore, full optimization is crucial for a high-performance QSDC system. Our work serves as a major step toward the further development of practical decoy-state QSDC systems.

关键词： quantum secure direct communication decoy-state method full parameter optimization

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Research on liquid crystal display technology based on regional dynamic dimming algorithm 9

Research on liquid crystal display technology based on regio...

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

作者： Yu, Zhitao Sun, Peng Zhou, Mingle Liu, Qianlong Zhao, Shilong Hisense Video Technology Co. Ltd. Qing Dao China Hisense Visual Technology Co. Ltd. Qing Dao 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

In vehicle liquid crystal display (LCD) technology has attracted much attention for its wide range of applications in automotive infotainment systems. However, conventional LCD technologies have limitations in terms of energy consumption and display quality, especially in the context of the increasing demand for high contrast and low energy consumption. To address these challenges, this paper carries out a study of LCD technology based on an area dynamic dimming algorithm. The technology significantly improves the contrast ratio of LCDs and effectively reduces energy consumption by dividing the backlight module into multiple independently adjustable regions and dynamically adjusting the backlight brightness of each region according to the image content. The results show that the region dynamic dimming algorithm proposed in this paper can effectively adjust the brightness of the backlight according to the image content, achieving higher display contrast and lower energy consumption. © 2024 IEEE.

关键词： Liquid crystal displays

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