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arXiv 2023年

作者： Wan, Wei Hu, Shengshan Li, Minghui Lu, Jianrong Zhang, Longling Zhang, Leo Yu Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Federated learning (FL) is a nascent distributed learning paradigm to train a shared global model without violating users' privacy. FL has been shown to be vulnerable to various Byzantine attacks, where malicious participants could independently or collusively upload well-crafted updates to deteriorate the performance of the global model. However, existing defenses could only mitigate part of Byzantine attacks, without providing an all-sided shield for FL. It is difficult to simply combine them as they rely on totally contradictory assumptions. In this paper, we propose FPD, a four-pronged defense against both non-colluding and colluding Byzantine attacks. Our main idea is to utilize absolute similarity to filter updates rather than relative similarity used in existingI works. To this end, we first propose a reliable client selection strategy to prevent the majority of threats in the bud. Then we design a simple but effective score-based detection method to mitigate colluding attacks. Third, we construct an enhanced spectral-based outlier detector to accurately discard abnormal updates when the training data is not independent and identically distributed (non-IID). Finally, we design update denoising to rectify the direction of the slightly noisy but harmful updates. The four sequentially combined modules can effectively reconcile the contradiction in addressing non-colluding and colluding Byzantine attacks. Extensive experiments over three benchmark image classification datasets against four state-of-the-art Byzantine attacks demonstrate that FPD drastically outperforms existing defenses in IID and non-IID scenarios (with 30% improvement on model accuracy). © 2023, CC BY.

关键词： Classification (of information)

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Downstream-agnostic Adversarial Examples

Downstream-agnostic Adversarial Examples

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International Conference on Computer Vision (ICCV)

作者： Ziqi Zhou Shengshan Hu Ruizhi Zhao Qian Wang Leo Yu Zhang Junhui Hou Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan University School of Information and Communication Technology Griffith University Department of Computer Science City University of Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial *** this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder. Our codes are available at: https://***/CGCL-codes/AdvEncoder.

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The Security and Privacy of Mobile Edge computing: An Artificial Intelligence Perspective

arXiv

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arXiv 2024年

作者： Wang, Cheng Yuan, Zenghui Zhou, Pan Xu, Zichuan Li, Ruixuan Wu, Dapeng Oliver Hubei Engineering Research Center on Big Data Security Key Laboratory of Distributed System Security of Hubei Province School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Software The Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian University of Technology Dalian116024 China Intelligent and Distributed Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong

Mobile Edge computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network’s edge. By shifting the load of cloud computing to individual local servers, MEC helps meet the requirements of ultralow latency, localized data processing, and extends the potential of Internet of Things (IoT) for end-users. However, the crosscutting nature of MEC and the multidisciplinary components necessary for its deployment have presented additional security and privacy concerns. Fortunately, Artificial Intelligence (AI) algorithms can cope with excessively unpredictable and complex data, which offers a distinct advantage in dealing with sophisticated and developing adversaries in the security industry. Hence, in this paper we comprehensively provide a survey of security and privacy in MEC from the perspective of AI. On the one hand, we use European Telecommunications Standards Institute (ETSI) MEC reference architecture as our based framework while merging the Software Defined Network (SDN) and Network Function Virtualization (NFV) to better illustrate a serviceable platform of MEC. On the other hand, we focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI. Finally, we comprehensively discuss the opportunities and challenges associated with applying AI to MEC security and privacy as possible future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Network security

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Gray Learning from Non-IID data with Out-of-distribution Samples

arXiv

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arXiv 2022年

作者： Zhao, Zhilin Cao, Longbing Wang, Chang-Dong The Data Science Lab School of Computing and DataX Research Centre Macquarie University SydneyNSW2109 Australia The School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Computational Science Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

The integrity of training data, even when annotated by experts, is far from guaranteed, especially for non-IID datasets comprising both in- and out-of-distribution samples. In an ideal scenario, the majority of samples would be in-distribution, while samples that deviate semantically would be identified as out-of-distribution and excluded during the annotation process. However, experts may erroneously classify these out-of-distribution samples as in-distribution, assigning them labels that are inherently unreliable. This mixture of unreliable labels and varied data types makes the task of learning robust neural networks notably challenging. We observe that both in- and out-of-distribution samples can almost invariably be ruled out from belonging to certain classes, aside from those corresponding to unreliable ground-truth labels. This opens the possibility of utilizing reliable complementary labels that indicate the classes to which a sample does not belong. Guided by this insight, we introduce a novel approach, termed Gray Learning (GL), which leverages both ground-truth and complementary labels. Crucially, GL adaptively adjusts the loss weights for these two label types based on prediction confidence levels. By grounding our approach in statistical learning theory, we derive bounds for the generalization error, demonstrating that GL achieves tight constraints even in non-IID settings. Extensive experimental evaluations reveal that our method significantly outperforms alternative approaches grounded in robust statistics. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Efficient meta neural heuristic for multi-objective combinatorial optimization 23

Efficient meta neural heuristic for multi-objective combinat...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Jinbiao Chen Jiahai Wang Zizhen Zhang Zhiguang Cao Te Ye Siyuan Chen School of Computer Science and Engineering Sun Yat-sen University P.R. China School of Computer Science and Engineering Sun Yat-sen University P.R. China and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University P.R. China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R. China School of Computing and Information Systems Singapore Management University Singapore

Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are still struggling to achieve high learning efficiency and solution quality. To tackle this issue, we propose an efficient meta neural heuristic (EMNH), in which a meta-model is first trained and then fine-tuned with a few steps to solve corresponding single-objective subproblems. Specifically, for the training process, a (partial) architecture-shared multi-task model is leveraged to achieve parallel learning for the meta-model, so as to speed up the training; meanwhile, a scaled symmetric sampling method with respect to the weight vectors is designed to stabilize the training. For the fine-tuning process, an efficient hierarchical method is proposed to systematically tackle all the subproblems. Experimental results on the multi-objective traveling salesman problem (MOTSP), multi-objective capacitated vehicle routing problem (MOCVRP), and multi-objective knapsack problem (MOKP) show that, EMNH is able to outperform the state-of-the-art neural heuristics in terms of solution quality and learning efficiency, and yield competitive solutions to the strong traditional heuristics while consuming much shorter time.

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A review of big data technology and its application in cancer care

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Computers in Biology and Medicine 2024年 176卷 108577-108577页

作者： Xiao, Tianyun Kong, Shanshan Zhang, Zichen Hua, Dianbo Liu, Fengchun Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Hebei Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Hebei Tangshan063210 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Hebei Tangshan China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Hebei Tangshan China Beijing Sitairui Cancer Data Analysis Joint Laboratory Beijing101149 China

The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future. © 2024 Elsevier Ltd

关键词： Big data

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Multi-User Probabilistic Semantic Communication with Semantic Compression Ratio Optimization

Multi-User Probabilistic Semantic Communication with Semanti...

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IEEE International Conference on Communications Workshops, ICC

作者： Zhouxiang Zhao Zhaohui Yang Mingzhe Chen Changsheng You Qianqian Yang Wei Xu Zhaoyang Zhang College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Info. Proc. Commun. & Netw. (IPCAN) Hangzhou China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China National Mobile Communications Research Laboratory Southeast University Nanjing China

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In this paper, the problem of joint transmission and computation resource allocation for a multi-user probabilistic semantic communication (PSC) network is investigated. In the considered model, users employ semantic information extraction techniques to compress their large-sized raw data before transmitting them to a multi-antenna base station (BS). Our model represents the raw data through comprehensive knowledge graphs, utilizing shared probability graphs between users and the BS for efficient semantic compression. The resource allocation problem is formulated as an optimization problem with the objective of maximizing the sum of equivalent rate of all users, considering total power budget constraint. This joint optimization problem inherently addresses the delicate balance between transmission efficiency and computational complexity. To address this optimization challenge, we present an iterative algorithm in which the optimal solution for the semantic compression ratio of a specific user is determined at each iteration. Numerical results validate the effectiveness of the proposed scheme.

关键词： Conferences Computational modeling Semantics Knowledge graphs Probabilistic logic Information retrieval data models

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Medical Image Registration and Its Application in Retinal Images: A Review

arXiv

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arXiv 2024年

作者： Nie, Qiushi Zhang, Xiaoqing Hu, Yan Gong, Mingdao Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, these surveys have not systematically summarized methodologies of existing medical image registration methods. To this end, we provide a comprehensive review of these methods from traditional and deep learning-based directions, aiming to help audiences understand the development of medical image registration quickly. In particular, we review recent advances in retinal image registration at the end of each section, which has not attracted much attention. Additionally, we also discuss the current challenges of retinal image registration and provide insights and prospects for future research. © 2024, CC BY-NC-SA.

关键词： Deep learning

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Machine-learning-assisted intelligent synthesis of UiO-66(Ce):Balancing the trade-off between structural defects and thermal stability for e hydrogenation of Dicyclopentadiene

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Materials Genome engineering Advances 2024年第3期2卷 96-106页

作者： Jing Lin Tao Ban Tian Li Ye Sun Shenglan Zhou Rushuo Li Yanjing Su Jitti Kasemchainan Hongyi Gao Lei Shi Ge Wang Beijing Key Laboratory of Function Materials for Molecule&Structure Construction Beijing Advanced Innovation Center for Materials Genome EngineeringSchool of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijingChina Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and EngineeringBeihang UniversityBeijingChina Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologySchool of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijingChina Department of Chemical Technology Chulalongkorn UniversityBangkokThailand

Metal-organic frameworks(MOFs),renowned for structural diversity and design flexibility,exhibit potential in ***,the pursuit of higher catalytic activity through defects often compromises stability,requiring a delicate *** trial-and-error method for optimizing synthesis parameters within the complex chemical space is ***,taking the typical MOF UiO-66(Ce)as an illustrative example,a closed loop workflow is built,which integrates ma-chine learning(ML)-assissted prediction,multi-objective optimization(MOO)and experimental preparation to synergistically optimize the defect content and thermal stability of UiO-66(Ce)for efficient hydrogenation of dicyclopentadiene(DCPD).An automatic data extraction program ensures data accuracy,establishing a high-quality *** is employed to explore the intricate synthesis-structure-property correlations,enabling precise delineation of pure-phase subspace and accurate predictions of *** two iterations,MOO model identifies optimal protocols for high defect content(>40%)and thermal stability(>300℃).The optimized UiO-66(Ce)exhibits superior catalytic performance in hydroge-nation of DCPD,validating the precision and reliability of our *** ML-assisted approach offers a valuable paradigm for solving the trade-off riddle in materials field.

关键词： defect content machine learning metal-organic frameworks multi-objective optimization thermal stability

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Resource Allocation for Semantic Relay Aided Wireless Networks with Probability Graph

Resource Allocation for Semantic Relay Aided Wireless Networ...

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IEEE International Conference on Communications (ICC)

作者： Jiahui Zhao Ming Chen Zhaohui Yang Changsheng You Mingzhe Chen National Mobile Communications Research Laboratory Southeast University Nanjing China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Info. Proc. Commun. & Netw. (IPCAN) Hangzhou China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In this paper, we introduce a novel uplink semantic relay (SemRelay)-aided wireless communication system, catering to multiple users by leveraging a shared probability graph between the SemRelay and the base station (BS). In this system, users transmit text information to the SemRelay through conventional bit transmission, and the SemRelay compresses this information using a knowledge based characterized by probability graph before transmitting it to the BS through semantic communication. Then, the BS recovers the information based on the shared probability graph. While the semantic information compression incurs computational resource consumption, it significantly reduces communication resource usage. This paper addresses the challenge of minimizing overall system latency through jointly optimizing communication and computation re-source allocation, considering limited wireless resources and the system's energy budget. To address this problem, we introduce an efficient iterative algorithm, which employs block coordinate descent for communication resource allocation and exhaustive searching for determining the optimal data compression scheme. In particular, both power allocation subproblem and bandwidth allocation subproblem are proved to be convex. The complexity analysis of the proposed algorithm are also provided. Numerical results validate the effectiveness of the proposed algorithm and the superior performance of semantic communication compared to the conventional bit transmission.

关键词： Wireless networks Semantics Knowledge based systems data compression Search problems Complexity theory Resource management

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