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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wang, Yu Du, Bo Wu, Shufan Yang, Xingli School of Modern Educational Technology Shanxi University Taiyuan030006 China School of Mathematics and Statistics Shanxi University Taiyuan030006 China Key Laboratory of Complex Systems and Data Science Shanxi University Ministry of Education Taiyuan030006 China

ISBN: (纸本)9783031783944

In neural network models, hyperparameters have a significant impact on model performance. Currently, the commonly used hyperparameter optimization methods include manual search, grid search, random search, Bayesian optimization, and so on. However, these methods always exhibit some problems such as high computational cost, low convergence rate and poor model performance. Thus, for image classification tasks, a hyperparameter optimization method based on statistical orthogonal design for neural network models is proposed in this paper. With the same number of experiments, the classification accuracy of the proposed method is significantly better than that of grid search, random search, and Bayesian optimization methods for both two-level and three-level orthogonal designs. With the same classification accuracy as grid search, random search, and Bayesian optimization methods, the proposed method has fewer experimental times. Furthermore, the single-factor rotation method and statistical variance analysis technique are also applied to study the effect of different hyperparameters on the performances of the neural network models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neural network models

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Joint Double Auction-Based Channel Selection in Wireless Monitoring Networks

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IEEE Transactions on Network and Service Management 2025年

作者： Xia, Na Chen, Lei Li, Meng Yin, Yutao Zhang, Ke Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data China State Grid Electric Power Research Institute Co. Ltd Hefei230088 China

In wireless networks, utilizing sniffers for fault analysis, traffic traceback, and resource optimization is a crucial task. However, existing centralized algorithms cannot be applied to high-density wireless networks. Therefore, distributed optimization of channel selection to maximize the monitoring rate of sensors in Wireless Monitoring Networks (WMNs) is a challenge. This paper proposes a joint double auction-based distributed channel selection algorithm (J2A-CS) to maximize overall quality of monitoring (QoM). First, sniffers are redundantly deployed in WMNs, and an initial channel allocation strategy is formulated. Subsequently, sniffers collectively act as buyers and sellers at different stages. Finally, buyers bid asynchronously, and sellers settle synchronously to maximize the seller's marginal revenue and update the channel selection scheme. As a distributed channel selection algorithm, J2A-CS addresses the highest overall QoM issue in WMNs, demonstrating high scalability and fault tolerance. Simulation results show that J2A-CS significantly improves QoM compared to existing distributed algorithms and outperforms centralized algorithms in high-density scenarios. © 2004-2012 IEEE.

关键词： Sales

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A knowledge-Enhanced Contrastive Learning Network for Weakly-Supervised Aspect Detection

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 506-517页

作者： Zhuoming Zheng Yi Cai Liuwu Li School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education South China University of Technology Guangzhou China

Aspect detection, aiming at identifying the aspects of review segments, is a fundamental task in opinion mining and aspect-based sentiment analysis. Due to the high cost and time consuming of human-annotation for massive reviews, several unsupervised and weakly-supervised methods are proposed recently. However, existing weakly-supervised models are mostly seed-driven methods based on co-occurrence of words, which suffer from lacking the ability of detecting the aspects with infrequent aspect terms and identifying Misc aspect. To tackle these problems, we leverage external knowledge to enhance the representation of aspects and segments by a weakly-supervised method. In this paper, we propose an aspect knowledge-enhanced contrastive learning (AKECL) network with two powerful knowledge-enhanced encoders for aspects and reivew segments to enhance weakly-supervised aspect detection task. Experiments in seven different domains show that AKECL outperforms the competitive baselines, and demonstrate the effectiveness of our proposed method, as well as the improvement by introducing external knowledge.

关键词： Reviews Contrastive learning Vectors Feature extraction knowledge graphs Speech processing Sentiment analysis knowledge engineering Hardware Touch sensitive screens

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Short-Term Synchronous and Asynchronous Ambient Noise Tomography in Urban Areas: Application to Karst Investigation

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engineering 2025年第5期48卷 292-308页

作者： Liu, Ya Xia, Jianghai Guan, Bo Xi, Chaoqiang Ning, Ling Zhang, Hao Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province School of Earth Sciences Zhejiang University Hangzhou310027 China State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines School of Earth and Environment Anhui University of Science and Technology Huainan232001 China Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of Education Jiangxi Normal University Nanchang330022 China Greater Bay Area Branch of Aerospace Information Research Institute Chinese Academy of Sciences Guangzhou510700 China

Dense-array ambient noise tomography is a powerful tool for achieving high-resolution subsurface imaging, significantly impacting geohazard prevention and control. Conventional dense-array studies, however, require simultaneous observations of numerous stations for extensive coverage. To conduct a comprehensive karst feature investigation with limited stations, we designed a new synchronous–asynchronous observation system that facilitates dense array observations. We conducted two rounds of asynchronous observations, each lasting approximately 24 h, in combination with synchronous backbone stations. We achieved wide-ranging coverage of the study area utilizing 197 nodal receivers, with an average station spacing of 7 m. The beamforming results revealed distinct variations in the noise source distributions between day and night. We estimated the source strength in the stationary phase zone and used a weighting scheme for stacking the cross-correlation functions (C1 functions) to suppress the influence of nonuniform noise source distributions. The weights were derived from the similarity coefficients between multicomponent C1 functions related to Rayleigh waves. We employed the cross-correlation of C1 functions (C2 methods) to obtain the empirical Green's functions between asynchronous stations. To eliminate artifacts in C2 functions from higher-mode surface waves in C1 functions, we filtered the C1 functions on the basis of different particle motions linked to multimode Rayleigh waves. The dispersion measurements of Rayleigh waves obtained from both the C1 and C2 functions were utilized in surface wave tomography. The inverted three-dimensional (3D) shear-wave (S-wave) velocity model reveals two significant low-velocity zones at depths ranging from 40 to 60 m, which align well with the karst caves found in the drilling data. The method of short-term synchronous–asynchronous ambient noise tomography shows promise as a cost-effective and efficient approach for urban ge

关键词： Shear waves

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CoGAP: A Personalized Federated Learning Method Using Collaborative Optimization for Medical Image Classification

CoGAP: A Personalized Federated Learning Method Using Collab...

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

作者： Zheng, Shenhai Li, Congyu Wen, Sian Gao, Xi Yu, Lei College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China Emergency Department The Second Affiliated Hospital of Chongqing Medical University Chongqing China

ISBN: (纸本)9798350368741

Federated learning (FL) has been widely used in medical image processing to protect data privacy, but it has issues with data heterogeneity. Personalized federated learning have emerged to tackle these issues but often focuses too much on personalized models at the expense of global models. To address these problem, we propose a personalized federated learning method CoGAP, using collaborative optimization to enhance both personalized and global model performance. On the client side, it employs adaptive weight aggregation to initialize personalized models and uses a training strategy based on L2 regularization. On the server side, it implements a gradient accumulation-based aggregation method. These modules facilitate a collaborative optimization process where the global model guides the personalized models, and the personalized models provide positive feedback to the global model, ultimately leading to mutual improvement for both. We conducted extensive comparative experiments on the OCT2017 dataset, as well as the BloodMnist and PathMnist (subsets from MedMnist), to evaluate both global and personalized models. Results show that CoGAP own a unique strength which not only outperforms other personalized federated learning methods in personalization capability while also remarkably achieving satisfying global generalization. Code is available at https://***/lcyCQUPT/CoGAP. © 2025 IEEE.

关键词： collaborative optimization federated learning medical image classification

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Learning Diversified Primitive Prompts for Compositional Zero-Shot Learning

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Chen, Ziyi Zhao, Xinru Lang, Congyan Wei, Lili Wang, Tao Li, Yidong Beijing Jiaotong University Key Laboratory of Big Data and Artificial Intelligence in Transportation Ministry of Education Beijing100044 China

Compositional Zero-Shot Learning (CZSL) aims to combine known attributes and objects as primitives for recognizing previously unseen attribute-object pairs. The essence of CZSL lies in separately modeling attributes and objects for transfer to unseen compositions. Since primitives exhibit weak entanglement in language features, Vision-Language Models (VLMs) based CZSL methods focus on prompt learning to assist primitive disentanglement in visual spaces. However, these approaches overlook the polysemy of primitives across different compositional contexts, leading the models to overly rely on seen compositions, thereby limiting their generalization capability. To tackle this issue, we propose a novel primitive disambiguation model named DPR, aiming to enhance CZSL performance by acquiring diversified primitive representations through joint-prompt learning. Specifically, DPR introduces knowledge-Aware Hard Prompting (KAHP), which generates diverse descriptive sentences as supplementary prompts for each composition from the rich knowledge of Large Language Models (LLMs). Simultaneously, we design Sample-Adaptive Soft Prompting (SASP), which employs a lightweight neural network to generate input-guided tokens for each image. SASP ensures enhanced generalization capability in DPR’s primitive representations. We validate the effectiveness of our model through extensive experiments and achieve state-of-the-art performance on three CZSL benchmark datasets in both closed-and open-world settings. © 1991-2012 IEEE.

关键词： Zero-shot learning

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Less is More: Token Context-aware Learning for Object Tracking

arXiv

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arXiv 2025年

作者： Xu, Chenlong Zhong, Bineng Liang, Qihua Zheng, Yaozong Li, Guorong Song, Shuxiang Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

Recently, several studies have shown that utilizing contextual information to perceive target states is crucial for object tracking. They typically capture context by incorporating multiple video frames. However, these naive frame-context methods fail to consider the importance of each patch within a reference frame, making them susceptible to noise and redundant tokens, which deteriorates tracking performance. To address this challenge, we propose a new token context-aware tracking pipeline named LMTrack, designed to automatically learn high-quality reference tokens for efficient visual tracking. Embracing the principle of Less is More, the core idea of LMTrack is to analyze the importance distribution of all reference tokens, where important tokens are collected, continually attended to, and updated. Specifically, a novel Token Context Memory module is designed to dynamically collect high-quality spatio-temporal information of a target in an autoregressive manner, eliminating redundant background tokens from the reference frames. Furthermore, an effective Unidirectional Token Attention mechanism is designed to establish dependencies between reference tokens and search frame, enabling robust cross-frame association and target localization. Extensive experiments demonstrate the superiority of our tracker, achieving state-of-the-art results on tracking benchmarks such as GOT-10K, TrackingNet, and LaSOT. Code and models are available at https://***/XuChenLong/LMTrack. Copyright © 2025, The Authors. All rights reserved.

关键词： Object detection

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Federated Incremental Named Entity Recognition 31

Federated Incremental Named Entity Recognition

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31st International Conference on Computational Linguistics, COLING 2025

作者： Liu, Zesheng Zhu, Qiannan Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China School of Artificial Intelligence Beijing Normal University Beijing China Engineering Research Center of Intelligent Technology and Educational Application MOE China

ISBN: (纸本)9798891761964

Federated learning-based Named Entity Recognition (FNER) has attracted widespread attention through decentralized training on local clients. However, most FNER models assume that entity types are pre-fixed, so in practical applications, local clients constantly receive new entity types without enough storage to access old entity types, resulting in severe forgetting on previously learned knowledge. In addition, new clients collecting only new entity types may join the global training of FNER irregularly, further exacerbating catastrophic forgetting. To overcome the above challenges, we propose a Forgetting-Subdued Learning (FSL) model which solves the forgetting problem on old entity types from both intra-client and inter-client two aspects. Specifically, for intra-client aspect, we propose a prototype-guided adaptive pseudo labeling and a prototypical relation distillation loss to surmount catastrophic forgetting of old entity types with semantic shift. Furthermore, for inter-client aspect, we propose a task transfer detector. It can identify the arrival of new entity types that are protected by privacy and store the latest old global model for relation distillation. Qualitative experiments have shown that our model has made significant improvements compared to several baseline methods. © 2025 Association for Computational Linguistics.

关键词： Federated learning

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Five-user quantum virtual local area network with an AlGaAs entangled photon source

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Science china(Physics,Mechanics & Astronomy) 2025年第3期68卷 61-73页

作者： Cheng Qian Hongkai Tian Xu Jing Yichen Liu Zhe Chen Huiran Luo Yuan Du Xiaodong Zheng Tangsheng Chen Yuechan Kong Hua-Lei Yin Dong Jiang Bin Niu Liangliang Lu Key Laboratory of State Manipulation and Advanced Materials in Provincial Universities School of Physical Science and TechnologyNanjing Normal UniversityNanjing210023China School of Internet Anhui UniversityHefei230039China National Key Laboratory of Solid-State Microwave Devices and Circuits Nanjing Chip Valley Industrial Technology InstituteNanjing Electronic Devices InstituteNanjing210016China Research Center for Quantum Optics and Quantum Communication School of ScienceQingdao University of TechnologyQingdao266520China Department of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials and Micro-nano Devices Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)Renmin University of ChinaBeijing100872China Beijing Academy of Quantum Information Sciences Beijing100193China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei230601China National Laboratory of Solid-State Microstructures and School of Physics Nanjing UniversityNanjing210093China Hefei National Laboratory Hefei230088China

The promotion of quantum network applications demands the scalable connection of quantum *** is preferable to set up multiple logical networks coexisting on a single physical network infrastructure to accommodate a larger number of *** we present a quantum virtual network architecture that offers this level of scalability,without being constrained to a fixed physical-layer network relying solely on passive multiplexing *** architecture can be understood as arising from the superposition of a fully connected entanglement distribution network and port-based virtual local area network,which group multiusers by access *** terms of hardware,we leverage a semiconductor chip with a high figure-of-merit modal overlap to directly generate high-quality polarization entanglement,and a streamlined polarization analysis module,which requires only one single-photon detector for each end *** experimentally perform the BBM92 QKD protocol on the five-user quantum virtual network and demonstrate voice and image encryption on a campus area *** results may provide insights into the realization of large-scale quantum networks with integrated and cost-efficient photonic architecture.

关键词： quantum network quantum entanglement distribution integrated photonics image and voice encryption

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NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on Adaptive Perturbation on Federated Graph Learning 34

NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on ...

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34th ACM Web Conference, WWW 2025

作者： Li, Ken Shi, Bin Wei, Jiazhe Dong, Bo School of Computer Science and Technology Xi’an Jiaotong University Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an China School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an China School of Distance Education Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an China

ISBN: (纸本)9798400712746

Federated Graph Learning (FedGL) is an emerging Federated Learning (FL) framework that learns the graph data from various clients to train better Graph Neural Networks(GNNs) model. Owing to concerns regarding the security of such framework, numerous studies have attempted to execute backdoor attacks on FedGL, with a particular focus on distributed backdoor attacks. However, all existing methods posting distributed backdoor attack on FedGL only focus on injecting distributed backdoor triggers into the training data of each malicious client, which will cause model performance degradation on original task and is not always effective when confronted with robust federated learning defense algorithms, leading to low success rate of attack. What’s more, the backdoor signals introduced by the malicious clients may be smoothed out by other clean signals from the honest clients, which potentially undermining the performance of the attack. To address the above significant shortcomings, we propose a non-intrusive graph distributed backdoor attack(NI-GDBA) that does not require backdoor triggers to be injected in the training data. Our attack trains an adaptive perturbation trigger generator model for each malicious client to learn the natural backdoor from the GNN model downloading from the server with the malicious client’s local data. In contrast to traditional distributed backdoor attacks on FedGL via trigger injection in training data, our attack on different datasets such as Molecules and Bioinformatics have higher attack success rate, stronger persistence and stealth, and has no negative impact on the performance of the global GNN model. We also explore the robustness of NI-GDBA under different defense strategies, and based on our extensive experimental studies, we show that our attack method is robust to current federated learning defense methods, thus it is necessary to consider non-intrusive distributed backdoor attacks on FedGL as a novel threat that requires custom d

关键词： Backdoor Attacks Federated Graph Learning

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