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9th international Conference on Wireless Communication and Sensor Networks, icWCSN 2022

作者： Xu, Gencheng Jin, Shiyu Tang, Kaicheng Zhou, Yimin School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Cybersecurity Chengdu University of Information Technology China

ISBN: (纸本)9781450396219

With the development of multicore processor, parallel computation is adopted to accelerate computation for higher efficiency. In signature verification process, batch verification has a high efficiency due to its aggregate operation. However, when there exists a invalid signature, the verification turns out failure, which is time-consuming. This paper proposes an adaptive bitwise division-based searching (ABDS) algorithm to provide a solution to the challenge. When there is only one invalid signature, the time complexity of ABDS algorithm is O(n) = 2 in parallel computation, while that of binary search (BS) algorithm is 1 + log 2n. Compared with BS algorithm, ABDS algorithm has a significant advantage in parallel computation when there are a small number of invalid signatures. The work can be used in searching problem where verification time greatly exceeds aggregation time. © 2022 ACM.

关键词： Efficiency

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Resisting Over-Smoothing in Graph Neural Networks via Dual-Dimensional Decoupling 24

Resisting Over-Smoothing in Graph Neural Networks via Dual-D...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Shen, Wei Ye, Mang Huang, Wenke National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798400706868

Graph Neural Networks (GNNs) are widely employed to derive meaningful node representations from graphs. Despite their success, deep GNNs frequently grapple with the oversmoothing issue, where node representations become highly indistinguishable due to repeated aggregations. In this work, we consider the oversmoothing issue from two aspects of the node embedding space: dimension and instance. Specifically, while existing methods primarily concentrate on instance-level node relations to mitigate oversmoothing, we propose to mitigate oversmoothing at dimension level. We reveal the heightened information redundancy between dimensions which diminishes information diversity and impairs node differentiation in GNNs. Motivated by this insight, we propose the Dimension-Level Decoupling (DLD) to reduce dimension redundancy, enhancing dimensional-level node differentiation. Besides, at the instance level, the neglect of class differences leads to vague classification boundaries. Hence, we introduce the Instance-Level Class-Difference Decoupling (ICDD) that repels inter-class nodes and attracts intra-class nodes, improving the instance-level node discrimination with clear classification boundaries. Additionally, we introduce a novel evaluation metric that considers the impact of class differences on node distances, facilitating precise oversmoothing measurement. Extensive experiments demonstrate the effectiveness of our method Dual-Dimensional Class-Difference Decoupling (DDCD) across diverse scenarios. © 2024 ACM.

关键词： Graph neural networks

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Corrigendum to "Threat hunting for adversary impact inhibiting system recovery" [computers & Security, Volume 154, July 2025, 104464] (computers & Security (2025) 154, (S0167404825001531), (10.1016/***.2025.104464))

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computers and Security 2025年 156卷

作者： Alsharabi, Naif Bhardwaj, Akashdeep Ayaba, Abdulaziz Jadi, Amr College of Computer Science & Engineering University of Ha'il Ha'il Saudi Arabia Center for Cybersecurity School of Computer Science UPES Dehradun India

The authors regret an error in the Acknowledgment section of the article. The project number was incorrectly listed as: "This research has been funded by Scientific Research Deanship at University of Ha'il - Saudi Arabia through project number >." It should be: "This research has been funded by Scientific Research Deanship at University of Ha'il - Saudi Arabia through project number >." Only the correction needed to change RG-23 to the RG-24 We apologize for any inconvenience this may cause and appreciate your assistance in processing this correction. © 2025 Elsevier Ltd

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Empowering Visible-Infrared Person Re-Identification with Large Foundation Models 38

Empowering Visible-Infrared Person Re-Identification with La...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Zhangyi Yang, Bin Ye, Mang National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China

Visible-Infrared Person Re-identification (VI-ReID) is a challenging cross-modal retrieval task due to significant modality differences, primarily resulting from the absence of color information in the infrared modality. The development of large foundation models like Large Language Models (LLMs) and Vision Language Models (VLMs) motivates us to explore a feasible solution to empower VI-ReID with off-the-shelf large foundation models. To this end, we propose a novel Text-enhanced VI-ReID framework driven by Large Foundation Models (TVI-LFM). The core idea is to enrich the representation of the infrared modality with textual descriptions automatically generated by VLMs. Specifically, we incorporate a pre-trained VLM to extract textual features from texts generated by VLM and augmented by LLM, and incrementally fine-tune the text encoder to minimize the domain gap between generated texts and original visual modalities. Meanwhile, to enhance the infrared modality with extracted textual representations, we leverage modality alignment capabilities of VLMs and VLM-generated feature-level filters. This enables the text model to learn complementary features from the infrared modality, ensuring the semantic structural consistency between the fusion modality and the visible modality. Furthermore, we introduce modality joint learning to align features across all modalities, ensuring that textual features maintain stable semantic representation of overall pedestrian appearance during complementary information learning. Additionally, a modality ensemble retrieval strategy is proposed to leverage complementary strengths of each query modality to improve retrieval effectiveness and robustness. Extensive experiments on three expanded VI-ReID datasets demonstrate that our method significantly improves the retrieval performance, paving the way for the utilization of large foundation models in downstream multi-modal retrieval tasks. © 2024 Neural information processing systems foundat

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A secure quantum homomorphic encryption ciphertext retrieval scheme

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Soft Computing 2025年第3期29卷 1497-1509页

作者： Cheng, Zhen-Wen Chen, Xiu-Bo Xu, Gang Chang, Yan Miao, Li-Hua Yang, Yi-Xian Wang, Ya-Lan School of Data and Computer Science Shandong Women’s University Jinan250300 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Cybersecurity Chengdu University of Information Technology Chengdu610225 China Huawei Technologies Co. Ltd Shenzhen518129 China Department of Computer Science Faculty of Engineering and Physical Sciences University of Surrey Surrey GuildfordGU2 7XH United Kingdom

In a recent paper (Gong et al. Quantum Inf Process 19:3, 2020), a novel ciphertext retrieval scheme based on the Grover algorithm and quantum homomorphic encryption was presented. In this scheme, when the server performs the operation of marking the solution on the user’s encrypted state in the Grover iteration, it needs to remove many gate-errors generated in the homomorphic evaluation of the T gate. And the server could judge this specific solution from the quantum circuit of marking the solution. It makes this scheme unable to achieve the low-cost and secure ciphertext retrieval. Therefore, we improve the Gong et al.’s scheme and propose a secure quantum homomorphic encryption ciphertext retrieval scheme. In our scheme, the trusted third party is introduced to cooperate with the server to execute the Grover algorithm. In each Grover iteration, the trusted third party can quickly mark the solution on the plaintext state, encrypt the marked state, and transmit it to the server. Then the server performs the remaining operations of this Grover iteration on the encrypted state. The trusted third party finally decrypts the iterated state. This cooperative approach ensures that the number of auxiliary qubits required and extra quantum gates executed in our scheme are lower than the Gong et al.’s scheme. By analyzing the security of our scheme, we confirm that the server and the trusted third party will not be informed of this solution. Thus, our scheme realizes the secure ciphertext retrieval with low computational overhead. We utilize IBM’s Qiskit framework to simulate our scheme, and the experimental result shows that our scheme is correct. It is worth noting that the low-cost and secure ciphertext retrieval will play a crucial role in modern information security and privacy protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Ciphertext

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A novel image encryption algorithm based on new one-dimensional chaos and DNA coding

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Multimedia Tools and Applications 2024年第36期83卷 84275-84297页

作者： Feng, Sijia Zhao, Maochang Liu, Zhaobin Li, Yuanyu Tianjin public emergency early warning information release center Tianjin Meteorology Information Center Tianjin300074 China School of Information Science & amp Technology Dalian Maritime University Dalian116026 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

This paper introduces a new one-dimensional chaotic system and a new image encryption algorithm. Firstly, the new chaotic system is analyzed. The bifurcation diagram and Lyapunov exponent show that the system has strong chaotic characteristics and is suitable for the field of image encryption. The chaotic sequence generated by the system is used in image encryption, scrambled according to its sequence value, and several sequences required for DNA coding operation are generated. The encrypted image is obtained by encoding, decoding, and diffusion operation according to the sequence. The initial values and parameters of system are generated by Hash algorithm based on plain image, so it has strong sensitivity and can effectively resist attacks such as selective plaintext attack. Experimental results show that the algorithm has high security and robustness, and can resist common attacks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Chaotic systems

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PKEST: Public-Key Encryption With Similarity Test for Medical Consortia Cloud Computing

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IEEE Transactions on Cloud Computing 2025年第2期13卷 680-693页

作者： Chen, Junsong Zeng, Shengke Han, Song Yin, Jin Chen, Peng Xihua University School of Computer and Software Engineering Chengdu610039 China Hangzhou City University School of Computer and Computing Science Hangzhou310015 China Sichuan University West China Biomedical Big Data Center West China Hospital Chengdu610041 China

Cloud computing eliminates the limitations of local hardware architecture while also enabling rapid data sharing between healthcare institutions. Encryption of electronic medical records (EMRs) before uploading to cloud servers is necessary for privacy. However, encryption brings challenges for computation. Public Key Encryption with Equality Test (PKEET) allows cloud servers to test the underlying message equality without decryption. Therefore, it can be used to classify the encrypted EMRs corresponding to different medical symptoms. However, traditional PKEETs have limitations in testing the similarity between the ciphertexts. Undoubtedly, it can not handle EMR classification with similar medical symptoms efficiently. In this work, we propose a lightweight public key encryption with similarity test (PKEST) for the EMR classification shared in medical consortia. Our scheme can resist offline message recovery attacks, which may be launched by the insider manager, and the traditional paring computation is not necessary. Our experiment simulation shows that the similarity error between ciphertext and plaintext is tiny when the parameters are set properly. Compared to previous works, our scheme not only achieves the classification of similar encrypted EMRs but is also more efficient than traditional PKEETs since our construction does not need paring computation anymore. © 2013 IEEE.

关键词： Ciphertext

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Harmonizing space–time dynamics for precision in human action recognition

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Multimedia Tools and Applications 2025年第5期84卷 2805-2832页

作者： Majid, Abdul Wang, Yulin Ullah, Anwar Naiz, Fahim Zarar, Muhammad School of Computer Science Wuhan University Hubei Wuhan430072 China National Engineering Research Center for E-Leaning Central China Normal University Hubei Wuhan430072 China

Human action recognition (HAR) plays a vital role in various fields, including surveillance, healthcare, and human–computer interaction. Recognizing multi-actor actions in crowded scenarios poses a significant challenge due to the complex dynamics and interactions among individuals. Existing methods often fail to effectively capture both spatial and temporal dependencies in video frames, leading to inaccuracies in recognizing diverse action classes under real-world conditions. In this paper, we introduce a novel framework called Video Capsule Net-Multi Actor (VCN-MA), designed to enhance the precision of HAR by harmonizing space–time dynamics. The VCN-MA architecture incorporates a deep neural network combining convolutional and recurrent layers to capture spatial and temporal features. Additionally, we leverage pre-trained embeddings and attention mechanisms to focus on critical features during action sequences. The proposed model utilizes capsule networks with dynamic routing to represent complex hierarchical spatial–temporal relationships, further refining action recognition accuracy. We evaluate our approach using the MAMA dataset, which includes 35 diverse action classes under varied real-world conditions such as different viewpoints, occlusions, and lighting variations. The VCN-MA model achieves an overall accuracy of 85% on the test set, outperforming existing state-of-the-art methods. Visualizations, including confusion matrices and ROC curves, along with metrics like the Matthews Correlation Coefficient (MCC), provide a comprehensive analysis of the model's performance across different action classes. These results demonstrate the potential applicability of VCN-MA in practical scenarios, including video surveillance, human–computer interaction, and healthcare applications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Building Holistic Situation Awareness through Large Language Models

Building Holistic Situation Awareness through Large Language...

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Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2023

作者： McDaniel, Emma L. Ruvinsky, Alicia I. Georgia State University Department of Computer Science AtlantaGA United States US Army Engineer Research and Development Center Cybersecurity Engineering and Analysis Branch VicksburgMS United States

In order to design a system to support a users’ situation awareness as they navigate and execute in a complex domain, the information landscape for the domain must be defined. Such a landscape becomes critical for recognizing and evaluating deficiencies in a user’s cognitive processing of the information landscape, and as such, the user’s state of situation awareness. This paper leverages previous work in defining the information landscape for the domain of rotorcraft pilotage, and explores an approach to identifying common deficiencies in a user’s awareness of the information landscape. This paper takes into account prominent frameworks on situation awareness, and presents a methodology leveraging Large Language Models (LLMs) to identify common information deficiencies occurring at various phases of rotorcraft flight through text mining aviation incident and accident reports. Once these deficiencies are identified, the system can provide holistic situation awareness considering a larger data space than the user, and also provide human-centered awareness of prioritized concerns of situation awareness factors and possible mitigations. © 2024, EasyChair. All rights reserved.

关键词： Aircraft accidents

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Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 66-75页

作者： Lingkang Zeng Wei Yao Ze Hu Hang Shuai Zhouping Li Jinyu Wen Shijie Cheng State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronics EngineeringHuazhong University of Science and TechnologyWuhan 430074China Dispatching and Control Center Central China Branch of State Grid Corporation of ChinaWuhan 430077China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA

Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and ***,GTS is composed of offline predetermination and real-time scenario ***,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power *** improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of ***,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability ***,linear action space is developed to reduce dimensionality of action space for multiple controllable ***,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making *** can enhance the efficiency and learning ***,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning *** simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.

关键词： Deep reinforcement learning generator tripping scheme graph convolutional network invalid action masking knowledgefusion

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