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

作者： Dong, Jinwei Wang, Xinsheng Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350368741

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method's superior performance over existing diffusion and discriminative models, validating its efficacy. © 2025 IEEE.

关键词： diffusion score matching speech separation stochastic differential equation

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Adversarial Contrastive Graph Augmentation with Counterfactual Regularization 39

Adversarial Contrastive Graph Augmentation with Counterfactu...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Long, Tao Zhang, Lei Zhang, Liang Cui, Laizhong College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China Shenzhen Research Institute of Big Data Guangdong Shenzhen China

ISBN: (纸本)157735897X

With the advancement of graph representation learning, self-supervised graph contrastive learning (GCL) has emerged as a key technique in the field. In GCL, positive and negative samples are generated through data augmentation. While recent works have introduced model-based methods to enhance positive graph augmentations, they often overlook the importance of negative samples, relying instead on rule-based methods that can fail to capture meaningful graph patterns. To address this issue, we propose a novel model-based adversarial contrastive graph augmentation (ACGA) method that automatically generates both positive graph samples with minimal sufficient information and hard negative graph samples. Additionally, we provide a theoretical framework to analyze the process of positive and negative graph augmentation in self-supervised GCL. We evaluate our ACGA method through extensive experiments on representative benchmark datasets, and the results demonstrate that ACGA outperforms state-of-the-art baselines. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Joint Participation Incentive and Network Pricing Design for Federated Learning 42

Joint Participation Incentive and Network Pricing Design for...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Ding, Ningning Gao, Lin Huang, Jianwei Northwestern University Department of Electrical and Computer Engineering EvanstonIL60208 United States Shenzhen Research Institute of Big Data Shenzhen518172 China Harbin Institute of Technology Sch. of Electronics and Info. Eng. and the Guangdong Prov. Key Lab. of Aerosp. Commun. and Networking Technol. Shenzhen China The Chinese University of Hong Kong Shenzhen School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen518172 China

ISBN: (纸本)9798350334142

Federated learning protects users' data privacy though sharing users' local model parameters (instead of raw data) with a server. However, when massive users train a large machine learning model through federated learning, the dynamically varying and often heavy communication overhead can put significant pressure on the network operator. The operator may choose to dynamically change the network prices in response, which will eventually affect the payoffs of the server and users. This paper considers the under-explored yet important issue of the joint design of participation incentives (for encouraging users' contribution to federated learning) and network pricing (for managing network resources). Due to heterogeneous users' private information and multi-dimensional decisions, the optimization problems in Stage I of multi-stage games are non-convex. Nevertheless, we are able to analytically derive the corresponding optimal contract and pricing mechanism through proper transformations of constraints, variables, and functions, under both vertical and horizontal interaction structures of the participants. We show that the vertical structure is better than the horizontal one, as it avoids the interests misalignment between the server and the network operator. Numerical results based on real-world datasets show that our proposed mechanisms decrease server's cost by up to 24.87% comparing with the state-of-the-art benchmarks. © 2023 IEEE.

关键词： Costs

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Nonreciprocity of energy transfer in a nonlinear asymmetric oscillator system with various vibration states

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Applied Mathematics and Mechanics(English Edition) 2023年第5期44卷 727-744页

作者： Jian’en CHEN Jianling LI Minghui YAO Jun LIU Jianhua ZHANG Min SUN Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical EngineeringTianjin University of TechnologyTianjin 300384China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of TechnologyTianjin 300384China School of Aeronautics and Astronautics Tiangong UniversityTianjin 300387China Agricultural Information Institute of Chinese Academy of Agricultural Sciences Key Laboratory of Agricultural Big DataMinistry of Agriculture and Rural AffairsBeijing 100081China School of Science Tianjin Chengjian UniversityTianjin 300384China

The nonreciprocity of energy transfer is constructed in a nonlinear asymmetric oscillator system that comprises two nonlinear oscillators with different parameters placed between two identical linear *** slow-flow equation of the system is derived by the complexification-averaging *** semi-analytical solutions to this equation are obtained by the least squares method,which are compared with the numerical solutions obtained by the Runge-Kutta *** distribution of the average energy in the system is studied under periodic and chaotic vibration states,and the energy transfer along two opposite directions is *** effect of the excitation amplitude on the nonreciprocity of the system producing the periodic responses is analyzed,where a three-stage energy transfer phenomenon is *** the first stage,the energy transfer along the two opposite directions is approximately equal,whereas in the second stage,the asymmetric energy transfer is *** energy transfer is also asymmetric in the third stage,but the direction is reversed compared with the second ***,the excitation amplitude for exciting the bifurcation also shows an asymmetric *** vibrations are generated around the resonant frequency,irrespective of which linear oscillator is *** excitation threshold of these chaotic vibrations is dependent on the linear oscillator that is being *** addition,the difference between the energy transfer in the two opposite directions is used to further analyze the nonreciprocity in the *** results show that the nonreciprocity significantly depends on the excitation frequency and the excitation amplitude.

关键词： nonreciprocity strong nonlinearity energy transfer chaotic vibration higher branch of response

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Finding the Missing data: A BERT-Inspired Approach Against Package Loss in Wireless Sensing

Finding the Missing Data: A BERT-Inspired Approach Against P...

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IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Zijian Zhao Tingwei Chen Fanyi Meng Hang Li Xiaoyang Li Guangxu Zhu Shenzhen Research Institute of Big Data School of Computer Science and Engineering Sun Yat-sen University School of Science and Engineering The Chinese University of Hong Kong Shenzhen

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Despite the development of various deep learning methods for Wi-Fi sensing, package loss often results in noncontinuous estimation of the Channel State Information (CSI), which negatively impacts the performance of the learning models. To overcome this challenge, we propose a deep learning model based on Bidirectional Encoder Representations from Transformers (BERT) for CSI recovery, named CSI-BERT. CSI-BERT can be trained in an self-supervised manner on the target dataset without the need for additional data. Furthermore, unlike traditional interpolation methods that focus on one subcarrier at a time, CSI-BERT captures the sequential relationships across different subcarriers. Experimental results demonstrate that CSI-BERT achieves lower error rates and faster speed compared to traditional interpolation methods, even when facing with high loss rates. Moreover, by harnessing the recovered CSI obtained from CSI-BERT, other deep learning models like Residual Network and Recurrent Neural Network can achieve an average increase in accuracy of approximately 15% in Wi-Fi sensing tasks. The collected dataset WiGesture and code for our model are publicly available at https://***/RS2002/CSI-BERT.

关键词： Deep learning Wireless communication Interpolation Wireless sensor networks Transformers data models Sensors

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Visual Servoing under Limited Field of View (FOV) for Lumbar Vertebrae Positioning

Visual Servoing under Limited Field of View (FOV) for Lumbar...

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IEEE Information Technology and Mechatronics engineering Conference (ITOEC)

作者： Peng Xiao Chi Xiong Yu Wang Lingming Kong Bin Cai Zhiyong Sun Le Zou Xiaofeng Wang Bo Song School of Artificial Intelligence and Big Data Hefei University Hefei China Chinese Academy of Sciences Institute of Intelligent Machines HFIPS Hefei China University of Science and Technology of China Hefei China Chimed Intelligent Machinery Co. Ltd Hefei China Intelligent Manufacturing Multimodal Data Fusion Engineering Research Center China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

X-ray machines are vital in medical imaging for viewing internal body structures. However, in lumbar vertebrae surgery, the X-ray machine has to move and position frequently and manually, which risks infection, misalignment, and overexposure to radiation. There’s a need for mobile X-ray machines with autonomous recognition and positioning. Although visual servoing suitable for X-ray positioning naturally, it still experiencing challenges like limited Field-of-View (FOV) and the structural similarity among vertebrae. In this paper, a Bayesian Estimation based approach is developed to improve the detection accuracy in case of vertebrae positions outside the FOV. Furthermore, a visual servoing method is proposed for X-ray positioning, considering their mechanical structure and imaging characteristics. The experiment results indicates that the proposed approach improves lumbar vertebrae detection accuracy significantly. This approach facilitates precise positioning in X-ray imaging and enhances the safety and effectiveness of lumbar vertebrae surgery.

关键词： Accuracy Mechatronics Surgery Estimation Visual servoing Safety Bayes methods Information technology X-ray imaging Biomedical imaging

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Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN

Fast and Accurate Cooperative Radio Map Estimation Enabled b...

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IEEE International Conference on Communications Workshops, ICC

作者： Zezhong Zhang Guangxu Zhu Junting Chen Shuguang Cui Future Network of Intelligent Institute (FNii) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China Pengcheng Lab Shenzhen China

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In the 6G era, real-time radio resource monitoring and management are urged to support diverse wireless-empowered applications. This calls for fast and accurate estimation on the distribution of the radio resources, which is usually represented by the spatial signal power strength over the geographical environment, known as a radio map. In this paper, we present a cooperative radio map estimation (CRME) approach enabled by the generative adversarial network (GAN), called as GAN-CRME, which features fast and accurate radio map estimation without the transmitters' information. The radio map is inferred by exploiting the interaction between distributed received signal strength (RSS) measurements at mobile users and the geographical map using a deep neural network estimator, resulting in low data-acquisition cost and computational complexity. Moreover, a GAN-based learning algorithm is proposed to boost the inference capability of the deep neural network estimator by exploiting the power of generative AI. Simulation results showcase that the proposed GAN-CRME is even capable of coarse error-correction when the geographical map information is inaccurate.

关键词： 6G mobile communication Accuracy Costs Three-dimensional displays Conferences Radio transmitters Estimation

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Predicting CircRNA-Disease Associations Through Non-negative Matrix Factorization and Adversarially Regularized Variational Graph Autoencoder

Predicting CircRNA-Disease Associations Through Non-negative...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lu, Mianshuo Wang, Lei Sun, Jinzhu Li, Yang Wei, Mengmeng Wei, Yu Liu, Changchun Li, Zhengwei China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Guangxi Academy of Sciences Big Data and Intelligent Computing Research Center Nanning530007 China Zaozhuang Mental Health Center Zaozhuang277100 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Zaozhuang Univerity College of Information Science and Enginnering 277100 China

ISBN: (纸本)9798350386226

Circular RNA (circRNA) is an RNA molecule that plays an important role in both pathology and physiology. Accurate identification of associations between circRNAs and diseases is crucial for further physiology research. However, verifying the circRNA-disease associations (CDA) through biological experimental methods is time-consuming. Here, we propose a novel method combines Non-negative Matrix Factorization (NMF) and Adversarially Regularized Variational Graph Autoencoder (ARVGA) to accurately predict CDA. Our model first fuses multi-source information in order to build circRNA similarity, disease similarity and circRNA-disease association matrices. Thereby our model constructs graphs for circRNA and disease respectively and optimize them using a K-means clustering algorithm. We obtain linear features using NMF and non-linear features using ARVGA. Finally, an Extremely Randomized Trees classifier is employed to predict CDA. On the gold standard dataset CircR2Disease, our model achieved a prediction accuracy of 94.8% and an AUC of 0.984 under 5-fold cross-validation. In case study, 19 of top 20 predicted circRNAs associated with Hepatocellular Carcinoma were confirmed in relevant literature. Furthermore, ablation experiment, classifier experiment and independent datasets test fully demonstrate the effectiveness and robustness of our model. © 2024 IEEE.

关键词： Non-negative matrix factorization

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Rate Adaptation with Correlated Multi-Armed Bandits in 802.11 Systems

Rate Adaptation with Correlated Multi-Armed Bandits in 802.1...

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IEEE International Conference on Communications in China (ICCC)

作者： Yuzhou Tong Jiakun Fan Xuhong Cai Yi Chen School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data

Rate adaptation is a mechanism critical for maximizing the throughput of 802.11 systems. In this paper, inspired by the observation that the packet success rate under different data rates is not independent, we model the rate adaptation problem as a multi-armed bandit (MAB) problem with correlated arms. Two MAB-based rate adaptation algorithms are developed in which the correlation between data rates is exploited to accelerate the convergence of the algorithms. To verify the performance of our algorithms, we build up an indoor 802.11n test bed. The proposed rate adaptation algorithms are implemented and deployed on the test bed. Both simulation and test-bed experiments demonstrate the superiority of our algorithms in stationary and non-stationary radio environments.

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Detection Method of River Floating Objects based on Edge Computing 2

Detection Method of River Floating Objects based on Edge Com...

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2nd International Conference on Robotics, Automation and intelligent Control, ICRAIC 2022

作者： Li, He Jia, Huimei Zhang, Rui Yang, Shuaipeng Qi, Qinglei Liu, Tianyang Henan Costar Group Co. Ltd Nanyang China College of Information Engineering Henan University of Science and Technology Luoyang China Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang China Sydney Business School Shanghai University Shanghai China

For the detection of objects floating in the river, most of the traditional intelligent video monitoring methods are used to monitor through manual viewing. During the traditional video data transmission and processing, the massive amount of data will put pressure on the background such as network and computing power. The new effective method is to crop and optimize YOLOV5s, add a specific image pre-processing module, and deploy it by edge computing, embed a SOC (System on Chip) chip in the web camera for real-time processing of video data. Simulation experiments prove that the above method can improve the accuracy and speed of the target detection network. © Published under licence by IOP Publishing Ltd.

关键词： Edge computing

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