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7th International Conference on Attacks and Defenses for Internet-of-Things, ADIoT 2024

作者： Chen, Xinyong Chen, Jiageng Luo, Jinquan Liu, Hongwei School of Computer Science and Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Hubei Wuhan430079 China School of Mathematics and Statistics Central China Normal University Hubei Wuhan430079 China

ISBN: (纸本)9783031855924

Vehicular Ad Hoc Networks (VANETs) are crucial for intelligent transportation systems (ITS), yet they face significant security and privacy challenges. The open wireless environment makes VANETs vulnerable to attacks, and existing authentication protocols often have limitations, such as susceptibility to quantum threats or high communication overhead. In this paper, we propose an effective privacy-preserving authentication protocol using lattice-based group signatures for VANETs. Our protocol ensures quantum security and provides robust security features. It not only ensures privacy protection for vehicles but also enables efficient tracing of malicious signers when necessary. In addition, we show that the proposed protocol features small and fixed public key and signature sizes, independent of the number of members, and incurs low communication overhead. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Authentication Protocol

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PolypSegDiff: Dynamic Multi-scale Conditional Diffusion Model for Polyp Segmentation 27th

PolypSegDiff: Dynamic Multi-scale Conditional Diffusion Mode...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Du, Xiaogang Jiao, Yipeng Lei, Tao Zhang, Xuejun Wang, Yingbo Nandi, Asoke K. Shaanxi Joint Laboratory of Artificial Intelligence Shaanxi University of Science and Technology Xi’an China School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China Department of Electronic and Computer Engineering Brunel University London London United Kingdom

ISBN: (纸本)9783031801358

Diffusion models have demonstrated impressive potential in semantic segmentation tasks. However, these models cannot accurately segment hidden polyps with complex structures owing to the absence of multi-scale conditional features for guiding the reverse process of diffusion models. To address this issue, we propose a dynamic multi-scale conditional diffusion model (PolypSegDiff). First, we design a dynamic multi-scale integration module to fuse the noise segmentation mask and the original image, dynamically extract multi-scale conditional features, and strengthen the network’s ability of identifying polyp areas. Second, we design a hierarchical feature enhancement module to extract and combine image features at different levels. This module significantly enriches the semantic diversity of conditional features, enabling the denoising network to more accurately understand the semantic relationships between polyps and the surrounding normal tissues. Experimental results across five publicly available polyp segmentation datasets demonstrate that PolypSegDiff outperforms existing popular methods in segmentation accuracy, achieving outstanding performance and robust generalization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Physically Secure and Privacy-Preserving Authentication Scheme for VANET

Physically Secure and Privacy-Preserving Authentication Sche...

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2024 International Conference on artificial intelligence of Things and Systems, AIoTSys 2024

作者： Chen, Dongmei Liang, Yangfan Li, Lihui Liu, Yining School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou China

ISBN: (纸本)9798331507886

The communication between vehicles and other entities occurs over open wireless networks, making them susceptible to various security attacks In Vehicular Ad Hoc Network (VANET). Consequently, many researchers have proposed numerous data transmission and key exchange protocols to ensure secure communication within VANET. The keys and sensitive information involved in these protocols need to be stored long-Term in the tamper-resistant devices of vehicles. However, due to the limited resources of these devices, many encryption schemes are difficult to embed. Even if effective encryption schemes can be embedded in VANET, the long-Term storage of data in vehicles still carries a significant risk of being attacked. To tackle this problem, we propose an authentication protocol designed for VANET that emphasizes both physical security and privacy protection. The protocol primarily leverages the properties of Physical Unclonable Functions to achieve enhanced security. This approach eliminates the need for vehicles to store keys and sensitive messages long-Term, reducing the storage burden on vehicles while enhancing the system's security and efficiency. Detailed security analyses demonstrate that our proposed scheme achieves the goals of physical security and privacy protection. A comparison with similar approaches demonstrates that our solution outperforms them in both time efficiency and communication overhead. © 2024 IEEE.

关键词： Authentication Protocol

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Linguistic feature template integration for Chinese-Vietnamese neural machine translation

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Frontiers of computer science 2022年第4期16卷 223-225页

作者： Zhiqiang YU Yantuan XIAN Zhengtao YU Yuxin HUANG Junjun GUO Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China School of Mathematics and Computer Science Yunnan Minzu UniversityKunming 650500China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming 650500China

1 Introduction and main contributions Template-based approaches have achieved significant progress in low-resource neural machine translation(NMT)recently[1],such as the efficient works,NMT-GTM[2],SoftPrototype[3],***,most previous works only retrieve target sentence as template to generate translation,neglecting the utilization of linguistic feature that contained in the source sentence and template.

关键词： translation template such

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NEAT-Net: An Unsupervised Cross-View Prior Inpainting Network for CBCT Metal Artifact Reduction 46

NEAT-Net: An Unsupervised Cross-View Prior Inpainting Networ...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Yang, Yang Tong, Zhan Zhong, Xinyun Shi, Yijie Xi, Yan Yu, Wenxue Wu, Zhan Chen, Yang First-Imaging Tech Shanghai China Southeast University Laboratory of Image Science and Technology The School of Computer Science and Engineering Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

ISBN: (纸本)9798350371499

Intraoperative Cone-Beam Computed Tomography (CBCT) facilitates intraoperative navigation for Minimally Invasive Spine Surgery (MISS). However, high-attenuation metal implants used in MISS often cause metal artifacts in the reconstructed CBCT images. Current algorithms do not consider the cross-view information in the projection-domain for metal artifact reduction (MAR). Inaccurate projection-domain inpainting results in CBCT MAR lead to tissue blurring and secondary artifacts, significantly compromising the accuracy of CBCT-guided MISS and increasing surgical risks. To address the above challenge, in this paper, we propose a novel unsupervised cross-view prior inpainting network for CBCT Metal Artifact Reduction named NEAT-Net. Firstly, a cross-view prior multi-scale inpainting module is constructed to learn the inter-view complementary information. Secondly, a hybrid feature attention module is proposed to adaptively fuse cross-view features. In addition, an unsupervised training approach is proposed to directly learn from metal-affected data. Extensive experiments are conducted to verify the effectiveness of our algorithm on a real clinical dataset. © 2024 IEEE.

关键词： computerized tomography

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Gaussian Function Representation of 2nd-Order Normal Cloud Model 18

Gaussian Function Representation of 2nd-Order Normal Cloud M...

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18th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2023

作者： Li, Min Wang, Guoyin Yu, Zeng Wang, Hongjun Wan, Jihong Li, Tianrui Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu China Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Computational Intelligence Chongqing China Guangdong University of Technology School of Computer Science and Technology Guangdong China

ISBN: (纸本)9798350318401

Natural language is a fruit of human cognition, and its basic units are linguistic terms. Linguistic terms' representation and processing have played a fundamental role in applications such as risk assessment. Cloud Model, which integrates the approaches of probability theory and fuzzy sets, is a model for expressing the uncertainties of linguistic terms. Its advantage is that it is a bidirectional cognitive model. The 2nd-order normal cloud (2Ord-NC) is a specific Cloud Model. It can represent the linguistic concept's intension using three statistics parameters (Expectation Ex, Entropy En, and Hyper-entropy He) and its extension using cloud drops (instances). A cognitive transformation between a qualitative concept and its quantitative instances can be implemented by probabilistic programming (FCT and BCT). In the current works, Upper and Lower membership functions characterize Cloud Model-s. However, scholars have yet to explain this representation method's rationality theoretically. This paper proposes the 2nd-order normal cloud model's Gaussian function representation. Firstly, it is proved that the 2nd-order normal cloud model (2Ord-NCM) can be determined by its outer envelope and expectation membership functions. The properties of the 2Ord-NCM's fuzzy membership functions are analyzed when the concept is clear and unclear, and the definitions of inner envelope membership function family, outer envelope membership function family, and representative membership function family are given. Secondly, it is proved that the 2Ord-NCM can also be determined by any two of its representative membership functions. This work enriches the Cloud Model's mathematical theoretical foundations, thereby better guiding its practical application. © 2023 IEEE.

关键词： Membership functions

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Relational learning in pre-trained models: a theory from hypergraph recovery perspective 24

Relational learning in pre-trained models: a theory from hyp...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yang Chen Cong Fang Zhouchen Lin Bing Liu National Key Lab of General Artificial Intelligence School of Intelligence Science and Technology Peking University National Key Lab of General Artificial Intelligence School of Intelligence Science and Technology and Institute for Artificial Intelligence Peking University National Key Lab of General Artificial Intelligence School of Intelligence Science and Technology and Institute for Artificial Intelligence Peking University and Pazhou Laboratory (Huangpu) Guangzhou China Department of Computer Science University of Illinois Chicago

Foundation Models (FMs) have demonstrated remarkable insights into the relational dynamics of the world, leading to the crucial question: how do these models acquire an understanding of world hybrid relations? Traditional statistical learning, particularly for prediction problems, may overlook the rich and inherently structured information from the data, especially regarding the relationships between objects. We introduce a mathematical model that formalizes relational learning as hypergraph recovery to study pre-training of FMs. In our framework, the world is represented as a hypergraph, with data abstracted as random samples from hyperedges. We theoretically examine the feasibility of a Pre-Trained Model (PTM) to recover this hypergraph and analyze the data efficiency in a minimax near-optimal style. By integrating rich graph theories into the realm of PTMs, our mathematical framework offers powerful tools for an in-depth understanding of pretraining from a unique perspective and can be used under various scenarios. As an example, we extend the framework to entity alignment in multimodal learning.

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Synergistic Integration of Cross-Spatial Learning for Lightweight Crack Detection

Synergistic Integration of Cross-Spatial Learning for Lightw...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Senyao Li Jingling Yuan Huilin Zhu Xian Zhong Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan University of Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Efficient crack segmentation is crucial for engineering surface inspection, especially on edge devices where both accuracy and computational efficiency are essential. To address the challenges posed by crack directionality and blurred edges while enhancing performance, we propose a lightweight segmentation model, SCCU-Net, based on cross-spatial synergistic learning and coordinate awareness. The model integrates coordinate information and perceptual sets into a hybrid attention mechanism, significantly boosting segmentation accuracy. We introduce a Mapping Attention Gate (MAG), which utilizes gating signals from fine-grained features to guide cross-spatial learning, alongside an Adaptive Skip Fusion (ASF) to ensure smooth feature integration while optimizing computational resources. Extensive experiments on six benchmark datasets demonstrate that SCCU-Net consistently outperforms existing lightweight models, setting a new benchmark for crack detection on edge devices. Code is available at https://***/lsyyy20000830/SCCU-Net.

关键词： Performance evaluation Adaptation models Accuracy Sensitivity Computational modeling Logic gates Benchmark testing Signal processing Surface cracks Speech processing

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Avoid attacks: A Federated Data Sanitization Defense in IoMT Systems

Avoid attacks: A Federated Data Sanitization Defense in IoMT...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Chen, Chong Gao, Ying Huang, Siquan Yan, Xingfu School of Computer Science and Technology South China University of Technology Guangzhou China Guangdong Prov. Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application China School of Computer Science South China Normal University Guangzhou China

ISBN: (纸本)9781665494274

Malicious falsification of medical data destroys the training process of the medical-aided diagnosis models and causes serious damage to Healthcare IoMT Systems. To solve this unsupervised problem, this paper finds a robust data filtering method for various data poisoning attacks. First, we adapt the federated learning framework to project all of the clients' data features into the public subspace domain, allowing unified feature mapping to be established while their data remains stored locally. Then we adopt the federated clustering to re-group their features to clarify the poisoned data. The federated clustering is based on the consistent association of data and its semantics. Finally, we do the data sanitization with a simple yet efficient strategy. Extensive experiments are conducted to evaluate the accuracy and efficacy of the proposed defense method against data poisoning attacks. © 2023 IEEE.

关键词： Semantics

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Breaking Modality Gap in RGBT Tracking: Coupled Knowledge Distillation 24

Breaking Modality Gap in RGBT Tracking: Coupled Knowledge Di...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Lu, Andong Zhao, Jiacong Li, Chenglong Xiao, Yun Luo, Bin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University AnHui HeFei China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Anhui HeFei China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Anhui HeFei China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Anhui HeFei China

ISBN: (纸本)9798400706868

Modality gap between RGB and thermal infrared (TIR) images is a crucial issue but often overlooked in existing RGBT tracking methods. It can be observed that modality gap mainly lies in the image style difference. In this work, we propose a novel Coupled Knowledge Distillation framework called CKD, which pursues common styles of different modalities to break modality gap, for high performance RGBT tracking. In particular, we introduce two student networks and employ the style distillation loss to make their style features consistent as much as possible. Through alleviating the style difference of two student networks, we can break modality gap of different modalities well. However, the distillation of style features might harm to the content representations of two modalities in student networks. To handle this issue, we take original RGB and TIR networks as the teachers, and distill their content knowledge into two student networks respectively by the style-content orthogonal feature decoupling scheme. We couple the above two distillation processes in an online optimization framework to form new feature representations of RGB and thermal modalities without modality gap. In addition, we design a masked modeling strategy and a multi-modal candidate token elimination strategy into CKD to improve tracking robustness and efficiency respectively. Extensive experiments on five standard RGBT tracking datasets validate the effectiveness of the proposed method against state-of-the-art methods while achieving the fastest tracking speed of 96.4 FPS. © 2024 ACM.

关键词： Students

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