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

作者： Zhaolin Wei Dengpan Ye Jiacheng Deng Yuhan Lin Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Music deepfake detection is aimed at identifying whether songs are generated by AI. Current methods usually separate vocals from background music for detection, but this could leave residual forgery information in the background. Our study demonstrates for the first time that incorporating background forgery information with vocals can improve detection accuracy. Furthermore, our findings show that using background features can reduce EER by an average of about 2% on existing frameworks. Based on this observation, we propose a novel Hybrid Frontend that captures generalized features from both vocal and background music. The Hybrid Frontend comprises two branches: vocal and background music part. Specifically, the vocal part uses the Sinconv encoder as a deeply embedded feature extractor. The latter captures background variation by fine-tuning the pre-trained model with adapters. Experimental results demonstrate that our method outperforms the vocal-only detection on WildSVDD dataset, achieving an EER of 8.53%, which is 1.3% lower.

关键词： Deepfakes Adaptation models Signal processing Feature extraction Forgery Acoustics Multiple signal classification Data mining Speech processing Artificial intelligence

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Network intrusion detection based on feature fusion of attack dimension

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Journal of Supercomputing 2025年第6期81卷 1-33页

作者： Sun, Xiaolong Gu, Zhengyao Zhang, Hao Gu, Jason Liu, Yanhua Dong, Chen Ye, Junwei College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Chinese Ministry of Education Fuzhou350116 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3J 1Z1 Canada

Network traffic anomaly detection involves the rapid identification of intrusions within a network through the detection, analysis, and classification of network traffic data. The variety of cyberattacks encompasses diverse attack principles. Employing an indiscriminate feature selection strategy may lead to the neglect of key features highly correlated with specific attack types. This oversight could diminish the recognition rate for that category, thereby impacting the overall performance of the detection model. To address this issue, this paper proposes a network traffic anomaly detection model based on the fusion of attack-dimensional features. Firstly, construct binary classification datasets independently for each attack class and perform individual feature selection to extract positively correlated features for each class. The features are then fused by employing a combination methods. Subsequently, based on the fused sub-datasets, base classifiers are trained. Finally, an ensemble learning approach is introduced to integrate the predictions of individual classifiers, enhancing the robustness of the model. The proposed approach, validated on NSL-KDD and UNSW-NB15 benchmark datasets, outperforms the latest methods in the field by achieving a 2% and 7% increase in precision on weighted averages. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Feature Selection

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Boosting the consolidation and antibacterial dual-functionalities of Ca(OH)₂ mural restoration materials via supporting Ag/g-C₃N₄ on hexagonal nano-Ca(OH)₂

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Journal of Cultural Heritage 2025年 73卷 172-184页

作者： Zhao, Ting Kou, Yu-Tong Ding, Nian-Chen Zhu, Jian-Feng Yang, Wen-Zong Liu, Yi Qin, Yi Key Laboratory of Materials and Technology for Unearthed Cultural Heritage Conservation Ministry of Education School of Materials Science and Engineering Shaanxi Key Laboratory of Green Preparation and Functionalization for Inorganic Materials Shaanxi University of Science and Technology Shaanxi Xi'an 710021 China Scientific Research Base of Conservation & Restoration for Mural as Collection and Materials Science in State Administration for Cultural Heritage Xi'an 710061 China

Ca(OH)2 is a well-known commercial inorganic consolidation material, which is used to restore tomb murals. Herein, nano-Ca(OH)2 with a high specific surface grown by molecular confinement method was supported with pho... 详细信息

Ca(OH)₂ is a well-known commercial inorganic consolidation material, which is used to restore tomb murals. Herein, nano-Ca(OH)₂ with a high specific surface grown by molecular confinement method was supported with photocatalyst Ag/g-C₃N₄ to form a highly permeable Ag/g-C₃N₄/Ca(OH)₂, which achieved consolidation and antibacterial properties of simulated murals. Ag/g-C₃N₄/Ca(OH)₂ forms a unique gradient permeation structure on tomb murals. In this structure, the Ca(OH)₂ nanomaterial penetrates deeply, while the Ag/g-C₃N₄-rich layers remain on the mural surfaces. The Ag/g-C₃N₄-rich layer effectively protects the murals from bacteria. The results indicate that the Ag/g-C₃N₄/Ca(OH)₂ nanocomposite significantly enhances the mural's strength and antibacterial properties when the silver ion doping content is 0.24. Its strengthening performance is 2 times higher than commercial Ca(OH)₂, while its antibacterial rate reaches 58.38 %, primarily due to its high permeability and stability. Further aging experiments indicate that the synthesized Ag_0.24/g-C₃N₄/Ca(OH)₂ nanocomposite has no adverse effect on the mural's pigment layer. This work offers a new method for developing effective materials to protect tomb murals. © 2025 Elsevier Masson SAS

关键词： Antimicrobial Consolidation Nano-Ca(OH)2 Nanocomposite Tomb mural

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Dual Space Representation Learning for Skeleton-based Action Recognition

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IEEE Signal Processing Letters 2025年 32卷 2104-2108页

作者： Yang, Yuheng Chen, Haipeng Liu, Zhenguang Hu, Sihao Jiao, Yingying Jilin University College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Zhejiang University School of Cyber Science and Technology Hangzhou310007 China Georgia Institute of Technology College of Computing GA United States

Skeleton-based action recognition is crucial for machine intelligence. Current methods generally learn from 3D articulated motion sequences in the straightforward Euclidean space. Yet, the vanilla Euclidean space may not be the optimal choice for modeling the intricate correlations among human body joints. This challenge arises from the non-Euclidean nature of human anatomy, where joint correlations often vary non-linearly during movement. To address this, we propose a dual space representation learning method. Specifically, we represent the motion sequences in Hyperbolic space, leveraging its intrinsic properties to capture the non-Euclidean latent anatomy of human motions. We then incorporate the motion features from both Hyperbolic and Euclidean spaces, allowing us to precisely model the non-linear joint correlations while effectively sketching human poses. The proposed method empirically achieves state-of-the-art performance on the NTU RGB+D 60, NTURGB+D 120, and NW-UCLA datasets. © 1994-2012 IEEE.

关键词： Digital arithmetic

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On the Performance of Active RIS-Assisted Mixed RF-THz Relaying Systems

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IEEE Internet of Things Journal 2025年第10期12卷 14938-14951页

作者： Yin, Yiyang Yang, Liang Li, Xingwang Liu, Hongwu Guo, Kefeng Li, Yingsong Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Jishou University School of Communication and Electronic Engineering Jishou416000 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454000 China Shandong Jiaotong University School of Information Science and Electrical Engineering Jinan250357 China Space Engineering University School of Space Information Beijing101407 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

We investigate the performance of an active reconfigurable intelligent surface (RIS)-assisted mixed radio frequency (RF)-terahertz (THz) relaying system, where the RF signal reaches the relay through the active RIS and is then transmitted to the user via the THz channel. Under this scenario, we analyze the system performance with the relay employing amplify-and-forward (AF) and decode-and-forward (DF) protocols. More specifically, we derive the exact expressions for the cumulative distribution function (CDF) of the end-to-end signal-to-noise ratio (SNR) for both relaying protocols. Based on this, we obtain the exact expressions for the outage probability (OP), average bit error rate (ABER), and average channel capacity (ACC). Furthermore, to gain deeper insights, we derive the asymptotic expressions at high SNRs and obtain diversity order (DO) of the system. Moreover, we extend the analysis to the variable gain relaying scheme. The findings reveal that the DOs for both relaying protocols are determined by the THz channel parameters, and the DO under the AF relaying protocol is twice that of the DF relaying protocol. Finally, we validate that active RIS (A-RIS) can more effectively assist the performance of the mixed RF-THz relaying system compared to passive RIS. © 2014 IEEE.

关键词： Distribution functions

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Verifiable and Forward-Secure Multi-keyword Query in Internet of Medical Things

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IEEE Internet of Things Journal 2025年

作者： Li, Hongjun He, Debiao Feng, Qi Luo, Min Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China National Key Laboratory of Security Communication Chengdu China

The Internet of Medical Things (IoMT) plays a pivotal role in modern healthcare systems, enhancing patients' medical experiences and improving the efficiency of public medical services. However, concerns regarding security and privacy may hinder the widespread implementation and development of IoMT in practical applications. Dynamic Searchable Symmetric Encryption (DSSE) can maintain search capabilities on encrypted data, even when files are dynamically added or deleted. Earlier DSSE schemes typically support only single keyword query and provide forward security under the assumption of semi-honest servers, which significantly limits their applicability in real-world scenarios. To resolve these limitations, we propose a forward-secure and verifiable DSSE scheme that supports multi-keyword conjunctive query. Our scheme ensures forward security by utilizing a chain structure and guarantees correctness and completeness through RSA accumulator and Homomorphic Message Authentication Code (HMAC). Furthermore, we integrate the forward index and inverted index to enable efficient retrieval. Through comprehensive security and performance analysis, we demonstrate that our scheme effectively protects users' privacy while maintaining low computation and communication overheads. Finally, some experimental evaluations are conducted to verify both the correctness and efficiency of the proposed scheme. © 2014 IEEE.

关键词： Structured Query Language

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Accuracy verification of sulfur dioxide data based on ultraviolet hyperspectral solar radiometer observations 3

Accuracy verification of sulfur dioxide data based on ultrav...

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3rd International Conference on Environmental Remote Sensing and Geographic Information Technology, ERSGIT 2024

作者： Zeng, Hao Wang, Shun Xun, Lina The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei China

ISBN: (纸本)9781510690219

One of the primary air pollutants that has a detrimental effect on both the environment and human health is sulfur dioxide. The ecosystem and climate greatly depend on the high-precision monitoring of sulfur dioxide. In this study, SO2 ground-based data in Xi'an and Hangzhou were calculated using the UV continuous spectral inversion method, and the accuracy was confirmed using OMI satellite data that had undergone bilinear spatial interpolation. Although there are still some discrepancies in the comparative analysis results, the results indicate a good correlation between the ground-based measurement results of the UV hyperspectral solar radiometer and OMI data products;the correlation numbers are 0.938 and 0.941, respectively, and the relative errors are roughly 10%. The ultraviolet hyperspectral solar radiometer can complement this study's findings with accuracy and is a valuable tool for environmental monitoring. © 2025 SPIE.

关键词： accuracy verification correlation sulfur dioxide distribution ultraviolet hyperspectral solar radiometer

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MCHNet: An Efficient Cross Attention-Guided Hierarchical Multiscale Network for Segmentation of Organs at Risk in CT Images

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IEEE Transactions on Radiation and Plasma Medical Sciences 2025年第5期9卷 598-612页

作者： Guo, Huimin Gu, Yin Du, Wu Chen, Boyang Cui, Ming Zhang, Teng Sun, Deyu Qian, Wei Ma, He Northeastern University College of Medicine and Biological Information Engineering Shenyang110169 China Cancer Hospital of Dalian University of Technology Department of Radiation Oncology Gastrointestinal and Urinary and Musculoskeletal Cancer Shenyang110042 China Northeastern University College of Medicine and Biological Information Engineering Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110169 China

In radiotherapy, precisely contoured organs at risk (OARs) near the target areas are essential for effective treatment planning. Manual delineation of OARs is labor-intensive and varies among experts. Deep learning has improved accuracy and consistency, but current methods use complex architectures with many parameters, risking overfitting on small medical image datasets. Addressing this, this article introduces MCHNet, a cross attention (CA)-guided hierarchical multiscale segmentation network based on CT images. MCHNet adopts a typical U-shaped structure, with MobileVit as its backbone. An efficient CA-guided block is utilized to enhance feature extraction while minimizing model parameters. Additionally, a novel skip-connection strategy is proposed to preserve critical medical image information during multiple down-sampling operations and bridge the gap between deep and shallow features. We conducted extensive experiments on three public datasets, i.e., multiatlas labeling beyond the cranial vault dataset, Segmentation of thoracic OARs dataset, and multimodality abdominal multiorgan segmentation challenge 2022 dataset. Experiment results demonstrate that the proposed method outperforms other related Transformer-based or hybrid models in terms of computational complexity, quantitative and qualitative results. We believe that the proposed method can offer an optimal blend of precision and efficiency that advances the capabilities of OARs segmentation in radiotherapy. © 2017 IEEE.

关键词： Image segmentation

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LSDK-LegalSum: improving legal judgment summarization using logical structure and domain knowledge

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Journal of King Saud University - Computer and Information Sciences 2025年第1期37卷 1-15页

作者： Gao, Wei Yu, Shuai Qin, Yongbin Yang, Caiwei Huang, Ruizhang Chen, Yanping Lin, Chuan Engineering Research Center of Text Computing & CognitiveIntelligence Ministry of Education Guizhou University Guizhou Huaxi Guiyang 550025 China State Key Laboratory of Public Big Data Guizhou University Guizhou Huaxi Guiyang 550025 China College of Computer Science and Technology Guizhou University Guizhou Huaxi Guiyang 550025 China

Legal judgment summarization enhances the efficiency and accuracy of processing and retrieving similar cases by distilling key information from legal judgment documents. Legal judgments, which record the trial process and outcome, tend to have lengthy texts. However, most existing methods for legal judgment summarization struggle to effectively handle long texts and focus only on the serialized text information, neglecting the auxiliary role of the document’s logical structure in generating summaries. Additionally, legal judgment documents contain extensive domain knowledge formed by professional legal terminology, which is crucial for accurately understanding and analyzing the content of the documents. We propose a method to improve the quality of legal judgment summaries by utilizing logical structures and domain knowledge (LSDK-LegalSum). Specifically, we first use a multi-label document classification approach to divide the legal judgment into four parts: ‘Type’, ‘Claim’, ‘Fact’ and ‘Result’, then generate a summary of the ‘Type’ part, and combine it with the ‘Claim’ part to create a new summary, and so forth until the final summary is produced. Moreover, the case descriptions in legal judgments cause substantial textual noise;therefore, we employ a text-denoising method that integrates domain knowledge to further enhance the performance of the summaries. Extensive experiments on the CAIL2020 legal summarization dataset have shown that our method achieves improvements of 8.37, 16.64, and 13.07 percentage points in Rouge-1, Rouge-2, and Rouge-L scores respectively over the state-of-the-art models. This demonstrates that our method not only produces concise and semantically complete summaries but also ensures that the content is logically rigorous and well-organized, meeting practical needs more effectively. © The Author(s) 2025.

关键词： Abstractive summarization Domain knowledge Legal judgment summarization Logical structure Low-source summarization

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CIEGCL: Counterfactual Intervention Enhancing Graph Contrastive Learning in Implicit Feedback

CIEGCL: Counterfactual Intervention Enhancing Graph Contrast...

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

作者： Zijian Huang Tian Jiang Yan Feng Zerui Wen Xiaohui Cui Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Graph neural network (GNN) is a powerful approach for graph-based recommender systems. Recently, GNNs integrated with contrastive learning have achieved superior performance in recommendations due to their data augmentation techniques for sparse data. These methods are susceptible to noisy perturbations and are affected by data bias stemming from missing-not-at-random (MNAR) issues, which are common in implicit feedback scenarios. In this paper, we propose a Counterfactual Intervention Enhancing Graph Contrastive Learning (CIEGCL) paradigm. Our model applies counterfactual intervention in the positive sampling process for contrastive augmentation and generates enhanced facts (e.g., random perturbations) for contrast penalty, improving model generalization. Additionally, our dual contrastive channels separately handle click prediction and recommendation tasks to reduce the impact of data bias on the final recommendations. Empirical studies on five real-world datasets demonstrate the effectiveness of our model over the state-of-the-arts. Further theoretical and experimental analysis confirms the rationale behind CIEGCL. We release our code at https://***/huang-zi-jian/CIEGCL.

关键词： Perturbation methods Noise Contrastive learning Resists Data augmentation Graph neural networks Data models Noise measurement Speech processing Recommender systems

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