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

作者： Yifan Zeng Peijia Zheng Jian Li School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Centralized training of deep learning-based network intrusion detection systems (DLNIDS) raises privacy concerns and incurs huge overhead. Federated learning (FL), while preserving privacy, confronts challenges including size imbalance, heterogeneous and global imbalance. To address these issues, this paper proposes a Self-Balancing Federated learning Network Intrusion Detection system (SBFedNIDS) for accurate and efficient network intrusion detection while preserving privacy and tackling negative impact caused by multiple imbalances resulting from FL. SBFedNΠ)S can efficiently mitigate model bias cause by multiple imbalances and addresses imbalances at data-level via federated cost sensitive learning (FCSL) and client synthetic minority over-sampling technique (CSMOTE), without requiring additional training resources, unlike generative models. Extensive experiments on a benchmark dataset show that SBFedNIDS outperforms baselines in multiple imbalanced scenarios and even surpasses centralized training in several settings.

关键词： Training Privacy Data privacy Accuracy Costs Federated learning Network intrusion detection Signal processing Benchmark testing Speech processing

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Identification of Non-Restorative Sleep Associated with Depression Using Ambulatory Electrocardiogram and Triaxial Acceleration

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IEEE Transactions on Biomedical Engineering 2025年 PP卷 PP页

作者： Liu, Li Wang, Chongyang Liu, Guangyuan Wen, Wanhui Southwest University Chongqing Key Laboratory of Generic Technology and System of Service Robots Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Sichuan University Key Laboratory of Rehabilitation Medicine in Sichuan Province Department of Rehabilitation Medicine Institute of Rehabilitation Medicine West China Hospital Chengdu610041 China

Objective: Non-restorative sleep is prevalent among individuals with depression and is strongly associated with the severity of the condition. Therefore, identifying non-restorative sleep can aid in the early screening of depression. Investigating non-restorative sleep in depression necessitates long-term monitoring under naturalistic conditions. Methods: In this study, we recruited 149 participants and collected electrocardiogram and triaxial acceleration from them, resulting in a total of 761 nights of data. The period from midnight to 6:30 AM was segmented into 78 five-minute intervals, from which 40 physiological features were extracted for each interval. To deal with variations in sleep patterns across participants and dates, we reordered the sleep data based on levels of parasympathetic nervous system (PNS) activation to explore the underlying neural mechanisms of non-restorative sleep in individuals with depressive symptoms. Results: We developed a model that integrated convolutional neural networks with an attention mechanism to identify nonrestorative sleep in individuals with depressive symptoms. The model demonstrated impressive performance on an independent test set, achieving an accuracy of 81.25% and an F1 score of 77.85%. Additionally, Bayes' theorem was used to compute the posterior probability indicating nonrestorative sleep in this population, assessing abnormal PNS activation. Conclusion: Finally, we designed a system capable of automatically evaluating nighttime sleep status and quantifying changes in non-restorative sleep associated with depression. Significance: This system offers a novel tool and method for the early identification of individuals at risk of depression. © 1964-2012 IEEE.

关键词： Sleep research

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Deep reinforcement learning for optimal microgrid energy management with renewable energy and electric vehicle integration

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Applied Soft computing 2025年 176卷

作者： Xiong, Baoyin Zhang, Lili Hu, Yang Fang, Fang Liu, Qingzhi Cheng, Long State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University China School of Computing Dublin City University Ireland Information Technology Group Wageningen University and Research Netherlands

The development and utilization of renewable energy sources (RES) are gaining unprecedented attention as a response to the environmental, economic, and energy security challenges posed by non-renewable fossil fuels. Nonetheless, integrating renewable energy into large-scale power grids remains a complex endeavour, which constrains the widespread adoption of these sustainable energy sources. Microgrid, which can function both autonomously and in coordination with the larger grid, provides an effective solution for integrating RES into the broader power system. To coordinate and optimize various energy resources within the microgrids to meet demand while maintaining stability and efficiency, the deployment of an Energy Management system (EMS) is crucial. This paper proposes a deep reinforcement learning (DRL)-based real-time optimal energy management method to assist the EMS for microgrids in making optimal scheduling decisions. Electric vehicles are introduced as a new controllable power source into the MG, alongside uncontrollable photovoltaic and wind power sources, resulting in an enhancement of the self-balance capability of the entire system. The efficacy of our proposed methodology is validated via a case study. Specifically, in comparison to the traditional energy scheduling approach, our method is found to enhance the self-balancing rate by a maximum of 22.97% and augment the operator's profit by as much as 33.74%. These results unequivocally demonstrate the superiority and practical value of our methodology in the relevant domain. © 2025

关键词： Wind power integration

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Rapid classification of proteins suspended in an evaporated sessile drop

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Talanta 2025年 294卷 128224页

作者： Liu, Maoxi Jiao, Feng He, Yongqing School of Chemical Engineering Kunming University of Science and Technology Kunming650500 China Chongqing Key Laboratory of Micro-Nano System and Intelligent Transduction National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing400067 China

Reliable and accessible technologies for biological sample analysis are crucial for rapid, accurate illness diagnosis in resource-limited settings. Conventional methods like mass spectrometry and enzyme-linked immunosorbent assay (ELISA) often face challenges of technological complexity and equipment immobility, leading to delayed diagnoses and elevated costs. This study introduces evaporative deposition patterns from sessile droplets as a simple, cost-effective approach for classifying proteins in protein-salt mixtures, leveraging quantitative texture analysis to achieve high diagnostic accuracy. Using mixtures of potassium chloride (KCl) or sodium chloride (NaCl) with Bovine Serum Albumin (BSA) or Ovalbumin (OVA) as model systems, we varied salt (0.25–2 wt%) and protein (0.5–1.5 wt%) concentrations, observing distinct deposition morphologies such as coffee rings, cruciform/dendritic crystals at high salt levels, and volcano-shaped /spoke-like patterns at low or high protein levels. A novel quantitative framework integrating radial density distributions, gray-level co-occurrence matrix (GLCM) texture features, and receiver operating characteristic (ROC) curves achieved a 96.74 % classification accuracy for NaCl-protein mixtures, surpassing traditional texture analysis in simplicity and efficacy. These findings demonstrate the potential of evaporative deposition analysis as a simple tool for distinguishing molecular mixtures, laying the groundwork for future diagnostic applications. Unlike prior qualitative studies, this work provides a robust, quantitative method for protein classification, advancing the understanding of protein-salt interactions. © 2025 Elsevier B.V.

关键词： Body fluids

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Unveiling the secrets without data: can graph neural networks be exploited through data-free model extraction attacks? 24

Unveiling the secrets without data: can graph neural network...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Yuanxin Zhuang Chuan Shi Mengmei Zhang Jinghui Chen Lingjuan Lyu Pan Zhou Lichao Sun Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education The Pennsylvania State University SONY AI Huazhong University of Science and Technology Lehigh University

ISBN: (纸本)9781939133441

Graph neural networks (GNNs) play a crucial role in various graph applications, such as social science, biology, and molecular chemistry. Despite their popularity, GNNs are still vulnerable to intellectual property threats. Previous studies have demonstrated the susceptibility of GNN models to model extraction attacks, where attackers steal the functionality of GNNs by sending queries and obtaining model responses. However, existing model extraction attacks often assume that the attacker has access to specific information about the victim model's training data, including node attributes, connections, and the shadow dataset. This assumption is impractical in real-world scenarios. To address this issue, we propose STEAL-GNN, the first data-free model extraction attack framework against GNNs. STEALGNN advances prior GNN extraction attacks in three key aspects: 1) It is completely data-free, as it does not require actual node features or graph structures to extract GNN models. 2) It constitutes a full-rank attack that can be applied to node classification and link prediction tasks, posing significant intellectual property threats across a wide range of graph applications. 3) It can handle the most challenging hard-label attack setting, where the attacker possesses no knowledge about the target GNN model and can only obtain predicted labels through querying the victim model. Our experimental results on four benchmark graph datasets demonstrate the effectiveness of STEALGNN in attacking representative GNN models.

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Impacts of terrain resolution on an extreme snowstorm process over the Southern Tibetan Plateau: Spatial distribution and cloud microphysical processes

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Atmospheric Research 2025年 322卷

作者： An, Pengchao Li, Ying Ye, Wei Fan, Xiaoting State Key Laboratory of Severe Weather Meteorological Science and Technology Chinese Academy of Meteorological Sciences Beijing China Department of Atmospheric and Oceanic Sciences Institute of Atmospheric Sciences Fudan University Shanghai China Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Tsinghua University Beijing China Sichuan Provincial Meteorological Service Centre Chengdu China

Precipitation is an essential component of the water cycle over the Tibetan Plateau (TP) in cold seasons. Investigating the impacts of various terrain resolutions in simulations on the precipitation distribution and rain-snow phase transition on the TP helps to understand the sources of simulation error and contributes to assessment and improvement. In this study, three types of terrain resolutions were used to conduct simulation experiments (namely WRF30s, WRF2m and WRF5m) on a large-scale extreme precipitation process over the TP under the influence of a storm over the Bay of Bengal in October 2008 near the Himalayas and the Yarlung Zangbo Grand Canyon, which serve as representative regions influenced by northward moisture. The results indicate that the terrain resolutions affect the precipitation amount and phase distribution. On the north of the Himalayas, a snowfall-dominated "phase bias" based on "wet bias" is observed above 3 km, which strengthens as the terrain resolution reduces, relating to the stronger water vapor flux transport and larger cold bias resulting from the terrain smoothing. The Bomi station, which is located in the canyon, undergoing a snow-to-rain phase transition influenced by the storm received further attention. The study not only shows the cloud microphysical characteristics of the precipitation on the TP under the influence of storms, but further reveals the snow melting phase transition weakening, manifested as the increase of snowfall accompanied by the decrease of terrain resolution. Further detailed analysis of cloud microphysical process in Bomi shows that the latter is related to the decrease melting rates of snow and graupel caused by the compression of the melting layer when the terrain near canyon increases with terrain resolution decrease. Consequently, the distribution and phase transition of precipitation on the TP induced by terrain resolutions requires attention during the simulation. © 2025 Elsevier B.V.

关键词： Cloud microphysical processes Extreme snowstorm Phase bias Terrain resolution The Tibetan Plateau Wet bias

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Source-free Domain Adaptation with Multiple Alignment for Efficient Image Retrieval

Source-free Domain Adaptation with Multiple Alignment for Ef...

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

作者： Jinghui Ni Hui Cui Lihai Zhao Fengling Li Xiaohui Han Lijuan Xu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Quan Cheng Laboratory Shandong Open University University of Technology Sydney

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Domain adaptation techniques help models generalize to target domains by addressing domain discrepancies between the source and target domain data distributions. These techniques are particularly valuable for cross-domain hashing retrieval, as they reduce training costs while maintaining high retrieval efficiency. However, existing unsupervised domain adaptative hashing methods often require access to both source and target domain data, which may raise privacy concerns regarding source domain data. To address these concerns, restricting access to source domain data is crucial, but this restriction also makes the alignment process between domains more challenging. In this paper, we propose a Source-free Domain Adaptive Hashing with Multiple Alignment (SFDAH-MA) approach for image retrieval. SFDAH-MA integrates model structure alignment, class moment alignment, and semantic relationship alignment to maximize inter-domain knowledge transfer. This comprehensive alignment strategy not only enhances retrieval performance across domains but also minimizes reliance on source domain data, thereby supporting data privacy protection. Extensive experiments show that SFDAH-MA achieves state-of-the-art performance in source-free settings and achieves comparable results to existing unsupervised domain adaptive hashing methods under conventional settings. The source codes of our method are available at: https://***/Nikphyc/SFDAH-MA.

关键词： Training Data privacy Adaptation models Source coding Semantics Image retrieval Signal processing Protection Speech processing Knowledge transfer

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Adaptive ELM-Based Time-Constrained Control of Uncertain Robotic Manipulators With Minimum Learning Computation

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IEEE Transactions on Emerging Topics in computing 2025年

作者： Jin, Xiaozheng Hou, Yuhan Wu, Xiaoming Chi, Jing 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 Shandong Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Jinan250013 China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Shandong Jinan250200 China Shandong University of Finance and Economics School of Computing and Artificial Intelligence Shandong Jinan250014 China

This paper studies the extreme learning machine (ELM)-based time-constrained trajectory tracking control of a class of robotic manipulator systems with model/input uncertainties, matched and mismatched disturbances. A uniform robust exact differentiator (URED) is developed to construct a multi-layer virtual control framework of the robotic manipulator system. Under the control framework, adaptive ELM-based control strategies are proposed to suppress the influence of the uncertainties and disturbances layer-by-layer, combining with the minimum learning parameter (MLP) technique which is employed to reduce the calculation complexity of ELM parameters. The significance of this study is that general model/input uncertainties, matched and mismatched disturbances can be effectively compensated within a limited time, so that bounded trajectory tracking of the robotic manipulator can be achieved avoiding the complexity explosion issue. The time-constrained stability of the closed-loop robotic error systems is proved through Lyapunov stability theory. Finally, comparative simulations are employed to display the feasibility and superiority of the designed robust adaptive ELM-based control schemes of a robotic manipulator system. © 2013 IEEE.

关键词： Robust control

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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High-Fidelity Editable Portrait Synthesis with 3D GAN Inversion

High-Fidelity Editable Portrait Synthesis with 3D GAN Invers...

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

作者： Jindong Xie Jiachen Liu Yupei Lin Jinbao Wang Xianxu Hou Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of Information Engineering Guangdong University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The 3D generative adversarial network (GAN) inversion converts an image into 3D representation to attain high-fidelity reconstruction and facilitate realistic image manipulation within the 3D latent space. However, previous approaches face challenges regarding the trade-off between the reconstruction ability and editability. That is, reversing a real-world image to a low-dimensional latent code would inevitably lead to information loss, and achieving a near-perfect reconstruction using high-rate triplane representation often limits the ability to manipulate the image freely in the latent space. To address these issues, we propose a novel latent conditioning encoder-based framework with the alignment between the low-dimensional latent and high-dimensional triplane. A non-semantic guided editing strategy bridges the intrinsic relation between the latent condition and triplane generation, making it possible to edit the high-dimensional representation by latent manipulation. As a result, our method can achieve high-fidelity reconstruction and editing simultaneously by directly controlling the latent code. Experimental results demonstrate that our approach excels in reconstruction and editing quality compared to previous 3D inversion methods. Furthermore, our method can also edit even real faces with large poses and out-of-domain cases.

关键词： Bridges Three-dimensional displays Codes Signal processing Generative adversarial networks Vectors Acoustics Speech processing Image reconstruction Faces

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