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arXiv 2024年

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

With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as versatile feature extractors, enabling downstream users to harness the benefits of expansive models with minimal effort through fine-tuning. Nevertheless, recent works have exposed a vulnerability in pre-trained encoders, highlighting their susceptibility to downstream-agnostic adversarial examples (DAEs) meticulously crafted by attackers. The lingering question pertains to the feasibility of fortifying the robustness of downstream models against DAEs, particularly in scenarios where the pre-trained encoders are publicly accessible to the attackers. In this paper, we initially delve into existing defensive mechanisms against adversarial examples within the pre-training paradigm. Our findings reveal that the failure of current defenses stems from the domain shift between pre-training data and downstream tasks, as well as the sensitivity of encoder parameters. In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models. Gen-AF employs a genetic-directed dual-track adversarial fine-tuning strategy in its first stage to effectively inherit the pre-trained encoder. This involves optimizing the pre-trained encoder and classifier separately while incorporating genetic regularization to preserve the model’s topology. In the second stage, Gen-AF assesses the robust sensitivity of each layer and creates a dictionary, based on which the top-k robust redundant layers are selected with the remaining layers held fixed. Upon this foundation, we conduct evolutionary adaptability fine-tuning to further enhance the model’s generalizability. Our extensive experiments, conducted across ten self-supervised training methods and six

关键词： Topology

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Secure and Privacy-preserving Data-sharing Framework based on Blockchain Technology for Al-Najaf/Iraq Oil Refinery 19

Secure and Privacy-preserving Data-sharing Framework based o...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Umran, Samir M. Lu, SongFeng Abduljabbar, Zaid Ameen Lu, Zhi Feng, Bingyan Zheng, Lu Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Iraqi Cement State Company Ministry of Industry and Minerals Baghdad10011 Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China University of Basrah College of Education for Pure Sciences Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Industrial Internet Research Institute Wuhan Huazhong Numerical Control Co. Ltd Wuhan430074 China South-Central University for Nationalities College of Computer Science Wuhan430074 China

ISBN: (纸本)9798350346558

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network;provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality. © 2022 IEEE.

关键词： Digital storage

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Ranking on Heterogeneous Manifold for Multimodal Information Retrieval

Ranking on Heterogeneous Manifold for Multimodal Information...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Zhenkun Jin Xingshi Wan Xin Nie Xinlei Zhou Yuanyuan Yi Gefei Zhou Wuhan Business University Wuhan Research Institute of Posts and Telecommunications Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan China School of Journalism and Information Communication Huazhong University of Science and Technology Wuhan China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China

In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZSCMR) of unlabeled multi-modal data has emerged as a prominent research topic. Early models utilized generative adversarial networks and class embeddings to assist in building a common feature space. However, this approach resulted in a loss of reliability in the feature distributions of individual modalities. In this paper, we propose a method called Ranking on Heterogeneous Manifold (RHM) to address the aforementioned issue. Our model comprises three feature extractors, each dedicated to extracting features from text, images and multimodal data. Subsequently, the relationship structures derived from these feature extractors are employed to construct a heterogeneous manifold. A diffusion process is then conducted on this heterogeneous manifold, ultimately yielding the final ranking scores. Comprehensive experiments demonstrate that the proposed modal has the capability to acquire more effective knowledge representation, thus enhancing the effectiveness of ZSCMR.

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FedCSA: Boosting the Convergence Speed of Federated Unlearning under Data Heterogeneity

FedCSA: Boosting the Convergence Speed of Federated Unlearni...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Zhen Wang Daniyal M Alghazzawi Li Cheng Gaoyang Liu Chen Wang Zeng Cheng Yang Yang Key Laboratory of Intelligent Sensing System and Security (Ministry of Education) School of Artificial Intelligence Hubei University Wuhan China King Abdulaziz University Jeddah Saudi Arabia Huazhong University of Science and Technology Wuhan China

The exponential growth of the Internet and the widespread availability of personal data have raised significant concerns regarding personal privacy and societal security. In response, the “right to be forgotten” has emerged as a fundamental principle, empowering individuals to request the removal of their personal data. Though relieving the privacy concerns at a certain degree by doing so, Artificial Intelligence (AI) models, trained by personal data, still suffers from privacy leakage risks. Therefore, machine unlearning attracts great attention from both industry and academia, aiming at erasing the risks of privacy leakage of AI models. However, existing machine unlearning methods primarily operate within centralized frame-works, limiting their applicability to decentralized Federated Learning (FL) settings where data is decentralized and not directly accessible. Additionally, current federated unlearning methods only approximate data deletion, leaving a potential risk of data leakage. To address these issues, this paper presents FedCSA, The FedCSA approach adopts a divide-and-conquer strategy by clustering and slicing the users’ data. Subsequently, submodels are trained on each individual slice, and their aggregation yields the global model. During the unlearning task, only the corresponding sub-model undergoes retraining, significantly reducing training time while ensuring data privacy and achieving precise data deletion. Extensive experiments are conducted on real-world datasets, including MNIST, Fashion-MNIST, and CIFAR-10, to evaluate the performance of FedCSA. The results demonstrate that FedCSA outperforms state-of-the-art machine learning methods, such as retrain and SISA, in terms of efficiency and global model performance. Our code is publicly available at https://***/ZhenWang9/FedCSA.

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Anomalous Hall effects in magnetic weak topological insulator films

arXiv

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arXiv 2024年

作者： Chen, Rui Yi, Xiao-Xia Zhou, Bin Xu, Dong-Hui Department of Physics Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security of Ministry of Education Hubei University Wuhan430062 China Department of Physics Chongqing Key Laboratory for Strongly Coupled Physics Chongqing University Chongqing400044 China Center of Quantum Materials and Devices Chongqing University Chongqing400044 China

The interplay between magnetism and strong topological insulator gives rise to distinct new topological phases and various intriguing phenomena, attracting significant attention in recent years. However, magnetic effects in weak topological insulators remain largely unexplored. In this work, we systematically investigate the magnetic effect on thin films of weak topological insulators. We focus on ferromagnetic and antiferromagnetic effects, which have been extensively studied in strong topological insulators, as well as the recently highlighted altermagnetic effect. We reveal that the interplay between magnetism and weak topological insulators leads to a variety of Hall effects in the absence of an external magnetic field, including the metallic quantum anomalous Hall effect without chiral edge states, the quantum anomalous Hall effect with a higher Hall conductance plateau, the quantized layer Hall effect, the metallic half-quantized valley-like Hall effect, and a quantized valley-like Hall effect. This work provides valuable insights for exploring magnetic effect on weak topological insulators. © 2024, CC BY.

关键词： Topological insulators

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Simulation of Propagation Pattern of Partial Discharge's Ultrasonic Signal in Oil-Immersed Transformer

Simulation of Propagation Pattern of Partial Discharge's Ult...

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2021 International Conference on Advanced Electrical Equipment and Reliable Operation, AEERO 2021

作者： Qian, Guochao Peng, Qingjun Wang, Zhongchang Zou, Yuepei Lei, Jiali Zhang, Zhixian Wu, Kejie Song, Yuxuan Electric Power Research Institute of Yunnan Power Grid Corporation Yunnan Kunming China Three Gorges Water Conservancy Project Cascade Dispatching Communication Center Hubei Yichang China Kunming Power Supply Bureau of Yunnan Power Grid Co. Ltd Yunnan Kunming China Chongqing University State Key Laboratory of Power Transmission Equipment System Security and New Technology Chongqing China

ISBN: (纸本)9781665402644

The breakdown of insulation caused by partial discharge inside the transformer is an important cause of transformer failure. Ultrasonic detection is a common method for detecting and positioning partial discharge in transformers. According to the needs of rationally arranging ultrasonic sensors and optimizing the positioning algorithm, this paper carries out a simulation analysis of the propagation pattern of partial discharge ultrasonic signals inside the oil-immersed transformer. Based on the finite element simulation software, an oil-immersed transformer model was built, and different geometric structures and material parameters were designed for the transformer's box, iron core, winding, and oil. The propagation mode of the partial discharge occurring in the oil passage between the high and low voltage windings, the high voltage winding and the outside of the winding in the transformer is analyzed, and the attenuation of the partial discharge ultrasonic signal in the transformer by the winding is studied. This article provides a certain reference for the layout and optimization of built-in ultrasonic sensors in oil-immersed power transformers. © 2021 IEEE.

关键词： Partial discharges

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Disentangling Client Contributions: Improving Federated Learning Accuracy in the Presence of Heterogeneous Data

Disentangling Client Contributions: Improving Federated Lear...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Chunming Liu Daniyal M Alghazzawi Li Cheng Gaoyang Liu Chen Wang Cheng Zeng Yang Yang Key Laboratory of Intelligent Sensing System and Security (Ministry of Education) School of Artificial Intelligence Hubei University Wuhan China King Abdulaziz University Jeddah Saudi Arabia Huazhong University of Science and Technology Wuhan China

Federated Learning (FL) is a promising paradigm that leverages distributed data sources to train machine learning models, thereby offering significant privacy advantages. However, the inherent statistical heterogeneity among clients, characterized by distinct data distributions and varying model performance, poses a substantial challenge. Such heterogeneity adversely affects both the convergence and overall performance of the global model within the FL framework. To address this issue, we introduce a novel FL algorithm, FedVa, which considers both the local data volume and model accuracy to determine client contributions, assigning respective weights. This consideration facilitates greater engagement from key clients during the aggregation process, thereby enhancing their influence on the global model refinement. Experimental results indicate that FedVa surpasses prevailing methods in managing statistical heterogeneity and enhancing global model accuracy, without incurring additional communication costs. Specifically, FedVa achieves a convergence speed 3.9 times faster than FedAvg on the MNIST dataset, and on the CIFAR-10 dataset, it results in a 2.3% increase in final model accuracy compared to FedAvg. Our code is publicly available at https://***/ChunmingLiu23/FedVa.

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Contrastive Learning-Based Generic Audio-to-Lyrics Alignment

Contrastive Learning-Based Generic Audio-to-Lyrics Alignment

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Zhenkun Jin Shuai Huang Xin Nie Xinlei Zhou Yuanyuan Yi Gefei Zhou Wuhan Business UIniversity Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Hubei Key Laboratory of Distributed System Security Wuhan China Hubei Engineering Research Center on BigData Security Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Journalism and Information Communication Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

In this research, we tackled the complex task of aligning audio and lyrics content automatically. This task entails the precise matching of lyrics with the corresponding audio segments in songs, necessitating the coordination of two distinct modalities: text and audio. To surmount this challenge, we employed a contrastive learning approach that generates cross-modal embeddings capable of establishing connections between the audio and text domains. Furthermore, we adopted a hierarchical and cascading design. Initially, the system predicts synchronization at the sentence level and subsequently refines the alignment at the word level. This design equips the system to handle extended sequences effectively, as cross-correlations leverage secondary memory that corresponds to sequence length. Through this innovative approach, we developed a trainable end-to-end system that can harness weakly annotated training data, concurrently learning a robust text model optimized for alignment tasks. Our experiments demonstrated substantial enhancements in average alignment accuracy when utilizing our proposed system, underscoring its robustness compared to previous state-of-the-art models.

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Theoretical study of the interaction of SF6 molecule on Ti3C2Tx surfaces

SSRN

引用

SSRN 2022年

作者： Zeng, Fuping Guo, Xinnuo Feng, Xiaoxuan Cai, Rijian Yao, Qiang Zhang, Shiling Tang, Ju School of Electrical Engineering and Automation Wuhan University Wuhan430072 China Hubei Key Laboratory of Power Equipment & System Security for Integrated Energy Resources Wuhan430072 China State Grid Chongqing Electric Power Company Electric Power Research Institute Chongqing401123 China State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing400044 China

At present, searching for efficient methods to degrade SF6 is a worldwide hotspot. Due to the abundant active groups on the surface, MXene is regarded as an ideal catalyst support. In this paper, the potential active sites of Ti3C2Tx (T=OH, F, O) for SF6 catalytic degradation were studied using DFT method. The results show the adsorption of SF6 on Ti3C2(OH)2 is significantly stronger than that on Ti3C2F2 and Ti3C2O2. Among typical adsorption sites on Ti3C2(OH)2, SF6 exhibited the strongest interaction at OH-1 site. Complex electron orbital interactions occurred between SF6 and Ti3C2(OH)2. The adsorption energy reached -6.739eV and about 1.414e transferred from Ti3C2(OH)2 to SF6. Structure of SF6 changed obviously and chemical adsorption occurred in this process. However, on Ti3C2F2 and Ti3C2O2, adsorption energy and electron transfer were small and the structure had little change, which was mainly physical adsorption. Compared with common catalysts such as Ag, α‑Al2O3 and BaTiO3, Ti3C2(OH)2 showed better catalytic activity for SF6 degradation. By strong adsorption of the active site on the surface, the degradation products were separated from each other, which weakened SF6 recombination and promoted the further degradation. The results provide theoretical support for MXene acting as a catalyst to efficiently degrade SF6 © 2022, The Authors. All rights reserved.

关键词： Sulfur hexafluoride

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Downstream-agnostic Adversarial Examples

arXiv

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arXiv 2023年

作者： Zhou, Ziqi Hu, Shengshan Zhao, Ruizhi Wang, Qian Zhang, Leo Yu Hou, Junhui Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Wuhan University China School of Information and Communication Technology Griffith University Australia Department of Computer Science City University of Hong Kong Hong Kong 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

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 use. In 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://github. com/CGCL-codes/AdvEncoder. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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