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41st International Conference on Machine Learning, ICML 2024

作者： Lian, Junrong Dong, Ziyue Wei, Pengxu Ke, Wei Liu, Chang Ye, Qixiang Ji, Xiangyang Lin, Liang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Software Engineering Xi'an Jiaotong University Xi'an China Department of Automation Tsinghua University Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

A codebook designed for learning discrete distributions in latent space has demonstrated state-of-the-art results on generation tasks. This inspires us to explore what distribution of codebook is better. Following the spirit of Kepler's Conjecture, we cast the codebook training as solving the sphere packing problem and derive a Kepler codebook with a compact and structured distribution to obtain a codebook for image representations. Furthermore, we implement the Kepler codebook training by simply employing this derived distribution as regularization and using the codebook partition method. We conduct extensive experiments to evaluate our trained codebook for image reconstruction and generation on natural and human face datasets, respectively, achieving significant performance improvement. Besides, our Kepler codebook has demonstrated superior performance when evaluated across datasets and even for reconstructing images with different resolutions. Codes and pre-trained weights are available at https://***/banianrong/KeplerCodebook. Copyright 2024 by the author(s)

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A Comprehensive Survey on Communication-Efficient Federated Learning in Mobile Edge Environments

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IEEE Communications Surveys and Tutorials 2025年

作者： Jia, Ninghui Qu, Zhihao Ye, Baoliu Wang, Yanyan Hu, Shihong Guo, Song Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Nanjing211100 China Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing210023 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Kowloon Hong Kong

In traditional centralized machine learning, transmitting raw data to a cloud center incurs high communication costs and raises privacy concerns. This is particularly challenging in mobile edge environments, where devices are dynamic and resource-constrained. Federated Learning (FL) addresses these issues by allowing devices to train models locally and upload parameters to a central server without sharing local data. However, limited wireless channel resources and dynamic transmission performance make communication overhead a major bottleneck of FL in mobile edge environments. Concerning this challenge, this survey provides a comprehensive summary of methods to improve communication efficiency in FL, focusing on: 1) minimizing communication complexity to reduce total transmission volume, 2) scheduling resources appropriately to improve training efficiency, 3) utilizing over-the-air computation (OTA) to integrate computation into communication for accommodating the computation/communication characteristics of FL in mobile edge environments. Thus, this work analyzes research from the perspective of convergence and data heterogeneity to reduce communication rounds by optimizing algorithm performance. We hope that this survey could offer insights into communication-efficient FL for future research. © 2025 IEEE.

关键词： Federated learning

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Difficulty-Aware Data Augmentor for Scene Text Recognition 48

Difficulty-Aware Data Augmentor for Scene Text Recognition

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Meng, Guanghao Dai, Tao Chen, Bin Li, Naiqi Jiang, Yong Xia, Shu-Tao Tsinghua University Tsinghua Shenzhen International Graduate School China Peng Cheng Laboratory Shenzhen China Shenzhen University College of Computer Science and Software Engineering China Harbin Institute of Technology Department of Computer Science and Technology Shenzhen China

ISBN: (纸本)9781728163277

Deep neural network (DNN) based scene text recognition (STR) methods usually require a large amount of annotated data for training, which is time-consuming and cost-expensive in practice. To address this issue, many data augmentation methods have been developed to train recognizers by improving the diversity of training samples. However, most existing methods neglect the difficulty inherent in samples, and easily suffer from the problem of over-diversity, i.e., the distribution of the augmented data significantly deviates from that of clean data. In this paper, we propose a novel difficulty-aware data augmentation framework for scene text recognition, which jointly considers the difficulty of samples and the strength of augmentations. Specifically, our framework first predicts the sample difficulty, followed by an adaptive data augmentation strategy. Furthermore, we build a more diverse set of augmentation methods for STR and integrate it into our augmentation framework. Extensive experiments on scene text recognition benchmarks show that our augmentation framework significantly improves the performance of recognizers. © 2023 IEEE.

关键词： data augmentation deep neural network difficulty-aware Scene text recognition

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Experiments and Analyses of Anonymization Mechanisms for Trajectory Data Publishing

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Journal of computer science & Technology 2022年第5期37卷 1026-1048页

作者： She Sun Shuai Ma Jing-He Song Wen-Hai Yue Xue-Lian Lin Tiejun Ma State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang UniversityBeijing 100191China Department of Decision Analytics and Risk Southampton Business SchoolUniversity of Southampton Southampton SO171BJU.K

With the advancing of location-detection technologies and the increasing popularity of mobile phones and other location-aware devices,trajectory data is continuously *** large-scale trajectories provide opportunities for various applications,the locations in trajectories pose a threat to individual ***,there has been an interesting debate on the reidentifiability of individuals in the science *** main finding of Sánchez et *** exactly opposite to that of De Montjoye et al.,which raises the first question:"what is the true situation of the privacy preservation for trajectories in terms of reidentification?''Furthermore,it is known that anonymization typically causes a decline of data utility,and anonymization mechanisms need to consider the trade-off between privacy and *** raises the second question:"what is the true situation of the utility of anonymized trajectories?''To answer these two questions,we conduct a systematic experimental study,using three real-life trajectory datasets,five existing anonymization mechanisms(i.e.,identifier anonymization,grid-based anonymization,dummy trajectories,k-anonymity andε-differential privacy),and two practical applications(i.e.,travel time estimation and window range queries).Our findings reveal the true situation of the privacy preservation for trajectories in terms of reidentification and the true situation of the utility of anonymized trajectories,and essentially close the debate between De Montjoye et *** Sánchez et *** the best of our knowledge,this study is among the first systematic evaluation and analysis of anonymized trajectories on the individual privacy in terms of unicity and on the utility in terms of practical applications.

关键词： anonymization privacy reidentification trajectory utility

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Weighted Consistent Multi-view Discriminant Analysis in Unreliable Labeling Environment 1

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3rd International Conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Li, Meihua Zhao, Yang Pei, Jihong Yang, Xuan College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Guangdong Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

Multi-view discriminant analysis method (MvDA) is an effective supervised multi-view learning method. However, in practical multi-view learning application scenarios, there may be the problem that the sample label information from some viewpoints is unreliable. In the MvDA method, the information of all views is considered to have equal reliability. The data information in views containing unreliable label information may make it difficult for MvDA to learn accurate common features among data from different views. Therefore, in this paper, a weighted consistent multi-view discriminant analysis (WMvDA-VC) is proposed for the multi-view learning task in unreliable labeling environment. By considering the data characteristics under different views, the information from different views is assigned different reliability weights. Using the weighted scatter matrices, multiple projection matrices are learned. Then the data from different views are projected into the same common projection space. In the common subspace, dissimilar samples in different views are dispersed as much as possible and similar samples are aggregated as much as possible. The impact of data from views containing unreliable labels on the multi-view learning performance is reduced by adjusting the weights of the views with unreliable labels. The method is experimented on four widely used datasets. The experimental results show that the proposed WMvDA-VC exhibits excellent performance in the classification recognition task with unreliably labeled environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Learning systems

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Low Resolution Face Image Recognition Based on Consistent Discriminant Correlation Analysis with Weight Correction 1

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3rd International Conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Lin, Xiaoan Li, Meihua Pei, Jihong Zhao, Yang College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Guangdong Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

Consistent Discriminant Correlation Analysis (CDCA) is an effective algorithm for dual-view learning. However, the importance of information from different samples and features may vary. In CDCA, the sample features from both views are equally weighted during training, which may not fully utilize the information contained in certain sample features. This can negatively impact the decision-making and predictive capabilities of the model. Additionally, CDCA is only applicable to dual-view data and cannot effectively utilize multi-view data. To address these issues, this paper extends CDCA to multi-view scenarios and proposes Consistent Discriminant Correlation Analysis with Weight Correction (CDCA-WC). The feature weighting approach used in this paper involves calculating the average weight of each feature using the ReliefF algorithm. The sample weighting method employed is based on a weighted loss function, where a larger weight indicates a stronger classification ability for the corresponding feature or sample. During the training process, CDCA-WC is used to obtain the feature subspace mappings for the training samples from multiple views. The data’s feature and sample dimensions are then adjusted based on their respective weights, and the adjusted data is used for a second round of training. During testing, the similarity between samples is measured using a KNN classifier. Experimental results on three publicly available face recognition datasets demonstrate that CDCA-WC can effectively improve the accuracy of low-resolution face image recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Face recognition

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Regularized Loose Coupled Deep Non-negative Basis Matrix Factorization for Low-Resolution Face Image Recognition 1

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3rd International Conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Li, Jianhua Wang, Jinxin Pei, Jihong Zhao, Yang College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Guangdong Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

Low-resolution face image recognition is a research focus in computer vision and pattern recognition. In this paper, the potential relationship between matched high-resolution face and low-resolution face images is considered to propose the regularized loose coupled deep non-negative basis matrix factorization (RLCDNBMF) architecture, which uses regularized deep non-negative basis matrix factorization for high-resolution images and couples its coefficient matrix with the coefficient matrix obtained from low-resolution non-negative factorization, so that the discriminative information of high-resolution face images can be fully utilized to guide the non-negative matrix factorization of low-resolution face images. Comparative experiments are conducted on three mainstream face databases, and the experimental results show that the method can improve the accuracy of low-resolution face recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Non-negative matrix factorization

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Coupled Non-negative Matrix Factorization with Deep Part-Based Feature Learning for Low-Resolution Image Recognition 1

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3rd International Conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Wang, Jinxin Zhao, Yang Pei, Jihong Yang, Xuan College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Guangdong Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

The existing low-resolution (LR) image recognition methods based on coupled relationship learning of high- and low-resolution images usually take similar feature extraction steps for both high- and low-resolution images. However, the high- and low-resolution features thus obtained may differ in terms of information content and information scale. This leads to a robust coupled relationship between the obtained high- and low-resolution images feature is difficult to be learned. Therefore, a coupled non-negative matrix factorization with a deep part-based feature learning method is proposed for the LR image recognition task. This method is divided into two main steps. Firstly, the depth non-negative matrix factorization method by graph convolution is used to obtain a robust depth underlying basis images of HR images for feature representation. Then the coupled relationship between the depth basis images feature of the HR images and the LR basis images feature is learned. Learning More Robust LR Basis images features using depth underlying features of HR images to assist LR images. Improve the expression of LR basis images of features. Experimental results show that the method can provide higher recognition performance. It outperforms existing image recognition methods in image recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Image recognition

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Evolution of Web API Cooperation Network via Exploring Community Structure and Popularity

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6659-6671页

作者： Kang, Guosheng Wang, Yang Liu, Jianxun Cao, Buqing Xiao, Yong Xu, Yu Hunan University of Science and Technology Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China University of Cambridge Cambridge Baker Systems Genomics Initiative British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care CambridgeCB1 8RN United Kingdom

With the growing popularity of the Internet, Web applications have become increasingly essential in our daily lives. Web application programming interfaces (Web APIs) play a crucial role in facilitating interaction between applications. However, most Web service platforms are suffering from the imbalance of Web services now, many services of good quality but low popularity are difficult to be invoked even once and do not create direct connections with the users. Some graph-based Web service recommendation methods also often present a long-tailed distribution of recommended Web services due to limited Mashup-API invocation relationships. To relieve this problem and promote service recommendation, in this article, we propose a community structure and popularity-based approach by constructing an evolving cooperation network for Web APIs. We leverage the Louvain algorithm in community detection to assign community structure to each Web API and consider both the popularity and community structure in constructing the network. By optimizing the Barabαsi-Albert (BA) evolving network model, we demonstrate that our approach outperforms the BA, Bianconi-Barabαsi (BB), and popularity-similarity optimization (PSO) models in Web service clustering. Based on our proposed evolutionary network model for the evolutionary extension of API cooperation network and used for downstream Web service recommendation tasks, the experimental results also show that our recommended approach outperforms some other baseline models for Web service recommendation. © 2020 IEEE.

关键词： Web services

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Attribute-Based Data Sharing Scheme with Flexible Search Functionality for Cloud-Assisted Autonomous Transportation System

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IEEE Transactions on Industrial Informatics 2023年第11期19卷 10977-10986页

作者： Xiong, Hu Wang, Hanxiao Meng, Weizhi Yeh, Kuo-Hui University of Electronic Science and Technology of China School of Information and Software Engineering Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby2800 Denmark National Dong Hwa University Department of Information Management Hualien97401 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804201 Taiwan

The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data sharing. To tackle this problem efficiently, in this article, we put forward the group-attribute-based encryption with equality test (G-ABEET) scheme. The presented G-ABEET allows sensors equipped in vehicles to encrypt traffic data with an expressive access policy before sharing it. Only users with attributes required by the access policy ought to access the shared ciphertexts, thus achieving selective one-to-many data sharing. Meanwhile, the authorized cloud server could provide group users with equality tests over the ciphertexts, realizing ciphertext search ability. Furthermore, with the group mechanism, the G-ABEET scheme could resist offline message recovery attacks. Besides, in the standard model, we give rigorous security proof of the G-ABEET construction. The feasibility and efficiency of G-ABEET are demonstrated by experimental simulations. © 2005-2012 IEEE.

关键词： Cloud computing

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