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Partial multi-label learning via label-specific feature corrections

Science China(Information Sciences) 2025年第3期68卷 95-109页

作者： Jun-Yi HANG Min-Ling ZHANG School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of Education

Partial multi-label learning（PML） allows learning from rich-semantic objects with inaccurate annotations, where a set of candidate labels are assigned to each training example but only some of them are valid. Existing approaches rely on disambiguation to tackle the PML problem, which aims to correct noisy candidate labels by recovering the ground-truth labeling information ahead of prediction model induction. However, this dominant strategy might be suboptimal as it usually needs extra assumptions that cannot be fully satisfied in real-world scenarios. Instead of label correction, we investigate another strategy to tackle the PML problem, where the potential ambiguity in PML data is eliminated by correcting instance features in a label-specific manner. Accordingly, a simple yet effective approach named PASE, i.e., partial multi-label learning via label-specific feature corrections, is proposed. Under a meta-learning framework, PASElearns to exert label-specific feature corrections so that potential ambiguity specific to each class label can be eliminated and the desired prediction model can be induced on these corrected instance features with the provided candidate labels. Comprehensive experiments on a wide range of synthetic and real-world data sets validate the effectiveness of the proposed approach.

关键词： machine learning multi-label learning partial multi-label learning label-specific features feature correction

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Physical Layer Security Scheme With AoI-Awareness for Industrial IoT Based on Covert Communications

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 276-278页

作者： Yaping Li Zhi-Xin Liu Jia-Wei Su Ya-Zhou Yuan the Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University

Dear Editor,Industrial Internet of things(IIoT) is a typical application of cyberphysical system(CPS). In the IIoT, wireless communication is an inevitable trend to replace the deployment-limited wired transmission for cases with large-scale and mobile devices. However, wireless communication gives rise to critical issues related to physical security, such as malicious detections and attacks [1].

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Attribute grouping-based naive Bayesian classifier

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Science China(Information Sciences) 2025年第3期68卷 125-149页

作者： Yulin HE Guiliang OU Philippe FOURNIER-VIGER Joshua Zhexue HUANG Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) College of Computer Science & Software Engineering Shenzhen University

The naive Bayesian classifier(NBC) is a supervised machine learning algorithm having a simple model structure and good theoretical interpretability. However, the generalization performance of NBC is limited to a large extent by the assumption of attribute independence. To address this issue, this paper proposes a novel attribute grouping-based NBC(AG-NBC), which is a variant of the classical NBC trained with different attribute groups. AG-NBC first applies a novel effective objective function to automatically identify optimal dependent attribute groups(DAGs). Condition attributes in the same DAG are strongly dependent on the class attribute, whereas attributes in different DAGs are independent of one another. Then,for each DAG, a random vector functional link network with a SoftMax layer is trained to output posterior probabilities in the form of joint probability density estimation. The NBC is trained using the grouping attributes that correspond to the original condition attributes. Extensive experiments were conducted to validate the rationality, feasibility, and effectiveness of AG-NBC. Our findings showed that the attribute groups chosen for NBC can accurately represent attribute dependencies and reduce overlaps between different posterior probability densities. In addition, the comparative results with NBC, flexible NBC(FNBC), tree augmented Bayes network(TAN), gain ratio-based attribute weighted naive Bayes(GRAWNB), averaged one-dependence estimators(AODE), weighted AODE(WAODE), independent component analysis-based NBC(ICA-NBC), hidden naive Bayesian(HNB) classifier, and correlation-based feature weighting filter for naive Bayes(CFW) show that AG-NBC obtains statistically better testing accuracies, higher area under the receiver operating characteristic curves(AUCs), and fewer probability mean square errors(PMSEs) than other Bayesian classifiers. The experimental results demonstrate that AG-NBC is a valid and efficient approach for alleviating the attribute i

关键词： naive Bayesian classifier attribute independence assumption attribute grouping dependent attribute group posterior probability class-conditional probability

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Secure signal and image transmissions using chaotic synchronization scheme under cyber-attack in the communication channel

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Soft Computing 2025年第1期29卷 339-353页

作者： Nobakht, Shaghayegh Ahmadi, Ali-Akbar Industrial Automation Laboratory Electrical and Computer Engineering Department Kharazmi University Tehran Iran

In this paper, problem of secure message (signal and image) transmission is studied. The message is encrypted by masking it with a chaotic system state and then transmitted to receiver-side via a communication channel. It is assumed that the communication channel may be prone to a cyber-attack. The transmission system dynamics in presence of the cyber-attack in the communication channel is modeled by a singular dynamical system. Then, at the receiver-side, a novel observer is designed to recover the message together with the cyber-attack signal. Estimation of the cyber-attack is crucial for monitoring the communication channel and enable a network supervisor to mitigate the adverse consequences and make an appropriate strategy. It is assumed neither restrictions on the message nor on the cyber-attack signals. This makes the proposed scheme, the best solution in the real applications. Moreover, comparing to the existing methods which two public and private channels are required, only one communication channel is used in this paper for transmission the encrypted signal which results in a cheaper solution. Solving the optimization problem can be easily performed with the help of the existing toolboxes such as YALMIP and CVX software in MATLAB. Finally, numerical simulations for signal and image transmission are presented to justify the applicability and superior performance of the proposed scheme. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Cyber attacks

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Secure beamforming and deployment design for rate-splitting multiple access-based UAV communications

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Science China(Information Sciences) 2025年第1期68卷 342-356页

作者： Bing WANG Xiaofeng TAO Shujun HAN Huici WU Kai YANG Zhu HAN National Engineering Research Center of Mobile Network Technologies Beijing University of Posts and Telecommunications Peng Cheng Laboratory Department of Electrical and Computer Engineering University of Houston

Rate-splitting multiple access(RSMA) has recently gained attention as a potential robust multiple access(MA)scheme for upcoming wireless networks. Given its ability to efficiently utilize wireless resources and design interference management strategies, it can be applied to unmanned aerial vehicle(UAV) networks to provide convenient services for large-scale access ground users. However, due to the line-of-sight(LoS) broadcast nature of UAV transmission, information is susceptible to eavesdropping in RSMA-based UAV networks. Moreover, the superposition of signals at the receiver in such networks becomes complicated. To cope with the challenge, we propose a two-user multi-input single-output(MISO) RSMA-based UAV secure transmission framework in downlink communication networks. In a passive eavesdropping scenario, our goal is to maximize the sum secrecy rate by optimizing the transmit beamforming and deployment location of the UAV-base station(UAV-BS),while considering quality-of-service(QoS) constraints, maximum transmit power, and flight space limitations. To address the non-convexity of the proposed problem, the optimization problem is first decoupled into two subproblems. Then, the successive convex approximation(SCA) method is employed to solve each subproblem using different propositions. In addition, an alternating optimization(AO)-based location RSMA(L-RSMA) beamforming algorithm is developed to implement joint optimization to obtain the suboptimal solution. Numerical results demonstrate that(1) the proposed L-RSMA scheme yields a28.97% higher sum secrecy rate than the baseline L-space division multiple access(SDMA) scheme;(2) the proposed L-RSMA scheme improves the security performance by 42.61% compared to the L-non-orthogonal multiple access(NOMA) scheme.

关键词： rate-splitting multiple access UAV communication precoding optimization deployment location physical layer security

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Cross-subcarrier precoder design for massive MIMO-OFDM downlink with symplectic optimization

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Science China(Information Sciences) 2025年第2期68卷 216-229页

作者： Yuxuan ZHANG An-An LU Xiang-Gen XIA Xiqi GAO National Mobile Communications Research Laboratory Southeast University Purple Mountain Laboratories Department of Electrical and Computer Engineering University of Delaware

We propose a cross-subcarrier precoder design(CSPD) for massive multiple-input multiple-output(MIMO) orthogonal frequency division multiplexing(OFDM) downlink. This work aims to significantly improve the channel estimation and signal detection performance by enhancing the smoothness of the frequency domain effective channel. This is accomplished by designing a few vectors known as the transform domain precoding vectors(TDPVs), which are then transformed into the frequency domain to generate the precoders for a set of subcarriers. To combat the effect of channel aging, the TDPVs are optimized under imperfect channel state information(CSI). The optimal precoder structure is derived by maximizing an upper bound of the ergodic weighted sum-rate(WSR) and utilizing the a posteriori beam-based statistical channel model(BSCM). To avoid the large-dimensional matrix inversion, we propose an algorithm with symplectic optimization. Simulation results indicate that the proposed cross-subcarrier precoder design significantly outperforms conventional methods.

关键词： massive MIMO-OFDM cross subcarrier precoder design transform domain precoding vectors effective channel symplectic optimization

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Distributionally Robust Chance-Constrained Optimization for Soft Open Points Operation in Active Distribution Networks

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CSEE Journal of Power and Energy Systems 2025年第2期11卷 637-648页

作者： Qilin Hou Ge Chen Ningyi Dai Hongcai Zhang Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart CityUniversity of MacaoMacao 999078China

The escalating installation of distributed generation (DG) within active distribution networks (ADNs) diminishes the reliance on fossil fuels, yet it intensifies the disparity between demand and generation across various regions. Moreover, due to the intermittent and stochastic characteristics, DG also introduces uncertain forecasting errors, which further increase difficulties for power dispatch. To overcome these challenges, an emerging flexible interconnection device, soft open point (SOP), is introduced. A distributionally robust chance-constrained optimization (DRCCO) model is also proposed to effectively exploit the benefits of SOPs in ADNs under uncertainties. Compared with conventional robust, stochastic and chance-constrained models, the DRCCO model can better balance reliability and economic profits without the exact distribution of uncertainties. More-over, unlike most published works that employ two individual chance constraints to approximate the upper and lower bound constraints (e.g, bus voltage and branch current limitations), joint two-sided chance constraints are introduced and exactly reformulated into conic forms to avoid redundant conservativeness. Based on numerical experiments, we validate that SOPs' employment can significantly enhance the energy efficiency of ADNs by alleviating DG curtailment and load shedding problems. Simulation results also confirm that the proposed joint two-sided DRCCO method can achieve good balance between economic efficiency and reliability while reducing the conservativeness of conventional DRCCO methods.

关键词： Active distribution networks distributionally robust chance-constrained optimization soft open points

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A survey on cross-user federated recommendation

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Science China(Information Sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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Foundation models for topic modeling: a case study

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Frontiers of computer Science 2025年第2期19卷 129-131页

作者： Han ZENG Jia-Ming SUN Chun-Shu LI Zhuying LI Tong WEI School of Computer Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration Southeast UniversityNanjing 210096China

1 Introduction In Natural Language Processing(NLP),topic modeling is a class of methods used to analyze and explore textual corpora,i.e.,to discover the underlying topic structures from text and assign text pieces to different *** NLP,a topic means a set of relevant words appearing together in a particular pattern,representing some specific *** is beneficial for tracking social media trends,constructing knowledge graphs,and analyzing writing *** modeling has always been an area of extensive research in *** methods like Latent Semantic Analysis(LSA)and Latent Dirichlet Allocation(LDA),based on the“bag of words”(BoW)model,often fail to grasp the semantic nuances of the text,making them less effective in contexts involving polysemy or data noise,especially when the amount of data is small.

关键词： words semantic textual

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Secure speech coding using chaotic shift keying for encryption combined with error recovery

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Multimedia Tools and Applications 2025年第7期84卷 3893-3927页

作者： Boumaraf, Messaouda Merazka, Fatiha LISIC Laboratory Telecommunications Department Electronic and Computer Engineering Faculty USTHB University Algiers Algeria LISIC Laboratory Telecommunications Department Electrical Engineering Faculty USTHB University Algiers Algeria

This paper presents a speech encryption algorithm utilizing chaotic shift keying alongside error recovery techniques for the G.722.2 codec. The algorithm employs chaotic data interleaving on speech inter-frames to replace continuous packet loss with isolated losses, thus restoring missing data packets. Codec parameters undergo encryption through the combination of multiple chaotic maps applied to intra sub-frames of speech, facilitating confusion and diffusion processes. Enhanced security is achieved through the utilization of multiple chaotic maps, including chaotic shift keying generators applied to codec sub-frame parameters. Specifically, the chaotic shift keying mechanism is employed using four chaotic maps: a Henon map for xn and yn, a logistic map for zn, a tent map for wn, and a cubic map for un. The keys derived from these maps are utilized for chaotic data interleaving on both inter-frames and intra sub-frames of the speech file to shuffle frame parameters (confusion) and scramble sub-frame parameters (diffusion). Additionally, the proposed system enhances speech quality by mitigating degradation caused by packet loss through a waveform substitution-based recovery method, which replaces lost packets with previously received copies. A series of experiments, including waveform representation, spectrogram analysis, Enhanced Modified Bark Spectral Distortion (EMBSD) test, WideBand Perceptual Evaluation of Speech Quality (WB-PESQ) test, MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) score, Mean Opinion Score (MOS) score, correlation test, entropy test, Number of Samples Change Rate (NSCR) test, Unified Average Modified Intensity (UACI) test, key space analysis, and sensitivity test, are conducted to evaluate the security and speech quality of the proposed system. The results validate the system’s security and confirm its ability to maintain good audio quality even under packet loss rates ranging from 5% to 20%. © The Author(s), under exclusive licen

关键词： Cryptography

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