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Future Communications and Networks (FCN), International Conference on

作者： Zhaoyang Liu Haoxin Lin Xijun Wang Jie Gong Xiang Chen School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin China State Key Lab of Novel Software Technology Nanjing University Nanjing China Guangdong Key Laboratory of Information Security Technology School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350396034

ISBN: (纸本)9798350396041

With the growing demand for wireless communication networks, achieving efficient and equitable channel access schemes has become paramount. In this paper, we delve into the realm of distributed channel access in homogeneous wireless networks, focusing on both fairness and efficiency. We introduce a media access control protocol, named Fair and Efficient Multiple Access (FEMA), by leveraging decomposed Multi-Agent Deep Deterministic Policy Gradient (MADDPG). FEMA decouples the dual objectives into self-oriented efficiency reward and team-oriented fairness reward. Correspondingly, local critic networks and global critic networks target self-oriented reward and team-oriented reward, respectively. Extensive simulations demonstrate FEMA’s superiority over conventional MAC protocols and other multiple access techniques based on deep reinforcement learning, showcasing its ability to simultaneously enhance fairness and efficiency.

关键词： Protocols Wireless networks Focusing Media Access Protocol Dynamic scheduling Deep reinforcement learning Resource management

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A Robust Blind Watermarking Scheme for Color Images Using Quaternion Fourier Transform 6th

A Robust Blind Watermarking Scheme for Color Images Using Qu...

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6th International Conference on Artificial Intelligence and security, ICAIS 2020

作者： Liang, Renjie Zheng, Peijia Fang, Yanmei Song, Tingting School of Data and Computer Science Guangdong Key Laboratory of Information Security Technology Sun Yat-Sen University Guangzhou510006 China

ISBN: (纸本)9783030578800

With the advancement of image devices, massive image data are being generated exponentially, which indirectly leads to more copyright issues over the images. Image watermarking is an important method for copyright protection. Considering that color images dominate the real-world image data, designing copyright protection techniques specially for color images are very meaningful. In this paper, we propose a robust blind watermarking scheme for color images based on quaternion Fourier transform (QFT). After converting the color image data into QFT coefficients, we embed the watermark message into the four channels in QFT domain, to improve the embedding capacity and robustness. We also conduct experiment to investigate the performance of visual quality and robustness, by comparing with some other common signal transforms. The experimental results show that our scheme achieves a satisfactory performance. © 2020, Springer Nature Switzerland AG.

关键词： Image watermarking

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A mathematical model for efficient extraction of key locations from point-cloud data in track area

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Industrial Artificial Intelligence 2023年第1期1卷 1-14页

作者： Chen, Shuyue Wu, Jiaolv Lu, Jian Wang, Xizhao College of Mathematics and Statistics Shenzhen University Shenzhen China School of Software Engineering Shenzhen Institue of Information Technology Shenzhen China Shenzhen No. 3 Vocational School of Technology Shenzhen China College of Engineering Huaqiao University Quanzhou China Shenzhen Key Lab. of Advanced Machine Learning and Applications Shenzhen University Shenzhen China Guangdong Key Lab. of Intelligent Information Process Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

During the construction of a metro system, it is inevitable that deviations will occur between the excavated tunnel and the original designed scheme. As such, it is necessary to adjust the designed scheme to accommodate these discrepancies. Specifically, the adjustment of the designed scheme involves a rigorous process of repeatedly selecting and verifying the feasibility of the proposed modifications using point-cloud data obtained from the tunnel. However, this process can be considerably time-consuming due to the large-scale and potentially redundant nature of the point-cloud data. This paper proposes a mathematical model for point-cloud data acquired in measuring a mined tunnel, which may deviate from the originally designed one. The modeling, which mainly includes determining its normal plane, and building the equation of tunnel point-cloud data, is to quickly extract several key locations in the tunnel surface for modifying the original design in order to achieve a minimum error between the modified design and the mined tunnel. In comparison with the conventional processing of extracting several key locations directly from point-cloud data, our model shows a significant promotion of extraction efficiency under an acceptable error bound. The model is tested in a real tunnel point-cloud data and the testing results confirm the increase of fitting accuracy and the decrease of computational load.

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Secure outsourced numerical solution of algebraic equations 6th

Secure outsourced numerical solution of algebraic equations

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6th International Conference on Artificial Intelligence and security,ICAIS 2020

作者： Zeng, Ke Zheng, Peijia Liu, Hongmei School of Data and Computer Science Guangdong Key Laboratory of Information Security Technology Sun Yat-Sen University Guangzhou510006 China

ISBN: (纸本)9789811581007

Numerical methods are designed to provide numerical solutions of algebraic equations, because there are not analytical solutions for algebraic equations whose degrees are larger than four. In cloud computing, outsourcing numerical solutions to the cloud raises privacy issues due to the privacy sensitive coefficients. In this paper, we propose an effective privacy-preserving computation outsourcing protocol for the common numerical solutions of algebraic equations. We first present a protocol that can securely evaluate Newton iterative method with the preservation of the private coefficients, by relying on somewhat homomorphic encryption. We then provide two implementations of our protocol based on the two popular somewhat homomorphic encryption schemes, i.e., the Brakerski-Gentry-Vaikuntanathan (BGV) scheme and the CKKS scheme. We conduct experiments to compare the two implementations and analyze the security and effectiveness. The experimental results show that the proposed scheme is effective in practice, and the CKKS-based implementation is general better than the BGV-based implementation. © Springer Nature Singapore Pte Ltd 2020.

关键词： Outsourcing

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NTIRE 2023 Challenge on Light Field Image Super-Resolution: dataset, Methods and Results

NTIRE 2023 Challenge on Light Field Image Super-Resolution: ...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany Eth Zürich Switzerland Sun Yat-sen University The Shenzhen Campus of Sun Yat-sen University China Bigo Technology Pte. Ltd. Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

ISBN: (纸本)9798350302493

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023 IEEE.

关键词： Image enhancement

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The minority matters: a diversity-promoting collab.rative metric learning algorithm 22

The minority matters: a diversity-promoting collaborative me...

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Proceedings of the 36th International Conference on Neural information Processing Systems

作者： Shilong Bao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Laboratory of Big Data Mining and Knowledge Management CAS and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

Collab.rative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collab.rative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collab.rative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a Diversity Control Regularization Scheme (DCRS) to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML.

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SemiCD-VL: Visual-Language Model Guidance Makes Better Semi-supervised Change Detector

arXiv

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arXiv 2024年

作者： Li, Kaiyu Cao, Xiangyong Deng, Yupeng Song, Jiayi Liu, Junmin Meng, Deyu Wang, Zhi School of Software Engineering Xi’an Jiaotong University Xi’an710049 China School of Computer Science and Technology Ministry of Education Key Lab For Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an China Guangdong Guangzhou China

Change Detection (CD) aims to identify pixels with semantic changes between images. However, annotating massive numbers of pixel-level images is lab.r-intensive and costly, especially for multi-temporal images, which require pixel-wise comparisons by human experts. Considering the excellent performance of visual language models (VLMs) for zero-shot, open-vocabulary, etc. with prompt-based reasoning, it is promising to utilize VLMs to make better CD under limited lab.led data. In this paper, we propose a VLM guidance-based semisupervised CD method, namely SemiCD-VL. The insight of SemiCD-VL is to synthesize free change lab.ls using VLMs to provide additional supervision signals for unlab.led data. However, almost all current VLMs are designed for single-temporal images and cannot be directly applied to bi- or multi-temporal images. Motivated by this, we first propose a VLM-based mixed change event generation (CEG) strategy to yield pseudo lab.ls for unlab.led CD data. Since the additional supervised signals provided by these VLM-driven pseudo lab.ls may conflict with the original pseudo lab.ls from the consistency regularization paradigm (e.g. FixMatch), we propose the dual projection head for de-entangling different signal sources. Further, we explicitly decouple the bi-temporal images semantic representation through two auxiliary segmentation decoders, which are also guided by VLM. Finally, to make the model more adequately capture change representations, we introduce contrastive consistency regularization by constructing feature-level contrastive loss in auxiliary branches. Extensive experiments show the advantage of SemiCD-VL. For instance, SemiCD-VL improves the FixMatch baseline by +5.3 IoUc on WHU-CD and by +2.4 IoUc on LEVIR-CD with 5% lab.ls, and SemiCD-VL requires only 5% to 10% of the lab.ls to achieve performance similar to the supervised methods. In addition, our CEG strategy, in an un-supervised manner, can achieve performance far superior to state-of-t

关键词： Visual languages

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Disentangled Noisy Correspondence Learning

arXiv

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arXiv 2024年

作者： Dang, Zhuohang Luo, Minnan Wang, Jihong Jia, Chengyou Han, Haochen Wan, Herun Dai, Guang Chang, Xiaojun Wang, Jingdong The School of Computer Science and Technology The Ministry of Education Key Laboratory of Intelligent Networks and Network Security The Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Shaanxi Xi'An710049 China SGIT AI Lab State Grid Shaanxi Electric Power Company Limited State Grid Corporation of China Shaanxi China The School of Information Science and Technology University of Science and Technology China United Arab Emirates The Baidu Inc China

Cross-modal retrieval is crucial in understanding latent correspondences across modalities. However, existing methods implicitly assume well-matched training data, which is impractical as real-world data inevitably involves imperfect alignments, i.e., noisy correspondences. Although some works explore similarity-based strategies to address such noise, they suffer from sub-optimal similarity predictions influenced by modality-exclusive information (MEI), e.g., background noise in images and abstract definitions in texts. This issue arises as MEI is not shared across modalities, thus aligning it in training can markedly mislead similarity predictions. Moreover, although intuitive, directly applying previous cross-modal disentanglement methods suffers from limited noise tolerance and disentanglement efficacy. Inspired by the robustness of information bottlenecks against noise, we introduce DisNCL, a novel information-theoretic framework for feature Disentanglement in Noisy Correspondence Learning, to adaptively balance the extraction of MII and MEI with certifiable optimal cross-modal disentanglement efficacy. DisNCL then enhances similarity predictions in modality-invariant subspace, thereby greatly boosting similarity-based alleviation strategy for noisy correspondences. Furthermore, DisNCL introduces soft matching targets to model noisy many-to-many relationships inherent in multi-modal input for noise-robust and accurate cross-modal alignment. Extensive experiments confirm DisNCL's efficacy by 2% average recall improvement. Mutual information estimation and visualization results show that DisNCL learns meaningful MII/MEI subspaces, validating our theoretical analyses. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Secure linear aggregation using decentralized threshold additive homomorphic encryption for federated learning

arXiv

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arXiv 2021年

作者： Tian, Haibo Zhang, Fangguo Shao, Yunfeng Li, Bingshuai The GuangDong Province Key Laboratory of Information Security Technology School of Data and Computer Science Sun Yat-Sen University Guangdong Guangzhou510275 China Huawei Noah's Ark Lab.

Secure linear aggregation is to linearly aggregate private inputs of different users with privacy protection. The server in a federated learning (FL) environment can fulfill any linear computation on private inputs of users through the secure linear aggregation. At present, based on pseudo-random number generator and one-time padding technique, one can efficiently compute the sum of user inputs in FL, but linear calculations of user inputs are not well supported. Based on decentralized threshold additive homomorphic encryption (DTAHE) schemes, this paper provides a secure linear aggregation protocol, which allows the server to multiply the user inputs by any coefficients and to sum them together, so that the server can build a full connected layer or a convolution layer on top of user inputs. The protocol adopts the framework of Bonawitz et al. to provide fault tolerance for user dropping out, and exploits a blockchain smart contract to encourage the server honest. The paper gives a security model, security proofs and a concrete lattice based DTAHE scheme for the protocol. It evaluates the communication and computation costs of known DTAHE construction methods. The evaluation shows that an elliptic curve based DTAHE is friendly to users and the lattice based version leads to a light computation on the server. Copyright © 2021, The Authors. All rights reserved.

关键词： Smart contract

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Hfcyclegan: High-Frequency information Guided Cycle-Consistent Adversarial Network for Fundus Image Enhancement

SSRN

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SSRN 2025年

作者： Liu, Shaopeng Wang, Kai Wu, Xiaohang Xu, Fabao Zhou, Ying Wang, Xinpeng Wang, Qinkang Cen, Haoran Guo, Jianhua Lu, Xu School of Computer Science Guangdong Polytechnic Normal University Guangzhou510665 China Zhongshan Ophthalmic Center State Key Laboratory of Ophthalmology Sun Yat-Sen University Guangdong Guangzhou China Qilu Hospital Shandong University Jinan China Shenzhen Institute of Information Technology Shenzhen China Guangdong Provincial Key Laboratory of Intellectual Property & Big Data Guangzhou510665 China Pazhou Lab Guangzhou510330 China

The development of fundus imaging techniques has greatly supported the diagnosis of ophthalmic *** advancements, poor imaging quality, such as artifacts or insufficient illumination, poses challenges for disease *** clinical, it is often difficult to obtain paired data for fundus image enhancement. For unpaired data, CycleGAN-based methods are commonly used to learn the style transfer from low-quality images to high-quality images for enhancement, improving the aforementioned transfer process through brightness loss or regularization ***, fundus images are quite complex, and high-quality images can also exhibit uneven *** methods are not efficient in modeling the characteristics of fundus *** wavelet analysis, we found that the texture information represented by vessels is expressed in the high-frequency components of the ***, we designed a high-frequency information guided cycle-consistent generative adversarial network for unpaired fundus image enhancement, termed *** introduces a novel high-frequency guided generator (HFGen) and a corresponding frequency-aware discriminator (FADis).Specifically, HG uses the proposed high-frequency feature extraction module (HFM) to extract high-frequency features and inject them into the decoding process, outside of the main branches of the *** concatenate the input image with the frequency components obtained from wavelet transformation and input them into *** embedded channel-wise attention blocks of discriminator enable HFCycleGAN to focus on the frequency information differences between low-quality and high-quality fundus *** experiments on two real-world datasets and one synthetic dataset verify that HFcycleGAN outperforms existing *** conclusion, the HFcycleGAN effectively enhances important structures such as vessels in fundus images, significantly impacting fundus image analysis. © 2025, The Author

关键词： Image segmentation

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