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2024 OES China Ocean Acoustics, COA 2024

作者： Liu, Weilu Zhou, Tian Chen, Baowei Du, Weidong Harbin Engineering University Acoustic Science and Technology Laboratory Harbin150001 China Ministry of Industry and Information Technology Key Laboratory of Marine Information Acquisition and Security Harbin150001 China Harbin Engineering University College of Underwater Acoustic Engineering Harbin150001 China

ISBN: (纸本)9798350340297

Interferometric phase filters are the cornerstone of sonar for high-precision interferometry. This study focuses on the use of interferometric phase filter for forward-looking sonar, proposing an adaptive short-time Fourier transform filtering algorithm from the perspective of a frequency domain. The algorithm analyzes the image characteristics of forward-looking sonar and proposes a two-dimensional Chebyshev-Kaiser window. The window is determined by the signal-to-noise ratio of the interferogram so that the phase filter can dynamically adjust the filter intensity. The filter reduces interferometric phase noise, thus improving the accuracy of forward-looking sonar interferometric measurement. Simulation experiment and lake experiment show that the filter proposed in this study eliminates over 95% of residues, providing reliable data support for subsequent interferometric phase unwrapping and underwater elevation models. © 2024 IEEE.

关键词： Adaptive filters

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Unbiased Multi-Label Learning from Crowdsourced Annotations 41

Unbiased Multi-Label Learning from Crowdsourced Annotations

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41st International Conference on Machine Learning, ICML 2024

作者： Xia, Mingxuan Huang, Zenan Wu, Runze Lyu, Gengyu Zhao, Junbo Chen, Gang Wang, Haobo School of Software Technology Zhejiang University Ningbo China State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Fuxi AI Lab NetEase Inc. Hangzhou China Faculty of Information Technology Beijing University of Technology Beijing China

This work studies the novel Crowdsourced Multi-Label Learning (CMLL) problem, where each instance is related to multiple true labels but the model only receives unreliable labels from different annotators. Although a few Crowdsourced Multi-Label Inference (CMLI) methods have been developed, they require both the training and testing sets to be assigned crowdsourced labels and focus on true label inferring rather than prediction, making them less practical. In this paper, by excavating the generation process of crowdsourced labels, we establish the first unbiased risk estimator for CMLL based on the crowdsourced transition matrices. To facilitate transition matrix estimation, we upgrade our unbiased risk estimator by aggregating crowdsourced labels and transition matrices from all annotators while guaranteeing its theoretical characteristics. Integrating with the unbiased risk estimator, we further propose a decoupled autoencoder framework to exploit label correlations and boost performance. We also provide a generalization error bound to ensure the convergence of the empirical risk estimator. Experiments on various CMLL scenarios demonstrate the effectiveness of our proposed method. The source code is available at https://***/MingxuanXia/CLEAR. Copyright 2024 by the author(s)

关键词： Risk perception

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Multi-frame non-coherent track-before-detect method for weak tones in passive sonar

Multi-frame non-coherent track-before-detect method for weak...

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2023 International Conference on Frontiers of Ocean Science and technology, ICFOST 2023

作者： Zhang, Liu Piao, Shengchun Guo, Junyuan Wang, Xiaohan National Key Laboratory of Underwater Acoustic Technology Harbin Engineering University Harbin150001 China Key Laboratory of Marine Information Acquisition and Security Harbin Engineering University Ministry of Industry and Information Technology Harbin150001 China College of Underwater Acoustic Engineering Harbin Engineering University Harbin150001 China

In order to solve the problem of performance degradation of track-before-detect (TBD) methods using single frame information when received signals exhibit a large fluctuation. A TBD method utilizing multi-frame information is proposed. Specifically, the novel measurement model and likelihood ratio function based on the non-coherent accumulation of multi-frame data are established. Multi-frame integration greatly improves the signal-to-noise ratio (SNR) of measurements. Furthermore, the derived likelihood ratio function can more effectively describe the statistical characteristics of the spectrum measurements. Combining these two advantages can improve the detection performance of weak tones for passive sonar. The simulation and experimental data results both demonstrate the advantages of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Signal to noise ratio

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Improved Cloth Simulation Filtering for Multi-Beam Bathymetric Point Cloud Using the Maximum Likelihood Method

Improved Cloth Simulation Filtering for Multi-Beam Bathymetr...

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2024 OES China Ocean Acoustics, COA 2024

作者： Yan, Zhenyu Zhou, Tian Liu, Weilu Du, Weidong Harbin Engineering University National Key Laboratory of Underwater Acoustic Technology Harbin150001 China Ministry of Industry and Information Technology Harbin Engineering University Key Laboratory of Marine Information Acquisition and Security Harbin150001 China College of Underwater Acoustic Engineering Harbin Engineering University Harbin150001 China

ISBN: (纸本)9798350340297

Cloth simulation filtering (CSF) is an automatic filtering algorithm used for airborne light detection and ranging (LiDAR) point clouds, with few parameters and high accuracy, and it is suitable for various landscapes. However, owing to the differences between the acoustic bathymetric and optical ranging principles, CSF may require more artificial intervention to handle multi-beam bathymetric point clouds, and the anomalies generated by the seafloor secondary echo result in diminishing the filtering accuracy of CSF. This paper presents an improved CSF based on maximum likelihood estimation (MLE), called MLE-CSF, which weakens the impact of negative anomalies on the filtering effect by determining the estimated bathymetric point for each cloth particle. In this method, a linear observation model of estimated bathymetry for cloth particles was established, and then the MLE was used to calculate the estimated bathymetric values of each particle in the vertical direction. Next, the CSF was carried out, and the simulated bathymetric value of each particle was obtained when the simulation terminated. Finally, the multi-beam bathymetric point cloud was divided as the off-seafloor or seafloor point set by comparing the simulated bathymetric value with the estimated one. To evaluate the filtering performance, the MLE-CSF was applied to the bathymetric data collected around the Chi Island in Qingdao, China. The filtering result reveals that the MLE-CSF outperforms CSF, which decreases the total error from 2.58 % to 1.92 % and increases Cohen's kappa coefficient from 77.42 % to 82.26 % in the research area. The MLE-CSF is more suitable and stable for multi-beam bathymetric data than the morphology-based filter. © 2024 IEEE.

关键词： Bathymetry

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Multipassive Sensor Multitarget Efficient Trajectory-Measurement Association Algorithm Based on the Submodular Optimization Theory

Multipassive Sensor Multitarget Efficient Trajectory-Measure...

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2024 OES China Ocean Acoustics, COA 2024

作者： Lyu, Ying Qi, Bin Liang, Guolong Harbin Engineering University National Key Laboratory of Underwater Acoustic Technology Harbin150001 China Harbin Engineering University Key Laboratory of Marine Information Acquisition and Security Ministry of Industry and Information Technology Harbin150001 China College of Underwater Acoustic Engineering Harbin Engineering University Harbin150001 China

ISBN: (纸本)9798350340297

data association is critical for resolving multisensor and multitarget tracking challenge. As the number of sensors and targets grows, the complexity of linking measurements to specific targets also grows. The question of efficiently associating ideal measurements for each target, particularly in multipassive sensor systems, remains unresolved. Inspired by least squares estimation, a cost function is determined that considers angle deviation and distance cost, the original objective function, and a set of constraints. The original objective function is then changed into a submodular function with the property of diminishing marginal benefits after an analysis of the problem's practical importance. Finally, based on the submodular optimization theory, an effective multisensor trajectory-measurement association algorithm is proposed. The simulation results show that using the proposed algorithm, each target only needs to be associated with a small number of high-accuracy measurement azimuths to achieve tracking performance comparable with the previous algorithm. © 2024 IEEE.

关键词： Cost functions

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EPRFL:An Efficient Privacy-Preserving and Robust Federated Learning Scheme for Fog Computing

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China Communications 2025年第4期22卷 202-222页

作者： Ke Zhijie Xie Yong Syed Hamad Shirazi Li Haifeng School of Computer Technology and Application Qinghai UniversityXining 810016China School of Computer and Information Science Qinghai Institute of TechnologyXining 810016China Guangdong Key Laboratory of Blockchain Security Guangzhou UniversityGuangzhou 510006China Department of Information Technology Hazara UniversityBaffa 21110Pakistan Qinghai Provincial Key Laboratory of Big Data in Finance and Artificial Intelligence Application Technology Xining 810016China

Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple *** combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data *** address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing ***,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node ***,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication *** minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive *** analysis demonstrates that EPRFL offers robust security and low *** experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.

关键词： federated learning fog computing internet of things privacy-preserving robustness

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Ppt4j: Patch Presence Test for Java Binaries 24

Ppt4j: Patch Presence Test for Java Binaries

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Pan, Zhiyuan Hu, Xing Xia, Xin Zhan, Xian Lo, David Yang, Xiaohu Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Huawei Software Engineering Application Technology Lab Hangzhou China The Hong Kong Polytechnic University Hong Kong School of Computing and Information Systems Singapore Management University Singapore

ISBN: (纸本)9798400702174

The number of vulnerabilities reported in open source software has increased substantially in recent years. security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results. In this paper, we propose a new patch presence test framework named Ppt4j (Patch Presence Test forJava Binaries). Ppt4j is designed for open-source Java libraries. It takes Java binaries (i.e. bytecode files) as input, extracts semantic information from patches, and uses feature-based techniques to identify patch lines in the binaries. To evaluate the effectiveness of our proposed approach Ppt4j, we construct a dataset with binaries that include 110 vulnerabilities. The results show that Ppt4j achieves an F1 score of 98.5% with reasonable efficiency, improving the baseline by 14.2%. Furthermore, we conduct an in-the-wild evaluation of Ppt4j on JetBrains IntelliJ IDEA. The results suggest that a third-party library included in the software is not patched for two CVEs, and we have reported this potential security problem to the vendor. © 2024 ACM.

关键词： Open source software

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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What's the Real: A Novel Design Philosophy for Robust AI-Synthesized Voice Detection 24

What's the Real: A Novel Design Philosophy for Robust AI-Syn...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Hai, Xuan Liu, Xin Tan, Yuan Liu, Gang Li, Song Niu, Weina Zhou, Rui Zhou, Xiaokang School of Information Science and Engineering Lanzhou University Lanzhou China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Business Data Science Kansai University Osaka Japan

ISBN: (纸本)9798400706868

Voice is one of the most widely used media for information transmission in human society. While high-quality synthetic voices are extensively utilized in various applications, they pose significant risks to content security and trust building. Numerous studies have concentrated on AI-synthesized voice detection to mitigate these risks, with many claiming to achieve promising performance. However, recent research has demonstrated that fake voice detectors suffer from serious overfitting to speaker-irrelative features (SiFs) and cannot be used in real-world scenarios. In this paper, we analyze the limitations of existing fake voice detectors and propose a new design philosophy, guiding the detection model to prioritize learning human voice features rather than the difference between the human voice and the synthetic voice. Based on this philosophy, we propose a novel AI-synthesized voice detection framework named SiFSafer, which uses pre-trained speech representation models to enhance the learning of feature distribution in human voices and the adapter fine-tuning to optimize the performance. The evaluation shows that the average EERs of existing fake voice detectors in the ASVspoof datasets can exceed 20% if the SiFs like silence segments are removed, while SiFSafer achieves an EER of less than 8%, indicating that SiFSafer is robust to SiFs and strongly resistant to existing attacks. © 2024 ACM.

关键词： Fake detection

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PFDF: Privacy Preserving Federated Decision Forest for Classification 24th

PFDF: Privacy Preserving Federated Decision Forest for Clas...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Tongyaqi Qi, Qingqiang Hu, Chengyu Li, Xuelei Tang, Peng Guo, Shanqing School of Cyber Science and Technology Shandong University Shandong Qingdao266237 China Quan Cheng Laboratory Shandong Jinan250103 China The Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Shandong University Shandong Jinan250100 China Electronic Information Industry Co. Ltd. Beijing China

ISBN: (纸本)9789819615247

The rapid development of data analysis technology and easily accessible datasets enable the construction of comprehensive analysis models, promoting decision-making processes in various fields. Decision trees, known for their high interpretability and cost-effectiveness, has become a common choice for decision-making in areas like housing price prediction and medical forecasting. However, in federated learning, there exists a risk of individual privacy leakage. To address this challenge, we propose a new privacy-preserving decision tree boosting model (PFDF). To protect data holders’ privacy, we adopt differential privacy technology to perturb sensitive data that might lead to privacy leakage. Under the premise of privacy preservation, this model includes a novel approach for designing the global attribute selection and leaf node judgment scheme, considering the data imbalance among data holders. Additionally, continuous splitting values are generated using a clustering method. To enhance classification accuracy, our "multi-perspective" decision tree boosting scheme considers the optimal attributions of previously constructed trees. Accuracy tests on several benchmark datasets demonstrate that our scheme outperforms classical and the state-of-the-art approaches currently known to us in both centralized and federated learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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