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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Yujun Chen, Yanqu Wang, Jiakai Hu, Jin Tao, Renshuai Liu, Xianglong State Key Laboratory of Complex & Critical Software Environment Beihang University China School of Computer Science and Engineering Beihang University China College of Computer Science Beijing University of Technology China Zhongguancun Laboratory China School of Computer and Information Technology Beijing Jiaotong University China Institute of Data Space Hefei Comprehensive National Science Center China

ISBN: (纸本)9798350368741

There is a growing concern about adversarial attacks against automatic speech recognition (ASR) systems. Although research into targeted universal adversarial examples (AEs) has progressed, current methods are constrained by inefficient exploitation of audio features, demonstrating insufficient attack ability and robustness in the physical world. To solve this problem, we propose a phoneme-tailored attack (PTA) to improve the quality of the generated AEs. Specifically, to improve attack ability, we propose a Diverse Audio Composition Enrichment method, which enhances the utilization of audio features through phoneme-level slicing and recombination. To adapt AEs to complex environments, we propose a Natural Noise Pattern Guidance method to align AEs with natural noise patterns to improve their robustness. Experiments show that our method achieves an average accuracy of more than 72.34% and 98% with and without a norm constraint, and also demonstrates excellent performance in terms of generalization across datasets and resilience to MP3 compression. © 2025 IEEE.

关键词： Automatic speech recognition natural noise pattern phoneme-level targeted universal adversarial examples

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An Efficient Direct Downlink Sensing Method Using 5G NR SSB Signals in Perceptive Mobile Networks

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IEEE Internet of Things Journal 2025年第11期12卷 15360-15369页

作者： Li, Hang Xiang, Yang Guo, Qinghua Liu, Lizhe Huang, Xiaojing Cheng, Zhiqun Pang, Yashan Hangzhou Dianzi University School of Electronics and Information Hangzhou310018 China Academy of Network and Communications of CETC National Key Laboratory of Advanced Communication Networks Hebei Shijiazhuang050081 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia University of Technology Sydney Global Big Data Technologies Centre UltimoNSW2007 Australia

In perceptive mobile networks (PMNs), using 5G New Radio (NR) signals for direct sensing poses a significant challenge to practical implementation due to the high computational complexity involved in estimating sensing parameters. In this paper, an efficient sensing method is proposed to incorporate both downlink active sensing and passive sensing to estimate multiple sensing parameters, including delays, angle of arrival (AoA), angle of departure (AoD) and Doppler. In particular, it exploits the synchronization signal blocks (SSBs) to facilitate sensing with multiple remote radio units (RRUs). To reduce the computational complexity of direct sensing, a sparse model is developed to decouple multiple sensing parameter estimation, enabling efficient sensing method design. Then, leveraging unitary approximate message passing (UAMP) and sparse Bayesian learning (SBL), we propose an efficient method to achieve parameter estimation and association with corresponding RRUs. This method is further extended to general scenarios involving multiple path components with the same delay. Extensive simulations demonstrate the effectiveness of the proposed method, showing that it outperforms existing ones in terms of sensing accuracy and complexity. © 2014 IEEE.

关键词： 5G mobile communication systems

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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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An intelligent solar flare prediction model based on X-ray flux curves using Long Short-Term Memory

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Astronomical Techniques and Instruments 2025年第2期2卷 65-72页

作者： Yan Gao Li Zhang Long Xu College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang 550025China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

Solar flares are violent solar outbursts which have a great influence on the space environment surrounding Earth,potentially causing disruption of the ionosphere and interference with the geomagnetic field,thus causing magnetic ***,it is very important to accurately predict the time period of solar *** paper proposes a flare prediction model,based on physical images of active solar *** employ X-ray flux curves recorded directly by the Geostationary Operational Environmental Satellite,used as input data for the model,allowing us to largely avoid the influence of accidental errors,effectively improving the model prediction efficiency.A model based on the X-ray flux curve can predict whether there will be a flare event within 24 *** reverse can also be verified by the peak of the X-ray flux curve to see if a flare has occurred within the past 24 *** True Positive Rate and False Positive Rate of the prediction model,based on physical images of active regions are 0.6070 and 0.2410 respectively,and the accuracy and True Skill Statistics are 0.7590 and *** model can effectively improve prediction efficiency compared with models based on the physical parameters of active regions or magnetic field records,providing a simple method for solar flare prediction.

关键词： Neural Network Long Short-Term Memory Solar flare prediction X-ray flux curve

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MASS FLOW DETECTION TECHNOLOGY FOR SEED AND FERTILISER PARTICLES AND ITS APPLICATION IN UAV-BASED SPREADING

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INMATEH - Agricultural engineering 2025年第1期75卷 584-599页

作者： Wang, Yanshuang Song, Cancan Gao, Yongqi Lan, Yubin Wang, Guobin Ming, Rui College of Agricultural Engineering and Food Science Shandong University of Technology Zibo China Shandong University of Technology Sub-center of National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology Zibo China Research of Institute of Ecological Unmanned Farm Shandong University of Technology Zibo China School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China

The processes of sowing and fertiliser application represent a significant aspect of agricultural production. In order to achieve efficient and precise seeding and fertiliser application, mass flow detection of seed and fertiliser particles can facilitate real-time monitoring and precise decision-making for intelligent seeding and fertiliser application. However, the diversity of seed and fertiliser particle types and particle flow modes presents a challenge for existing detection methods, particularly in meeting the varying operational requirements, especially for particle mass flow detection in low-altitude and high-speed sowing operations of agricultural drones. In such cases, the overlap and flow rate will have a significant impact on the detection results due to the large displacement and the continuous high-throughput dense phase of the particle flow. This paper provides a summary of the existing seed and fertiliser particle mass flow detection techniques and their underlying working principles. It compares the direct detection based on mass with the indirect detection methods based on velocity and concentration, and analyses their respective advantages, disadvantages and applicability. It also explores the possibility of optimising the existing detection methods for the specific needs of agricultural UAVs and considers the potential introduction of cutting-edge science and technology in order to develop an efficient, accurate and convenient detection system to meet the growing market demand. © (2025), (INMA Bucharest). All Rights Reserved.

关键词： Precision agriculture

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NUMERICAL ENERGY DISSIPATION FOR TIME-FRACTIONAL PHASE-FIELD EQUATIONS

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Journal of Computational Mathematics 2025年第3期43卷 515-539页

作者： Chaoyu Quan Tao Tang Jiang Yang School of Science and Engineering The Chinese University of Hong KongShenzhen 518172China Division of Science and Technology BNU-HKBU United International CollegeZhuhai 519087China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International CollegeZhuhai 519087China Department of Mathematics SUSTech International Center for Mathematics&National Center for Applied Mathematics Shenzhen(NCAMS)Southern University of Science and TechnologyShenzhen 518055China

The numerical integration of phase-field equations is a delicate task which needs to recover at the discrete level intrinsic properties of the solution such as energy dissipation and maximum *** the theory of energy dissipation for classical phase field models is well established,the corresponding theory for time-fractional phase-field models is still *** this article,we study certain nonlocal-in-time energies using the first-order stabilized semi-implicit L1 *** particular,we will establish a discrete fractional energy law and a discrete weighted energy *** extension for a(2-α)-order L1 scalar auxiliary variable scheme will be ***,we demonstrate that the energy bound is preserved for the L1 schemes with nonuniform time *** numerical experiments are carried to verify our theoretical analysis.

关键词： Time-fractional phased-field equation Allen-Cahn equations Cahn-Hilliard equations Caputo fractional derivative Energy dissipation

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Research of photon counting imaging detector based on delay line anode 10

Research of photon counting imaging detector based on delay ...

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10th Symposium on Novel Optoelectronic Detection Technology and Applications

作者： Liu, Yong'an Zhang, Yalong Yang, Xianghui Zhang, Ruili Liu, Zhe Sheng, Lizhi State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781510688155

ISBN: (纸本)9781510688148

Photon counting imaging detector based on microchannel plate (MCP) and position-sensitive anode readout has the ability to detect single photon, and has been widely used in space detection, high-energy physics and bioluminescence imaging. Traditionally, the detector consists of photocathode, electron multiplication part and position decoding part, in which position decoding is accomplished by position sensitive anode. The delay line anode not only has high spatial resolution and large detection area, but also has high count rate. Based on the principle of delay line decoding, an induction readout cross delay line anode (XDL) is designed and fabricated. And the performance of the detector is tested by using the experimental system. The results show that the width of XDL anode output pulse (FWHM) is about 4ns, the spatial resolution of the detector is better than 100μm, and the dark count rate is 2s). © 2025 SPIE.

关键词： Photocathodes

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Exploring Heterogeneity and Uncertainty for Graph-based Cognitive Diagnosis Models in Intelligent Education 25

Exploring Heterogeneity and Uncertainty for Graph-based Cogn...

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Proceedings of the 31st ACM SIGKDD Conference on knowledge Discovery and data Mining V.1

作者： Pengyang Shao Yonghui Yang Chen Gao Lei Chen Kun Zhang Chenyi Zhuang Le Wu Yong Li Meng Wang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei Anhui China BNRist Tsinghua University Beijing China Department of Electronic Engineering BNRist Tsinghua University Beijing China Ant Group HangZhou Zhejiang China

ISBN: (纸本)9798400712456

Graph-based Cognitive Diagnosis (CD) has attracted much research interest due to its strong ability on inferring students' proficiency levels on knowledge concepts. While graph-based CD models have demonstrated remarkable performance, we contend that they still cannot achieve optimal performance due to the neglect of edge heterogeneity and uncertainty. Edges involve both correct and incorrect response logs, indicating heterogeneity. Meanwhile, a response log can have uncertain semantic meanings, e.g., a correct log can indicate true mastery or fortunate guessing, and a wrong log can indicate a lack of understanding or a careless mistake. In this paper, we propose an Informative Semantic-aware Graph-based Cognitive Diagnosis model (ISG-CD), which focuses on how to utilize the heterogeneous graph in CD and minimize effects of uncertain edges. Specifically, to explore heterogeneity, we propose a semantic-aware graph neural networks based CD model. To minimize effects of edge uncertainty, we propose an Informative Edge Differentiation layer from an information bottleneck perspective, which suggests keeping a minimal yet sufficient reliable graph for CD in an unsupervised way. We formulate this process as maximizing mutual information between the reliable graph and response logs, while minimizing mutual information between the reliable graph and the original graph. After that, we prove that mutual information maximization can be theoretically converted to the classic binary cross entropy loss function, while minimizing mutual information can be realized by the Hilbert-Schmidt Independence ***, we adopt an alternating training strategy for optimizing learnable parameters of both the semantic-aware graph neural networks based CD model and the edge differentiation layer. Extensive experiments on three real-world datasets have demonstrated the effectiveness of ISG-CD.

关键词： cognitive diagnosis

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Efficient and Verifiable Multi-server Framework for Secure Information Classification and Storage 20th

Efficient and Verifiable Multi-server Framework for Secure...

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20th International Conference on Information Security and Cryptology, Inscrypt 2024

作者： Guo, Ziqing Wang, Yaohui Jin, Xuanyu Wang, Xiuhua He, Yuanyuan Dai, Yueyue Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9789819647330

With the rapid development of digital technology, the complexity and diversity of data sources present significant security and privacy challenges. To address these issues, this paper proposes an efficient and verifiable Multi-Server Framework for Secure Information Classification and Storage (MSF-SCS). The framework distributes sensitive data across multiple servers, proposing an opening authority to manage classified information and a semi-honest sanitizer to ensure strong authentication while preserving user anonymity. We formalize the security model of our scheme and prove that it satisfies the no-read rule, no-write rule, and unforgeability. We instantiate the scheme and conduct comprehensive experimental evaluations, which demonstrate that MSF-SCS significantly outperforms traditional architectures in terms of scalability and efficiency, making it particularly suitable for industries with stringent data protection requirements. Future work will focus on further improving the system’s security while maintaining its high performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Access Control Encryption Public-key Encryption Secure Information Classification

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Location Privacy Preservation Crowdsensing With Federated Reinforcement Learning

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 1877-1894页

作者： You, Zhichao Dong, Xuewen Liu, Ximeng Gao, Sheng Wang, Yongzhi Shen, Yulong Xidian University School of Computer Science and Technology The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Xi'an710071 China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710071 China Fuzhou University College of Computer and Data Science Fuzhou350025 China Central University of Finance and Economics School of Information Beijing102206 China California State Polytechnic University Pomona Department of Computer Information Systems PomonaCA91768 United States Xidian University School of Computer Science and Technology Xi'an710071 China Shaanxi Key Laboratory of Network and System Security Xi'an710071 China

Crowdsensing has become a popular method of sensing data collection while facing the problem of protecting participants' location privacy. Existing location-privacy crowdsensing mechanisms focus on static tasks and participants without considering sensing tasks' time requirements and participants' mobility, which cannot achieve satisfactory collected data quality and task completion in crowdsensing with dynamic tasks and participants. Inspired by this, we proposed a location-preservation crowdsensing mechanism, FedSense, considering dynamic tasks and participants based on federated learning (FL) and reinforcement learning (RL). In FedSense, through RL's outstanding decision-making ability, participants select sensing tasks to perform by well-trained RL models without uploading location information to servers for task allocation. We propose an independent tasks selection environment that defines actions, states, and rewards of RL to enable FedSense to achieve satisfactory task completion and data quality while preserving location privacy. Besides, FedSense applies an asynchronous FL aggregation algorithm that reduces participants' network stabilization and device computing ability requirements. Analysis proves that participants' location information does not leave the local device during the model training and task selection process, effectively avoiding privacy leakage. Simulation shows that compared with existing location-preservation crowdsensing mechanisms, FedSense achieves the highest task completion and sensing accuracy for dynamic tasks and participants. © 2024 IEEE.

关键词： data privacy

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