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Artificial Intelligence of Things (AIoT), IEEE Annual Congress on

作者： Yujie Chen Zaiqi Huang Xudong Cao Xuejun Li Yuqing Zhang HangZhou Institute of Technology Xidian University Xi’an China National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences Beijing China School of Cyber Engineering Xidian University Xi’an China

ISBN: (数字)9798350392296

ISBN: (纸本)9798350392302

Security vulnerabilities are an important part of the cybersecurity field. These vulnerabilities are dispersed across numerous websites and forums, exhibiting multi-source heterogeneity and data redundancy, and showing a significant growth trend. This presents a challenge for the collection, sharing, and analysis of vulnerability information. To address this issue, this paper presents a novel knowledge graph-based vulnerability platform. The platform fuses the vulnerability data collected by crawlers through a knowledge graph, obtaining 1.72 times more vulnerability data than NVD, 2.05 times more than CNVD, and 4.69 times more than NSFocus. This reflects the effectiveness of knowledge graph in fusing heterogeneous vulnerability data. Furthermore, the vulnerability platform provides data services that are easily queryable and retrievable, thus facilitating the early warning and repair of security vulnerabilities.

关键词： Fuses Redundancy Crawlers Knowledge graphs Maintenance engineering Market research Internet of Things

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Control of Magnetization Precession Frequency Using Current Density of Spin-Orbit Torque Applied to Local Area

Control of Magnetization Precession Frequency Using Current ...

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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Koda, Tetsunori Nakagawa, Ao Muroga, Sho Endo, Yasushi Oshima College National Institute of Technology Department of Electronic Mechanical Engineering Suo-Oshima742-2193 Japan Akita University Graduate School of Engineering Science Department of Mathematical Science and Electrical-Electronic-Computer Engineering Akita010-8502 Japan Department of Electrical Engineering Graduate School of Engineering Tohoku University Sendai980-8579 Japan Center for Spintronics Research Network Tohoku University Sendai980-8577 Japan Organization for Advanced Studies Tohoku University Sendai980-8577 Japan

ISBN: (纸本)9798350338362

We numerically study current density dependence of magnetization dynamics in a local area induced by the spin-orbit torque as a source of spin waves. The precession frequency of magnetization dynamics shows large change at a critical current density, which controls the wave length of spin waves. The origin of the change is due to the increase of exchange field caused by the magnetization torsion at the interface between SOT-applied and non-applied regions. Our new finding gives a way to manipulate spin wave's properties by controlling the magnetization direction in a local area. © 2023 IEEE.

关键词： Spin waves

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IQ-VFI: Implicit Quadratic Motion Estimation for Video Frame Interpolation

IQ-VFI: Implicit Quadratic Motion Estimation for Video Frame...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Mengshun Hu Kui Jiang Zhihang Zhong Zheng Wang Yinqiang Zheng National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Harbin Institute of Technology Shanghai Artificial Intelligence Laboratory The University of Tokyo

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Advanced video frame interpolation (VFI) algorithms approximate intermediate motions between two input frames to synthesize intermediate frame. However, they struggle to handle complex scenarios with curvilinear motions since they overlook the latent acceleration information between the input frames. Moreover, the supervision of predicted motions is tricky because ground-truth motions are not available. To this end, we propose a novel frame-work for implicit quadratic video frame interpolation (IQ-VFI), which explores latent acceleration information and accurate intermediate motions via knowledge distillation. Specifically, the proposed IQ-VFI consists of an implicit acceleration estimation network (IANet) and a VFI back-bone, the former fully leverages spatio-temporal information to explore latent acceleration priors between two input frames, which is then used to progressively modulate linear motions from the latter into quadratic motions in coarse-to-fine manner. Furthermore, to encourage both components to distill more acceleration and motion cues oriented towards VFI, we propose a knowledge distillation strategy in which implicit acceleration distillation loss and implicit motion distillation loss are employed to adaptively guide latent acceleration priors and intermediate motions learning, respectively. Extensive experiments show that our proposed IQ-VFI can achieve state-of-the-art performances on various benchmark datasets.

关键词： Interpolation computer vision Accuracy Motion estimation Computational modeling Estimation Benchmark testing

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A Website Fingerprinting Attack Based on Parameter Prediction Model for Conceptual Drift

A Website Fingerprinting Attack Based on Parameter Predictio...

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Software System and Information Processing (ICSSIP), IEEE International Conference on

作者： Jian Cui Taizhong Xu Li Zhao Shuai Han College of Computer Science and Technology Harbin Engineering University Harbin China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China School of Electrical and Information Engineering Yunnan Minzu University Yunnan Key Laboratory of Unmanned Autonomous System Kunming China

ISBN: (数字)9798331506131

ISBN: (纸本)9798331506148

Website content updates and changes in the network environment can lead to concept drift, significantly impacting the performance of existing website fingerprinting methods. Recent research has focused on transfer learning-based approaches for concept drift adaptation to address this issue with limited samples. These methods involve pre-training a robust feature extractor and fine-tuning the classifier with a few samples. However, the effectiveness of these classifiers is highly dependent on the distributional gap between pre-training and fine-tuning datasets. As the divergence increases, more fine-tuning data is required to maintain classification performance. Consequently, existing methods struggle to achieve effective website identification tasks under severe concept drift with limited tuning data. To overcome these limitations, we propose a model parameter prediction approach, which adapts to new domains from data and model perspectives to enhance website identification performance. The proposed method predicts future model parameters to mitigate the effects of distributional changes, effectively adapting to evolving network environments. Experimental results demonstrate that our approach achieves significant improvements in accuracy, particularly under scenarios with limited tuning data and substantial concept drift.

关键词： Adaptation models Accuracy Concept drift Predictive models Fingerprint recognition Feature extraction Data models Robustness Long short term memory Tuning

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Cerebrovascular segmentation from mesoscopic optical images using Swin Transformer

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Journal of Innovative Optical Health Sciences 2023年第4期16卷 120-133页

作者： Yuxin Li Qianlong Zhang Hang Zhou Junhuai Li Xiangning Li Anan Li Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Xi'an 710048P.R.China School of Computer Science Chengdu University of Information Technology Chengdu 610225P.R.China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics MoE Key Laboratory for Biomedical Photonics Huazhong University of Science and Technology Wuhan 430074 P.R.China HUST-Suzhou Institute for Brainsmatics Suzhou 215123 P.R.China

Vascular segmentation is a crucial task in biomedical image processing,which is significant for analyzing and modeling vascular networks under physiological and pathological *** advances in fluorescent labeling and mesoscopic optical techniques,it has become possible to map the whole-mouse-brain vascular networks at capillary ***,segmenting vessels from mesoscopic optical images is a challenging *** problems,such as vascular signal discontinuities,vessel lumens,and background fluorescence signals in mesoscopic optical images,belong to global semantic information during vascular *** vascular segmentation methods based on convolutional neural networks(CNNs)have been limited by their insufficient receptive fields,making it challenging to capture global semantic information of vessels and resulting in inaccurate segmentation ***,we propose SegVesseler,a vascular segmentation method based on Swin *** adopts 3D Swin Transformer blocks to extract global contextual information in 3D *** approach is able to maintain the connectivity and topology of blood vessels during *** evaluated the performance of our method on mouse cerebrovascular datasets generated from three different labeling and imaging *** experimental results demonstrate that the segmentation effect of our method is significantly better than traditional CNNs and achieves state-of-the-art performance.

关键词： Vascular segmentation Swin Transformer mesoscopic optical imaging fMOST

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6G-Enabled Edge AI for Metaverse:Challenges, Methods,and Future Research Directions

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Journal of Communications and Information networks 2022年第2期7卷 107-121页

作者： Luyi Chang Zhe Zhang Pei Li Shan Xi Wei Guo Yukang Shen Zehui Xiong Jiawen Kang Dusit Niyato Xiuquan Qiao Yi Wu School of Data Science and Technology Heilongjiang UniversityHarbin 150080China Institute for Cryptology and Network Security Heilongjiang UniversityHarbin 150080China SenseTime Group Limited Shenzhen 518000China Pillar of Information Systems Technology and Design Singapore University of Technology and Design.Singapore 487372Singapore School of Automation.Guangdong University of Technology and Center for Intelligenl Batch Manufacturing based on IoT TechnologyGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798.Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications.Beijing 100876China

Sixth generation(6G)enabled edge intelligence opens up a new era of Internet of everything and makes it possible to interconnect people-devices-cloud anytime,*** and more next-generation wireless network smart service applications are changing our way of life and improving our quality of *** the hottest new form of next-generation Internet applications,Metaverse is striving to connect billions of users and create a shared world where virtual and reality ***,limited by resources,computing power,and sensory devices,Metaverse is still far from realizing its full vision of immersion,materialization,and *** this end,this survey aims to realize this vision through the organic integration of 6G-enabled edge artificial intelligence(AI)and ***,we first introduce three new types of edge-Metaverse architectures that use 6G-enabled edge AI to solve resource and computing constraints in *** we summarize technical challenges that these architectures face in Metaverse and the existing ***,we explore how the edge-Metaverse architecture technology helps Metaverse to interact and share digital ***,we discuss future research directions to realize the true vision of Metaverse with 6G-enabled edge AI.

关键词： edge artificial intelligence artificial intelli-gence 6G metaverse federated learning

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BLDTS: Blockchain-based Lightweight Data Trusted Sharing Scheme for Internet of Vehicles

BLDTS: Blockchain-based Lightweight Data Trusted Sharing Sch...

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International Conference on computer Supported Cooperative Work in Design

作者： Haoran Lin Zhongyuan Yu Guijuan Wang Anming Dong Xiang Tian School of Computer Science Qufu Normal University Rizhao China School of Information Science and Engineering Lanzhou University Lanzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Ensuring safe and reliable data sharing is crucial for the development of Internet of Vehicles (IoV) technology. To provide a trusted data environment for IoV and enable traditional consensus algorithms to meet the high dynamic requirements of the IoV. In this article, we propose a blockchain-based data sharing scheme for IoV (BLDTS) to achieve secure and trusted sharing. First, we design a false information identification strategy that utilizes a bayesian inference model to determine the authenticity of shared data with the assistance of reputation value. Second, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to construct a novel lightweight consensus mechanism based on vehicle reputation values and traffic environment factors, and the nodes with high scores were selected as participants in the consensus, which can reduce the computational overhead. Finally, experimental results show that our scheme has advantages in improving the accuracy of false message identification and consensus efficiency.

关键词： Data privacy Federated learning Heuristic algorithms Bayes methods Consensus protocol Reliability Vehicle dynamics Protection Fake news Internet of Vehicles

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Decrease the Prompt Uncertainty: Adversarial Prompt Learning for Few-shot Text Classification

Decrease the Prompt Uncertainty: Adversarial Prompt Learning...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jinta Weng Zhaoguang Zhang Jing Yaqi Chenxu Niu Heyan Huang Yue Hu School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Computer Science and Cyber Engineering Guangzhou University China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing Institute of Technology School of Computer Science and Technology Beijing China Southeast Academy of Information Technology Putian China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

With few-shot learning abilities, pre-trained language models (PLMs) have achieved remarkable success in classification tasks. However, recent studies have shown that the performance of PLM is vulnerable due to different prompts and the instability of the prompt-based learning process. To address this challenge, we explore appropriate perturbation addition of adversarial training and integrate the global knowledge of the full-parameter fine-tuned pre-trained language model(PLM). Specifically, we propose an adversarial prompt learning model (ATPET) and ATPET with fine-tuning(ATPET-FT), incorporating ATPET with fine-tuning knowledge into the prompt learning process. Through extensive experiments on several few-shot classification tasks and challenging data settings, we demonstrate that our methods consistently improve the robustness while maintaining the effectiveness of PLMs.

关键词： Training Uncertainty Perturbation methods Text categorization Semantics Robustness Few shot learning Cybernetics

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Provably Secure Authenticated Key-Management Mechanism for e-Healthcare Environment

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IEEE Internet of Things Journal 2025年

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Ghaffar, Zahid Xie, Yong Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan Qinghai Institute of Technology School of Computer and Information Science China Qinghai Provincial Laboratory for Intelligent Computing and Application China Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan250353 China

The Internet of Things (IoT) is rapidly permeating all aspects of human life, involving a network of devices that share sensitive data. A notable application is the e-healthcare systems, which employ connected sensors, medical servers, and wearable devices. However, the public nature of communication in e-healthcare systems poses challenges such as security, privacy, and authentication of participating entities. Recently, many authentication protocols have been introduced to address these challenges. However, most of these protocols remain vulnerable to various security attacks, including device or medical server impersonation, denial of service, physical or cloning, and de-synchronization attacks. Therefore, we introduce an authenticated key-management protocol utilizing hash functions and Cipher-Block Chaining-Advanced Encryption Standard encryption (CBC-AES) encryption. The proposed protocol also employs the Physical Unclonable Function (PUF), which makes it more robust and efficient in resisting physical or cloning attacks. Additionally, the proposed scheme resists various security threats, including impersonation, session key leakage, ephemeral secret leakage, and de-synchronization attacks. We analyze the scheme’s security and reliability through formal and informal analysis. The informal analysis demonstrates that the scheme encompasses crucial security features, while the formal analysis substantiates. Moreover, performance analysis of the proposed protocol with various competing results indicates that our protocol achieves an average reduction in communication and computation overheads by 36.03.% and 41.79%, respectively. © 2014 IEEE.

关键词： Electronic health record

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Data-consistent Unsupervised Diffusion Model for Metal Artifact Reduction

Data-consistent Unsupervised Diffusion Model for Metal Artif...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tong, Zhan Wu, Zhan Yang, Yang Mao, Weilong Wang, Shijie Li, Yinsheng Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Chinese Academy of Sciences Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Shenzhen518055 China School of Computer Science and Engineering Key Lab. of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology Nanjing210096 China

ISBN: (纸本)9798350337488

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly available dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics. © 2023 IEEE.

关键词： computed tomography deep unsupervised learning denoising diffusion probabilistic model Metal artifact reduction physics-based consistency constraint

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