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arXiv 2025年

作者： Li, Zhenglai Wang, Jun Tang, Chang Zhu, Xinzhong Zhang, Wei Liu, Xinwang Faculty of data science City University of Macau 999078 China School of computer National University of Defense Technology Changsha410073 China School of computer China University of Geosciences Wuhan430074 China College of Mathematics Physics and Information Engineering Zhejiang Normal University Jinhua China Research Institute of Ningbo Cixing Co. Ltd Ningbo China Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250000 China

Multi-view clustering (MvC) aims to integrate information from different views to enhance the capability of the model in capturing the underlying data structures. The widely used joint training paradigm in MvC is potentially not fully leverage the multi-view information, since the imbalanced and under-optimized view-specific features caused by the uniform learning objective for all views. For instance, particular views with more discriminative information could dominate the learning process in the joint training paradigm, leading to other views being under-optimized. To alleviate this issue, we first analyze the imbalanced phenomenon in the joint-training paradigm of multi-view clustering from the perspective of gradient descent for each view-specific feature extractor. Then, we propose a novel balanced multi-view clustering (BMvC) method, which introduces a view-specific contrastive regularization (VCR) to modulate the optimization of each view. Concretely, VCR preserves the sample similarities captured from the joint features and view-specific ones into the clustering distributions corresponding to view-specific features to enhance the learning process of view-specific feature extractors. Additionally, a theoretical analysis is provided to illustrate that VCR adaptively modulates the magnitudes of gradients for updating the parameters of view-specific feature extractors to achieve a balanced multi-view learning procedure. In such a manner, BMvC achieves a better trade-off between the exploitation of view-specific patterns and the exploration of view-invariance patterns to fully learn the multi-view information for the clustering task. Finally, a set of experiments are conducted to verify the superiority of the proposed method compared with state-of-the-art approaches on eight benchmark MvC datasets. The demo code of this work is publicly available at https://***/guanyuezhen/BMvC. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Partition-A-Medical-Image: Extracting Multiple Representative Sub-regions for Few-shot Medical Image Segmentation

arXiv

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arXiv 2023年

作者： Zhu, Yazhou Wang, Shidong Xin, Tong Zhang, Zheng Zhang, Haofeng The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The School of Engineering Newcastle University Newcastle upon TyneNE1 7RU United Kingdom The School of Computing Newcastle University Newcastle upon TyneNE1 7RU United Kingdom The Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China

Few-shot Medical Image Segmentation (FSMIS) is a more promising solution for medical image segmentation tasks where high-quality annotations are naturally scarce. However, current mainstream methods primarily focus on extracting holistic representations from support images with large intra-class variations in appearance and background, and encounter difficulties in adapting to query images. In this work, we present an approach to extract multiple representative sub-regions from a given support medical image, enabling fine-grained selection over the generated image regions. Specifically, the foreground of the support image is decomposed into distinct regions, which are subsequently used to derive region-level representations via a designed Regional Prototypical Learning (RPL) module. We then introduce a novel Prototypical Representation Debiasing (PRD) module based on a two-way elimination mechanism which suppresses the disturbance of regional representations by a self-support, Multi-direction Self-debiasing (MS) block, and a support-query, Interactive Debiasing (ID) block. Finally, an Assembled Prediction (AP) module is devised to balance and integrate predictions of multiple prototypical representations learned using stacked PRD modules. Results obtained through extensive experiments on three publicly accessible medical imaging datasets demonstrate consistent improvements over the leading FSMIS methods. The source code is available at https://***/YazhouZhu19/PAMI. © 2023, CC BY.

关键词： Image segmentation

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Blockchain-assisted Puncturable Signcryption for Cloud and Fog-Hosted Industrial Cyber-Physical Systems

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IEEE Network 2025年

作者： Ma, Qingru Shen, Jian Pandi, Vijayakumar Gupta, Brij B. Arya, Varsha Mahalinghum, Maheswari Zhejiang Sci-Tech University School of Computer Science and Technology School of Artificial Intelligence Hangzhou310018 China Zhejiang Sci-Tech University School of Information Science and Engineering School of Cyber Science and Technology Zhejiang Key Laboratory of Digital Fashion and Data Governance Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Hangzhou310018 China Department of Computer Science and Engineering Tamil Nadu Tiruchirappalli620009 India Asia University Department of Computer Science and Information Engineering Taichung413 Taiwan China Medical University Hospital China Medical University Department of Medical Research Taichung Taiwan Pune412115 India School of Cybersecurity Korea University Seoul Korea Republic of Kyung Hee University Seoul02447 Korea Republic of Lebanese American University Beirut1102 Lebanon Center for Interdisciplinary Research Dehradun India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

The widespread adoption of emerging technologies, such as interconnected sensors and advanced automation systems, has led to rapid advancements in smart industrial environments. While industrial cyber-physical systems (ICPSs) bring efficiency and innovation to industrial processes, the security risks are also of great concern. The existing work provides many methods for data security and authentication for ICPSs. However, since the authentication status of ICPSs is changeable, the up-to-date methods can hardly meet the requirements for rapid authentication and secure data transmission. What’s more, largescale message revocation incurs significant communication and computational costs. This paper proposes a blockchain-assisted identity-based puncturable signcryption (BC-IBPSC) scheme for cloud and fog-hosted ICPSs. First, based on the concepts of puncturable encryption and signature, a novel identity-based puncturable signcryption method is presented to achieve efficient verifiable data encryption. Second, the blockchain is utilized to record all valid and punctured messages, to achieve evidence storage and traceability. The results of the analysis indicate that the scheme ensures security and operates efficiently. In addition, experiments show that the size of the signcrypted files does not affect the value of Gas consumption on the blockchain when uploading proofs. © 2025 IEEE. All rights reserved.

关键词： Authentication

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PROM: A persistent routing optimization method based on supervised learning

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Journal of Network and Computer Applications 2025年 242卷

作者： Yingya Guo Zebo Huang Mingjie Ding Bin Lin Huan Luo College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China

Traditional Traffic engineering (TE) typically improves network performance by formulating and solving routing optimization problems based on measured or predicted Traffic Matrices (TMs). However, since network traffic is dynamic, routing strategies optimized for the current measured TMs may not be suitable for future traffic patterns. To adapt to dynamic traffic, one possible solution is to perform more frequent routing updates, but this may lead to service disruptions. Another approach is the two-step method of prediction followed by optimization, which could lead to degraded performance of routing strategies due to inaccuracies in traffic prediction. Additionally, existing approaches focus solely on either distributed or centralized networks, lacking flexibility to adapt to different network architectures. To address these challenges, we propose PROM, a Machine Learning (ML)-based routing optimization model that directly predicts an effective routing strategy in an end-to-end manner based on historical traffic. This routing strategy is applicable across multiple future TMs, thereby reducing the need for frequent routing updates. Furthermore, PROM can be easily extended from a centralized implementation to a distributed one, adapting to different network architectures. To enhance model performance, we have also designed a custom loss function for routing optimization scenarios to avoid overfitting. Extensive simulation results demonstrate that PROM can predict high-quality routing strategies in future unknown traffic scenarios and is adaptable to both centralized and distributed network architectures.

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Decentralized Linear MMSE Equalizer Under Colored Noise for Massive MIMO Systems

Decentralized Linear MMSE Equalizer Under Colored Noise for ...

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Xiaotong Zhao Xin Guan Mian Li Qingjiang Shi School of Software Engineering Tongji University Shanghai China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9781728181059

Conventional uplink equalization in massive MIMO systems relies on a centralized baseband processing architecture. However, as the number of base station antenna increases, centralized baseband processing architectures encounter two bot-tlenecks, i.e., the tremendous data interconnection and the high-dimensional computation. To tackle these obstacles, decentralized baseband processing was proposed for uplink equalization, but only applicable to the scenarios with unpractical white Gaussian noise assumption. This paper presents an uplink linear mini-mum mean-square error (L-MMSE) equalization method in the daisy chain decentralized baseband processing architecture under colored noise assumption. The optimized L-MMSE equalizer is derived by exploiting the block coordinate descent method, which shows near-optimal performance both in theoretical and simulation while significantly mitigating the bottlenecks.

关键词： Baseband Equalizers Computer architecture Massive MIMO Mean square error methods Interference Large-scale systems

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Zero-Shot Cross-Domain Code Search without Fine-Tuning

arXiv

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arXiv 2025年

作者： Liang, keyu Liu, Zhongxin Liu, Chao Wan, Zhiyuan Lo, David Yang, Xiaohu The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Big Data and Software Engineering Chongqing University Chongqing China School of Computing and Information Systems Singapore Management University Singapore Singapore

Code search is a crucial task in software engineering, aiming to retrieve code snippets that are semantically relevant to a natural language query. Recently, Pre-trained Language Models (PLMs) have shown remarkable success and are widely adopted for code search tasks. However, PLM-based methods often struggle in cross-domain scenarios. When applied to a new domain, they typically require extensive fine-tuning with substantial data. Even worse, the data scarcity problem in new domains often forces these methods to operate in a zero-shot setting, resulting in a significant decline in performance. RAPID, which generates synthetic data for model fine-tuning, is currently the only effective method for zero-shot cross-domain code search. Despite its effectiveness, RAPID demands substantial computational resources for fine-tuning and needs to maintain specialized models for each domain, underscoring the need for a zero-shot, fine-tuning-free approach for cross-domain code search. The key to tackling zero-shot cross-domain code search lies in bridging the gaps among domains. In this work, we propose to break the query-code matching process of code search into two simpler tasks: query-comment matching and code-code matching. We first conduct an empirical study to investigate the effectiveness of these two matching schemas in zero-shot cross-domain code search. Our findings highlight the strong complementarity among the three matching schemas, i.e., query-code, query-comment, and code-code matching. Based on the findings, we propose CodeBridge, a zero-shot, fine-tuning-free approach for cross-domain code search. Specifically, CodeBridge first employs zero-shot prompting to guide Large Language Models (LLMs) to generate a comment for each code snippet in the codebase and produce a code for each query. Subsequently, it encodes queries, code snippets, comments, and the generated code using PLMs and assesses similarities through three matching schemas: query-code, query-comment,

关键词： Search engines

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Pushing AI to wireless network edge: An overview on integrated sensing, communication, and computation towards 6G

arXiv

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arXiv 2022年

作者： Zhu, Guangxu Lyu, Zhonghao Jiao, Xiang Liu, Peixi Chen, Mingzhe Xu, Jie Cui, Shuguang Zhang, Ping Shenzhen Research Institute of Big Data Shenzhen518172 China Shenzhen518172 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables33146 United States State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Peng Cheng Laboratory Shenzhen518066 China

Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the huge amount of data scattered at wireless network edge. In general, realizing edge intelligence corresponds to the process of sensing, communication, and computation, which are coupled ingredients for data generation, exchanging, and processing, respectively. However, conventional wireless networks design the sensing, communication, and computation separately in a task-agnostic manner, which encounters difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications such as auto-driving. This thus prompts a new design paradigm of seamless integrated sensing, communication, and computation (ISCC) in a task-oriented manner, which comprehensively accounts for the use of the data in the downstream AI applications. In view of its growing interest, this article provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art development, and shedding light on the road ahead. Copyright © 2022, The Authors. All rights reserved.

关键词： Artificial intelligence

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning

arXiv

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arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Graph-Based Terminal Ranking for Sparsification of Interference Coordination Parameters in Ultra-Dense Networks 10th

Graph-Based Terminal Ranking for Sparsification of Interfere...

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10th EAI International Conference on Mobile Networks and Management, MONAMI 2020

作者： Gou, Junxiang Wang, Lusheng Lin, Hai Peng, Min Ministry of Education School of Computer Science and Information Engineering Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei230601 China Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan University Wuhan430072 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei230601 China

ISBN: (纸本)9783030640019

In the future mobile communication system, inter-cell interference becomes a serious problem due to the intensive deployment of cells and terminals. Traditional interference coordination schemes take long time for optimization in ultra-dense networks. Meanwhile, due to the increase of factors affecting communication and in order to better meet the communication needs of each terminal, an interference coordination scheme needs to fully consider multiple characteristic parameters of the terminal, which will further increase the scheme’s computational time. Therefore, we should compress all the data through sparsification of parameters before optimization. There are many terminal parameters, and the essence of sparsification of parameters is to rank terminals. In this paper, a graph-based terminal ranking scheme is designed. First, each terminal can be represented by its multiple parameters. Then, all terminals are used as the vertexes in the graph to form a complete weighted graph, and edge weights represent the degree of dissimilarity between terminals. A ranking of terminals is obtained by finding a minimum Hamiltonian path in the graph. Finally, the ranking of all parameter sequences is obtained according to terminals ranking, which makes the sparsity of all parameter sequences better. Simulation results show that the proposed scheme can accomplish sparsification of parameter sequences effectively, especially when the number of sequences increases. In addition, compared with the optimal coordination of traditional scheme, this scheme improves the fairness of the system while ensuring high system capacity, and dramatically reduces the computational time of interference coordination. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2020.

关键词： Approximation algorithms

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Contextual-Semantic-Aware Linkable Knowledge Prediction in Stack Overflow via Self-Attention

Contextual-Semantic-Aware Linkable Knowledge Prediction in S...

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International Symposium on Software Reliability engineering (ISSRE)

作者： Zhaolin Luo Ling Xu Zhou Xu Meng Yan Yan Lei Can Li Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9781665425889

In Stack Overflow, a question and its answers are defined as a knowledge unit. These knowledge units can be linked together for different purposes, which typically subdivided into four classes: Duplicate, Directly linkable, Indirectly linkable, and Isolated. Developers usually use these linkable knowledge units to search for more targeted information. Prior studies have found that deep learning or SVM technique can effectively predict the class of linkable knowledge units. However, they focus on short-distance semantic relationship but fail to capture global information (semantic relationship between a word and all the words in the same knowledge unit) and ignore joint semantics (semantic relationship between a word with all the words in different knowledge units). To address the issues, we propose a Self-Attention-based contextual semantic aware Linkable Knowledge prediction model (SALKU). SALKU leverages self-attention to pay attention to all the words in a knowledge unit and fully capture the global information needed for each word, then utilizes a variant of self-attention to extract joint semantics between two knowledge units. Experiment results on an existing dataset show that SALKU out-performs the state-of-the-art approaches CNN, Tuning SVM, and Soft-cos SVM in terms of three metrics, respectively. Additionally, SALKU is faster than the three baseline approaches.

关键词： Support vector machines Measurement Knowledge engineering Deep learning Semantics Predictive models Software reliability

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