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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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Residual diverse ensemble for long-tailed multi-label text classification

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Science China(information Sciences) 2024年第11期67卷 92-105页

作者： Jiangxin SHI Tong WEI Yufeng LI National Key Laboratory for Novel Software Technology Nanjing University School of Artificial Intelligence Nanjing University School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of Education

Long-tailed multi-label text classification aims to identify a subset of relevant labels from a large candidate label set, where the training datasets usually follow long-tailed label distributions. Many of the previous studies have treated head and tail labels equally, resulting in unsatisfactory performance for identifying tail labels. To address this issue, this paper proposes a novel learning method that combines arbitrary models with two steps. The first step is the “diverse ensemble” that encourages diverse predictions among multiple shallow classifiers, particularly on tail labels, and can improve the generalization of tail *** second is the “error correction” that takes advantage of accurate predictions on head labels by the base model and approximates its residual errors for tail labels. Thus, it enables the “diverse ensemble” to focus on optimizing the tail label performance. This overall procedure is called residual diverse ensemble(RDE). RDE is implemented via a single-hidden-layer perceptron and can be used for scaling up to hundreds of thousands of labels. We empirically show that RDE consistently improves many existing models with considerable performance gains on benchmark datasets, especially with respect to the propensity-scored evaluation ***, RDE converges in less than 30 training epochs without increasing the computational overhead.

关键词： multi-label learning extreme multi-label learning long-tailed distribution multi-label text classification ensemble learning

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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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Blockchain and signcryption enabled asynchronous federated learning framework in fog computing

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Digital Communications and networks 2025年第2期11卷 442-454页

作者： Zhou Zhou Youliang Tian Jinbo Xiong Changgen Peng Jing Li Nan Yang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology

Federated learning combines with fog computing to transform data sharing into model sharing, which solves the issues of data isolation and privacy disclosure in fog computing. However, existing studies focus on centralized single-layer aggregation federated learning architecture, which lack the consideration of cross-domain and asynchronous robustness of federated learning, and rarely integrate verification mechanisms from the perspective of incentives. To address the above challenges, we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning(BSAFL) framework based on dual aggregation for cross-domain scenarios. In particular, we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains. Second, we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models' availability of intra-domain user. Furthermore, we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain. Finally, security analysis demonstrates the security and privacy effectiveness of BSAFL, and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.

关键词： Blockchain Signcryption Federated learning Asynchronous Fog computing

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YOLO-CORE: Contour Regression for Efficient Instance Segmentation

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Machine Intelligence Research 2023年第5期20卷 716-728页

作者： Haoliang Liu Wei Xiong Yu Zhang School of Computer Science and Engineering and the Key Laboratory of Computer Network and Information Integration(Ministry of Education) Southeast UniversityNanjing 211189China

Instance segmentation has drawn mounting attention due to its significant ***,high computational costs have been widely acknowledged in this domain,as the instance mask is generally achieved by pixel-level *** this paper,we present a conceptually efficient contour regression network based on the you only look once(YOLO)architecture named YOLO-CORE for instance *** mask of the instance is efficiently acquired by explicit and direct contour regression using our designed multiorder constraint consisting of a polar distance loss and a sector *** proposed YOLO-CORE yields impressive segmentation performance in terms of both accuracy and *** achieves 57.9%AP@0.5 with 47 FPS(frames per second)on the semantic boundaries dataset(SBD)and 51.1%AP@0.5 with 46 FPS on the COCO *** superior performance achieved by our method with explicit contour regression suggests a new technique line in the YOLO-based image understanding ***,our instance segmentation design can be flexibly integrated into existing deep detectors with negligible computation cost(65.86 BFLOPs(billion float operations per second)to 66.15 BFLOPs with the YOLOv3 detector).

关键词： computer vision instance segmentation object shape prediction contour regression polar distance.

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A new quantum key distribution resource allocation and routing optimization scheme

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Chinese Physics B 2024年第3期33卷 244-259页

作者：毕琳袁晓同吴炜杰林升熙 School of Computer Science and Technology Changchun University of Science and TechnologyChangchun 130012China Key Laboratory of Network and Information Security in Jilin Province Changchun 130012China

Quantum key distribution(QKD)is a technology that can resist the threat of quantum computers to existing conventional cryptographic ***,due to the stringent requirements of the quantum key generation environment,the generated quantum keys are considered valuable,and the slow key generation rate conflicts with the high-speed data transmission in traditional optical *** this paper,for the QKD network with a trusted relay,which is mainly based on point-to-point quantum keys and has complex changes in network resources,we aim to allocate resources reasonably for data packet ***,we formulate a linear programming constraint model for the key resource allocation(KRA)problem based on the time-slot ***,we propose a new scheduling scheme based on the graded key security requirements(GKSR)and a new micro-log key storage algorithm for effective storage and management of key ***,we propose a key resource consumption(KRC)routing optimization algorithm to properly allocate time slots,routes,and key *** results show that the proposed scheme significantly improves the key distribution success rate and key resource utilization rate,among others.

关键词： quantum key distribution(QKD) resource allocation key storage routing algorithm

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TRBFT: An Efficient Blockchain Consensus for Edge Computing-Enabled IoT Systems

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IEEE Internet of Things Journal 2025年第11期12卷 15853-15868页

作者： Zheng, Jiali Zhang, Yuxi Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information Guangxi Nanning530004 China

With the continuous expansion of the Internet of Things (IoT), and despite reductions in communication latency enabled by edge computing, the decentralized nature of node structures poses significant security challenges. Blockchain is considered a critical technology for enhancing IoT security. However, traditional consensus exhibits limitations in efficiency and security when facing the exponential growth of IoT nodes necessitating further optimization and innovation. This paper proposes the threshold signature and reputation-enhanced Byzantine fault tolerance (TRBFT) algorithm. First, we integrate IoT with edge computing architectures, conduct a comprehensive evaluation of node reputation based on behavioral quality and contribution to consensus, and periodically update and categorize nodes into different sets with varying permissions. Secondly, the algorithm ensures that the selection of primary nodes strikes a balance between reliability and randomness, while employing a segmented screening scheme that progressively restricts the participating nodes in consensus, aiming to enhance accuracy. Subsequently, the communication structure is improved by incorporating threshold signature with reputation weight, which reduces communication complexity. Ultimately, simulation results demonstrate that TRBFT reduces the average consensus delay by 71.1% and increases the average throughput by 3.95 times compared to the advanced consensus HotStuff. In scenarios with faulty nodes, TRBFT increases the chain growth rate by 65.12% and decreases the block interval by 54.26% relative to HotStuff. The findings show the superior scalability and fault tolerance of TRBFT, making it better suited for large-scale IoT scenarios integrated with edge computing. © 2025 IEEE.

关键词： Blockchain

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Weighted Attribute Based Conditional Proxy Re-Encryption in the Cloud

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computers, Materials & Continua 2025年第4期83卷 1399-1414页

作者： Xixi Yan Jing Zhang Pengyu Cheng Henan Key Laboratory of Network Cryptography Technology Network Information Security and Cryptography LaboratorySchool of SoftwareHenan Polytechnic UniversityJiaozuo454003China School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China

Conditional proxy re-encryption(CPRE)is an effective cryptographic primitive language that enhances the access control mechanism and makes the delegation of decryption permissions more granular,but most of the attribute-based conditional proxy re-encryption(AB-CPRE)schemes proposed so far do not take into account the importance of user attributes.A weighted attribute-based conditional proxy re-encryption(WAB-CPRE)scheme is thus designed to provide more precise decryption rights *** introducing the concept of weight attributes,the quantity of system attributes managed by the server is reduced *** the same time,a weighted tree structure is constructed to simplify the expression of access structure *** conditional proxy re-encryption,large amounts of data and complex computations are outsourced to cloud servers,so the data owner(DO)can revoke the user’s decryption rights directly with minimal *** scheme proposed achieves security against chosen plaintext attacks(CPA).Experimental simulation results demonstrated that the decryption time is within 6–9 ms,and it has a significant reduction in communication and computation cost on the user side with better functionality compared to other related schemes,which enables users to access cloud data on devices with limited resources.

关键词： Cloud service conditional proxy re-encryption user revocation weighted attribute

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A cost-efficient quantum access network with qubit-based synchronization

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Science China(Physics,Mechanics & Astronomy) 2024年第4期67卷 12-22页

作者： Chunfeng Huang Ye Chen Tingting Luo Wenjie He Xin Liu Zhenrong Zhang Kejin Wei Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and InformationGuangxi UniversityNanning 530004China

Quantum key distribution(QKD)is a physical layer encryption technique that enables two distant parties to exchange secure keys with information-theoretic *** the last two decades,QKD has transitioned from laboratory research to real-world applications,including multi-user quantum access networks(QANs).This network structure allows users to share a single-photon detector at a network node through time-division multiplexing,thereby significantly reducing the network ***,current QAN implementations require additional hardware for auxiliary tasks such as time *** address this issue,we propose a cost-efficient QAN that uses qubit-based *** this approach,the transmitted qubits facilitate time synchronization,eliminating the need for additional synchronization *** tested our scheme by implementing a network for two users and successfully achieved average secure key rates of 53.84 kbps and 71.90 kbps for each user over a 50-km commercial fiber *** addition,we investigated the capacity of the access network under cross-talk and loss *** simulation results demonstrate that this scheme can support a QAN with 64 users with key rates up to 1070 *** work provides a feasible and cost-effective way to implement a multi-user QKD network,further promoting the widespread application of QKD.

关键词： quantum key distribution quantum access network qubit-based synchronization

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Adaptive VDI Session Placement via User Logoff Prediction

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Machine Intelligence Research 2025年第1期22卷 189-200页

作者： Wenping Fan Puhui Meng Yu Tian Min-Ling Zhang Yao Zhang School of Computer Science and Engineering Southeast UniversityNanjing210096China VMware Information Technology(China)Limited Beijing100190China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of EducationNanjing210096China

After the global pandemic,DaaS(desktop as a service)has become the first choice of many companies’remote working *** the desktops are usually deployed in the public cloud when using DaaS,customers are more cost-sensitive which boosts the requirement of proactive power *** researches in this area focus on virtual desktop infrastructure(VDI)session logon behavior modeling,but for the remote desktop service host(RDSH)-shared desktop pools,logoff optimization is also *** systems place sessions by round-robin or in a pre-defined order without considering their logoff ***,these approaches usually suffer from the situation that few left sessions prevent RDSH servers from being powered-off which introduces cost *** this paper,we propose session placement via adaptive user logoff prediction(SODA),an innovative compound model towards proactive RDSH session ***,an ensemble machine learning model that can predict session logoff time is combined with a statistical session placement bucket model to place RDSH sessions with similar logoff time in a more centralized manner on RDSH ***,the infrastructure cost-saving can be improved by reducing the resource waste introduced by those RDSH hosts with very few hanging sessions left for a long *** on real RDSH pool data demonstrate the effectiveness of the proposed proactive session placement approach against existing static placement techniques.

关键词： Virtual desktop infrastructure(VDI)resource management remote desktop service logoff prediction adaptive modeling session placement

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