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arXiv 2021年

作者： Wang, Lei Liu, Xin Zhang, Yin State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences China School of Data Science The Chinese University of Hong Kong Shenzhen China

Sparse principal component analysis (PCA) improves interpretability of the classic PCA by introducing sparsity into the dimension-reduction process. Optimization models for sparse PCA, however, are generally non-convex, non-smooth and more difficult to solve, especially on large-scale datasets requiring distributed computation over a wide network. In this paper, we develop a distributed and centralized algorithm called DSSAL1 for sparse PCA that aims to achieve low communication overheads by adapting a newly proposed subspace-splitting strategy to accelerate convergence. Theoretically, convergence to stationary points is established for DSSAL1. Extensive numerical results show that DSSAL1 requires far fewer rounds of communication than state-of-the-art peer methods. In addition, we make the case that since messages exchanged in DSSAL1 are well-masked, the possibility of private-data leakage in DSSAL1 is much lower than in some other distributed algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Principal component analysis

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Stand-in Backdoor: A Stealthy and Powerful Backdoor Attack

Stand-in Backdoor: A Stealthy and Powerful Backdoor Attack

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Shuang Li Hongwei Li Hanxiao Chen School of Computer Science and Engineering University of Electronic Science and Technology of China China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

ISBN: (纸本)9781728181059

Lack of transparency in deep learning models makes them vulnerable to backdoor attack, which can cause severe security consequences. For a backdoored model, the specific inputs can trigger misclassification rules while it performs normal behaviors on clean data. Existing backdoor attacks usually generate poisoned data by adding an obvious trigger to the original data and mislabeling them, which suffers from poor invisibility and hence can be easily detected. In this paper, we propose Stand-in Backdoor, a more stealthy and powerful backdoor attack, which can completely hide the trigger while maintaining correct labels of poisoned data. Specifically, we design a novel optimization strategy to transform triggers into imperceptible perturbation in the feature space. Furthermore, utilizing the transferability of feature perturbation, we fine-tune the victim model with well-constructed poisoned data that are correctly labeled. Extensive experiments conducted on various image classification tasks demonstrate that our attack outperforms the state-of-the-art work in terms of backdoor stealth and attack performance, without sacrificing the model's utility.

关键词： Perturbation methods Transforms Resists Manuals Inspection data models Security

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Category re-balancing based domain adaptive semantic segmentation

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Procedia Computer Science 2024年 242卷 1155-1162页

作者： Siyu Zhu Fenfen Zhou Kai Li Minghao Liu Haonan Wen Computer School Beijing information science and technology university 102206 No.55 Taihang Road Changping District Beijing China Beijing Key Laboratory of Information Service Engineering Beijing Union University 100101 Beijing China. College of Robotics Beijing Union University 100027 Beijing China National Engineering Research Center of Visual Technology School of Computer Science Peking University 100871 Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences No.19(A) Yuquan Road Shijingshan District Beijing 100049 P.R. China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 of Zhongguancun East Road Haidian District Beijing 100190 Beijing China

The semantic segmentation task faces the bottleneck of high manual annotation costs. Domain adaptive learning provides an effective solution through inter domain knowledge transfer. However, existing domain adaptive semantic segmentation tasks face the situation of category asymmetry between scenes when dealing with complex scenes. Existing methods lack handling of class imbalance in the prediction process. In domain adaptive semantic segmentation tasks, class imbalance limits the performance of inter domain transfer. Our work focuses on the impact of category distribution on domain adaptive semantic segmentation tasks in image pairs. Inspired by long tail learning, we divided categories into head, middle, and tail categories, and designed effective strategies to enhance the contribution of tail categories in domain adaptation tasks. We introduce the concept of category rebalancing into domain adaptive image matching, and our method is lightweight and effective, proving its effectiveness on general datasets.

关键词： Long-tail learning Domain adaptation Semantic segmentation Transfer learning

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A Study on the Mechanism and Kinetic of Nitrate Reduction by the nZVI-g-C3N4/TiO2 composite under the simulated sunlight

Research Square

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Research Square 2021年

作者： Wei, Linyu Tian, Jing Wang, Qing Liu, Yuanyuan Yu, Yi Yang, Chun College of Chemistry and Materials Science Sichuan Normal University Chengdu610066 China Sichuan Engineering Laboratory of Livestock Manure Treatment and Recycling Chengdu140028001 China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University Chengdu610068 China

g-C3N4/TiO2 composite has excellent photoelectric properties and is considered as a good carrier of nanoparticles. A novel composite of nZVI-g-C3N4/TiO2 was successfully synthesized through in-situ growth nZVI on the surface of g-C3N4/TiO2 with liquid phase reduction method. The composite was characterized by TEM, XRD, EDS and evaluated its nitrate removal efficiency. The effects of composite dosage, solution initial pH and HCOOH concentration on nitrate reduction were investigated. The results showed that nitrate was rapidly reduced by nZVI-g-C3N4/TiO2 composite. The dosage of 4 g/L nZVI-g-C3N4/TiO2 composite and 3.0 mM of HCOOH concentration was more suitable for nitrate reduction. Solution initial pH had little impact on the nitrate reduction efficiency, but affected the proportion of the nitrate reduction products. The mechanism of nitrate reduction in the nZVI-gC3N4/TiO2/HCOOH-Xe-lamp system was proposed. The nZVI-gC3N4/TiO2 composite could be considered as a viable and promising technology for water pollution remediation. © 2021, CC BY.

关键词： Nitrates

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Semantic Level Image Retrieval in Domain Adaptive Semantic Segmentation

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Procedia Computer Science 2024年 242卷 1163-1170页

作者： Siyu Zhu Fenfen Zhou Kai Li Haonan Wen Minghao Liu Computer School Beijing information science and technology university 102206 No.55 Taihang Road Changping District Beijing China Beijing Key Laboratory of Information Service Engineering Beijing Union University 100101 Beijing China. College of Robotics Beijing Union University 100027 Beijing China National Engineering Research Center of Visual Technology School of Computer Science Peking University 100871 Beijing China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 of Zhongguancun East Road Haidian District Beijing 100190 Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences No.19(A) Yuquan Road Shijingshan District Beijing 100049 P.R. China

The traditional domain adaptation task is not sufficient in mining inter domain image context. We propose a solution from the perspective of image semantic level retrieval. Image semantic level retrieval is based on the high-level semantics of images, retrieving the similarity or correspondence between images in terms of content and scene between domains. Our work focuses on the meaning, structure, and contextual information expressed by the target image, and retrieves the most relevant image from the source domain to improve the accuracy of domain adaptation. Furthermore, in response to the negative transfer brought about by image retrieval, we will introduce a category level weight adjustment module to further improve the accuracy of semantic level retrieval and weaken the negative impact caused by low precision categories. Finally, we demonstrated the effectiveness of semantic level image retrieval through experiments.

关键词： Image retrieval Domain adaptation Semantic segmentation Transfer learning

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The DUDFTO Attack: Towards Down-to-UP Timeout Probing and Dynamically Flow Table Overflowing in SDN

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IEEE Transactions on Network and Service Management 2025年

作者： Li, Jiasong Cui, Yunhe Chen, Yi Shen, Guowei Guo, Chun Qian, Qing Guizhou University State Key Laboratory of Public Big Data Engineering Research Center of Text Computing and Cognitive Intelligence Ministry of Education College of Computer Science and Technology Guizhou Provincial Characteristic Key Laboratory of Software Engineering and Information Security Guiyang550000 China Guizhou University of Finance and Economics School of Information Guiyang550000 China

As a new network structure, the decoupling of the control plane and forwarding plane makes Software-Defined Networking (SDN) widely used in large-scale network scenarios. However, the decoupling network architecture also brings new vulnerabilities. The flow table overflow attack is an attack strategy that can overwhelm SDN switches. Nevertheless, the existing flow table overflow attacks may fail in probing timeouts and match fields of flow entries, due to link failure, measurement of the round-trip time (RTT) of different packets, interference of hard-timeout and idle-timeout. Meanwhile, the stealthiness of the existing attacks may also reduce, as these attacks use fixed attack rate. To improve the timeout probing accuracy and the stealthiness of attack, a new flow table overflow attack strategy, DUDFTO, is proposed to accurately probe timeout settings and match fields, then stealthily overflow SDN flow tables. Firstly, it probes the match fields by measuring the one-sided transmission delay of the packets. After that, DUDFTO designs a down-to-up feedback-based timeout probing algorithm to eliminate the issues caused by high RTT, link failure, interference between hard-timeout and idle-timeout. Then, DUDFTO designs a dynamic attack packets sending algorithm to improve its stealthiness. Finally, DUDFTO probes the flow table state to stop sending new attack packets. The evaluation results demonstrate that DUDFTO outperforms the existing attacks in terms of match fields probing ability, timeout probing relative error, number of packet_in and flow_mod messages generated by the attack, rate distribution of packet_in and flow_mod messages generated during the attack, and number of detected attack packets. © 2004-2012 IEEE.

关键词： flow table overflow attack information probing SDN switchs

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Wireless Large AI Model: Shaping the AI-Native Future of 6G and Beyond

arXiv

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arXiv 2025年

作者： Zhu, Fenghao Wang, Xinquan Li, Xinyi Zhang, Maojun Chen, Yixuan Huang, Chongwen Yang, Zhaohui Chen, Xiaoming Zhang, Zhaoyang Jin, Richeng Huang, Yongming Feng, Wei Yang, Tingting Bai, Baoming Gao, Feifei Yang, Kun Liu, Yuanwen Muhaidat, Sami Yuen, Chau Huang, Kaibin Wong, Kai-Kit Niyato, Dusit Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Automation Southeast University Nanjing210096 China Tsinghua University Beijing100084 China Pengcheng Laboratory Shenzhen518066 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China Suzhou215163 China The University of Hong Kong Hong Kong KU 6G Research Center Computer and Communication Engineering Khalifa University Abu Dhabi127788 United Arab Emirates School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology poised to enable this revolutionary vision is the wireless large AI model (WLAM), characterized by its exceptional capabilities in data processing, inference, and decision-making. In light of these remarkable capabilities, this paper provides a comprehensive survey of WLAM, elucidating its fundamental principles, diverse applications, critical challenges, and future research opportunities. We begin by introducing the background of WLAM and analyzing the key synergies with wireless networks, emphasizing the mutual benefits. Subsequently, we explore the foundational characteristics of WLAM, delving into their unique relevance in wireless environments. Then, the role of WLAM in optimizing wireless communication systems across various use cases and the reciprocal benefits are systematically investigated. Furthermore, we discuss the integration of WLAM with emerging technologies, highlighting their potential to enable transformative capabilities and breakthroughs in wireless communication. Finally, we thoroughly examine the high-level challenges hindering the practical implementation of WLAM and discuss pivotal future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Sorting

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An artificial intelligence and blockchain technology-based data management framework for multicenter randomized controlled trials

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Science Bulletin 2025年第6期70卷 856-860页

作者： Dongyuan Yun Xiaohang Wu Xi Chen Yahan Yang Yuanjun Shang Shaopeng Liu Dinesh Visva Gunasekeran Duoru Lin Lixue Liu Lanqin Zhao Wenben Chen Jingjing Chen Ling Jin Yuxuan Wu Jiaming Hong Weiling Hu Zhenzhe Lin Carol Y.Cheung Xiang Chen Peichen Xie Zhenzhen Liu Changhai Ding Patrick Yu-Wai-Man Keming Yu Daniel Shu Wei Ting Yizhi Liu Zibin Zheng Gansen Zhao Zhihua Xia Tien Yin Wong Haotian Lin on behalf of Advanced Technologies Improve Clinical Trial(ATICT)Group State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular DiseasesGuangzhou 510000China School of Biomedical Engineering Sun Yat-sen UniversityGuangzhou 510000China School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhou 510000China Singapore Eye Research Institute Singapore National Eye CenterSingapore999002Singapore School of Medical Information Engineering Guangzhou University of Chinese MedicineGuangzhou 510000China Department of Ophthalmology and Visual Sciences The Chinese University of Hong KongHong Kong 999077China Zhongshan School of Medicine Sun Yat-sen UniversityGuangzhou 510000China Clinical Research Center Zhujiang HospitalSouthern Medical UniversityGuangzhou 510000China Cambridge Center for Brain Repair and MRC Mitochondrial Biology Unit Department of Clinical NeurosciencesUniversity of CambridgeCambridgeCB20XYUnited Kingdom UCL Institute of Ophthalmology University College LondonLondonWC1E 6BTUnited Kingdom School of Data and Computer Science Sun Yat-sen UniversityGuangzhou 510000China School of Computer Science South China Normal UniversityGuangzhou 510000China College of Cyber Security Jinan UniversityGuangzhou 510000China Duke-NUS Medical School Singapore999002Singapore Tsinghua Medicine Tsinghua UniversityBeijing 100010China Hainan Eye Hospital and Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityHaikou 570100China Centre for Precision Medicine and Department of Genetics and Biomedical Informatics Zhongshan School of MedicineSun Yat-sen UniversityGuangzhou 510000China 不详

Randomized controlled trials(RCTs)provide optimal evidence of the effectiveness and safety of a new drug,a new medical device,or a new therapeutic strategy with the necessary scientific design[1].Traditional electronic data collection(EDC)systems for clinical trials primarily focus on data entry,validation,and compliance ***,their reliance on centralized architectures introduces vulnerabilities in data security and integrity。

关键词： Artificial Intelligence Multicenter Randomized Controlled Trials data Security scientific design traditional Blockchain Technology clinical trials randomized controlled trials rcts provide centralized architectures

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Size-fixed group discovery via multi-constrained graph pattern matching

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Information Sciences 2025年 690卷

作者： Guliu Liu Lei Li Guanfeng Liu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education 230601 Hefei China School of Computer Science and Information Engineering Hefei University of Technology 230601 Hefei China Department of Computing Macquarie University Sydney 2122 Australia

Multi-Constrained Graph Pattern Matching (MC-GPM) aims to match a pattern graph with multiple attribute constraints on its nodes and edges, and has garnered significant interest in various fields, including social-based e-commerce and trust-based group discovery. However, the existing MC-GPM methods do not consider situations where the number of each node in the pattern graph needs to be fixed, such as finding experts group with expert quantities and relations specified. In this paper, a Multi-Constrained Strong Simulation with the Fixed Number of Nodes (MCSS-FNN) matching model is proposed, and then a Trust-oriented Optimal Multi-constrained Path (TOMP) matching algorithm is designed for solving it. Additionally, two heuristic optimization strategies are designed, one for combinatorial testing and the other for edge matching, to enhance the efficiency of the TOMP algorithm. Empirical experiments are conducted on four real social network datasets, and the results demonstrate the effectiveness and efficiency of the proposed algorithm and optimization strategies.

关键词：

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Group Signatures with Decentralized Tracing 15th

Group Signatures with Decentralized Tracing

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15th International Conference on Information Security and Cryptology, Inscrypt 2019

作者： Lu, Tingting Li, Jiangtao Zhang, Lei Lam, Kwok-Yan Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute East China Normal University Shanghai China State Key Laboratory of Cryptology P.O. Box 5159 Beijing China Shanghai Institute for Advanced Communication and Data Science Shanghai China School of Computer Engineering and Science Shanghai University Shanghai China Nanyang Technological University Singapore Singapore

ISBN: (纸本)9783030429201

Group signature is a useful cryptographic primitive that allows a message to be signed by a user on behalf of a group which is managed by some trusted authority, namely the group manager. However, group signature schemes typically place a disturbingly high level of trust on the group manager, which has become a major deployment issue in cyber applications where there is no centralized trust management. In this paper, we investigate mechanisms that aim to achieve a balance between anonymity and accountability in group signatures by decentralizing the operation of tracing the signer. We propose a practical group signature scheme with decentralized tracing. When comparing with a similar result by Benjumea et al. (FC 2008), our proposal has the advantage of a shorter signature size. © 2020, Springer Nature Switzerland AG.

关键词： Managers

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