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Conference on Local computer Networks (LCN)

作者： Sikai He Jian Chen Peiyun Zhang Zhangjie Fu School of Software Nanjing University of Information Science & Technology China School of Computer Science School of Cyber Science and Engineering Nanjing University of Information Science & Technology China

ISBN: (数字)9798350388008

ISBN: (纸本)9798350388015

Edge devices are positioned on the periphery of cloud environments, serving as agile, secure, and dependable service providers. Nevertheless, IoT edge devices grapple with distinct physical and communication vulnerabilities, such as message forgery and tampering. Thus, trust computing is indispensable for IoT edge devices. Existing trust computing methods confront certain challenges, such as overlooking the impact of fluctuating device resources and subjectivity in human-based weighting aggregation. This work designs a multi-source trust model rooted in IoT edge task collaboration. Edge devices undergo evaluation based on direct trust, indirect trust, and capability trust. This evaluation yields trust values for the edge devices, allowing for the identification of those with dynamic identities with malicious behavior. Subsequently, these malicious entities are excluded from the pool of edge devices. The proposed model employs an IoT consortium blockchain. This blockchain maintains records of interactions within edge task collaboration and documents feedback on trust concerning edge devices. The stored data is accessible to the public and can be verified by other devices, acting as a safeguard against tampering by malicious devices, thereby ensuring data security and privacy. Experimental results affirm that the proposed model exhibits superior computational efficiency and greater reliability when compared to existing methods.

关键词： Data privacy Computational modeling Data security Collaboration Forgery Blockchains Reliability

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An Improved Marine Predators Algorithm Based on Group Learning 19

An Improved Marine Predators Algorithm Based on Group Learni...

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Li, Liying Zhao, Jian Wang, Jiacun Guo, Xiwang School of Science University of Science and Technogly Liaoning Anshan China Monmouth University Department of Computer Science and Software Engineering United States Computer and Communication Engineering College Liaoning Petrochemical University Monmouth University Fushun China

ISBN: (纸本)9781665472432

To address the shortcomings of the marine predators algorithm (MPA) in solving complex problems, such as low optimization accuracy and easily falling into local optimization, this paper proposes an improved marine predators algorithm based on group learning (GLMPA). An opposition-based learning method is adopted to enhance the quality of the initial solutions. Then, a group learning strategy is used to diversify the population. Two subgroups are produced by fitness evaluation and employing different updating mechanisms. In addition, a new position-updating rule is used to help the proposed algorithm escape from the local optima in the later stage of iteration. Finally, six test functions are utilized to test the GLMPA, and the simulation results verify the effectiveness of the proposed algorithm when compared with other famous algorithms. © 2022 IEEE.

关键词： Iterative methods

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Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers

arXiv

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arXiv 2025年

作者： Sun, Bohang Liò, Pietro School of Information and Software Engineering University of Electronic Science and Technology China Computer Science Department University of Cambridge United Kingdom

In this study, we introduce the Multi-Head Explainer (MHEX), a versatile and modular framework that enhances both the explainability and accuracy of Convolutional Neural Networks (CNNs) and Transformer-based models. MHEX consists of three core components: an Attention Gate that dynamically highlights task-relevant features, Deep Supervision that guides early layers to capture fine-grained details pertinent to the target class, and an Equivalent Matrix that unifies refined local and global representations to generate comprehensive saliency maps. Our approach demonstrates superior compatibility, enabling effortless integration into existing residual networks like ResNet and Transformer architectures such as BERT with minimal modifications. Extensive experiments on benchmark datasets in medical imaging and text classification show that MHEX not only improves classification accuracy but also produces highly interpretable and detailed saliency scores. © 2025, CC BY.

关键词： Convolutional neural networks

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Chaotic Association Feature Extraction of Big Data Clustering Based on the Internet of Things

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Informatica (Slovenia) 2022年第3期46卷 333-342页

作者： Liu, Xiaoming Singh, Thipendra Pal Gupta, Rajeev Kumar Onyema, Edeh Michael JingZhou Vocational College of Technology Software Engineering Institute Hubei Jingzhou434000 China School of Computer Science University of Petroleum and Energy Studies Dehradun India Pandit Deendayal Energy University Gandhinagar India Department of Mathematics and Computer Science Coal City University Enugu Nigeria

This article addresses the stabilization of chaotic characteristics in abnormal data by proposing chaotic correlation feature extraction of big data clustering based on the Internet of things. The chaotic features in big data usually show complex folding and distortion without obvious rules and order and non-synchronization. In this article, the dimension of extracted correlation is utilized as the chaotic feature for the clustering of big data. The one-dimensional time series that can be extended in multi-dimensional space is analysed based on phase space reconstruction, to extract the chaotic correlation dimension (CCD) features. After the relevant experimental analysis, this paper mainly compares the energy consumption and processing time of the two respective algorithms. In the simulation parameter design, the time interval of big data packet generation is 0.1s, and the data is generated from the simulation time of 300s. The results obtained show that when dealing with the same amount of data, the energy consumption of this algorithm is significantly lower than that of the traditional algorithm. When dealing with the same amount of data, the time required by this algorithm is significantly lower than that of the traditional algorithm. This is because this algorithm is easy to implement and has good clustering efficiency for data, so the clustering time is short. With the gradual increase in the amount of data, the correlation dimension of this algorithm tends to be stable. While the correlation dimension of the traditional algorithm fluctuates greatly, it is revealed that the proposed approach has high data clustering efficiency and verifies the effectiveness of this algorithm. © 2022 Slovene Society Informatika. All rights reserved.

关键词： Feature extraction

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Blockchain-Assisted Secure Fine-Grained Searchable Encryption for a Cloud-Based Healthcare Cyber-Physical System

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IEEE/CAA Journal of Automatica Sinica 2021年第12期8卷 1877-1890页

作者： Mamta Brij B.Gupta Kuan-Ching Li Victor C.M.Leun Kostas E.Psannis Shingo Yamaguchi IEEE the Department of Computer Engineering National Institute of Technology the Asia University the Macquarie University the Department of Computer Science and Information Engineering Providence University the Computer Science and Software Engineering Shenzhen University the University of British Columbia the Department of Applied Informatics University of Macedonia the Graduate School of Sciences and Technology for Innovation Yamaguchi University

The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access *** privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective ***-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud ***,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious *** a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE *** the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain ***,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical *** the assistance of blockchain technology,the proposed scheme offers two main ***,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of ***,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain ***,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus *** eliminates the need of the trusted authority and reduces the burden of data users,***,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret

关键词： Cloud-based cyber-physical systems(CCPS) data encryption healthcare information search and retrieval keyword search public-key cryptosystems searchable encryption

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Q-Learning enabled Ant Colony (Q-ANT) based Navigation Optimization Framework for UAVs

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IEEE Transactions on Consumer Electronics 2025年

作者： Wei, Xing Li, Ning Yang, Hua Guilin University of Aerospace Technology School of Computer Science and Engineering Guilin China Guilin University of Electric Technology School of Software Engineering school of Computer Science and Information Security Guilin China Huanghuai University School of Computer and Artificial Intelligence Henan China

Flying ad-hoc networks (FANETs) composed of unmanned aerial vehicles (UAVs) which are serving in both military as well as civilian domains. The environmental changes and alterations in network topology present considerable challenges to communication among drone swarms. Consequently, there is an imperative need to investigate a high-performance routing scheme for UAV based FANETs. In this study, a routing scheme is introduced that employs the reinforcement Q-learning algorithm to enhance the ant colony optimization routing protocol (Q-ANT). The primary innovation of this scheme lies in the implementation of a neighborhood link selection mechanism that incorporates link residual time and link signal strength. This approach addresses the inadequacy of relying solely on a single indicator for assessing link residual survival time and expedites the convergence velocity of the ant colony routing algorithm. Q-ANT possesses the capability to optimize its routing strategy in accordance with the prevailing network environment by utilizing the Q-learning algorithm to attain the ideal option strategy in response to environmental fluctuations. The proposed scheme is implemented within the NS3 simulation environment and scenario-based simulation experiments have been conducted on three critical network metrics: network lifetime, end-to-end delay, and packet delivery rate. The experimental results exhibit the superiority of Q-ANT in terms of minimal energy consumption, minimal latency, and an enhanced packet delivery rate to the existing approaches such as FAnt-HocNet and Q-learning based QRF. © 1975-2011 IEEE.

关键词： Ant colony optimization

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Hybrid Makes Better: Privacy-Preserving Medical Image Classification Based on Federated Learning

Hybrid Makes Better: Privacy-Preserving Medical Image Classi...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Zhi Chi, Haotian Li, Yonggang Zhang, Yuxuan Jiang, Shunrong Du, Xiaojiang Guizani, Mohsen China University of Mining and Technology School of Computer Science Xuzhou221116 China Shanxi University School of Automation and Software Engineering Taiyuan030006 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350351255

The power of machine learning makes it available for medical image classification. However, machine learning requires large medical datasets to improve accuracy, which will involve patients' private information and lead to their privacy leakage. Federated learning is a trending technique to both protect patients' privacy and improve the accuracy of medical image classification. Unfortunately, current research shows that federated learning faces the risk of privacy leakage. In this paper, we propose a privacy-preserving scheme FHDM. Specifically, we construct a local hybrid differential privacy algorithm (LHDP) against patients' privacy leakage. This hybrid algorithm utilizes both Gaussian and Laplace differential privacy without enlarging the privacy budget. We prove that the algorithm applies to the model parameter. Moreover, we design loss optimization and global optimization strategies on the algorithm to achieve higher accuracy in medical image classification. Finally, we validate FHDM in terms of privacy-preserving and model accuracy on real datasets. Experiments show that FHDM effectively protects privacy and improves the average accuracy by 10.0% compared to adopting the LDP-based scheme with the same medical image dataset and privacy budget. © 2024 IEEE.

关键词： Federated learning

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Analysis of converting -circuit into -circuit

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EPJ Quantum Technology 2025年第1期12卷 1-14页

作者： Luo, Qing-bin Ding, Lang Yang, Guo-wu Li, Xiao-yu College of Intelligent Systems Science and Engineering Hubei Minzu University Enshi China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Big data research Center & School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

A $\mathfrak{C^{*}}$ -circuit, which was proposed in Asiacypt 2022 by Huang and Sun (Advances in cryptology – ASIACRYPT 2022, pp. 614–644, 2022), can directly perform calculations with the existing quantum states, thereby reducing the use of quantum resources in quantum logic synthesis. We theoretically prove how to convert a $\mathfrak{C^{0}}$ -circuit into the corresponding $\mathfrak{C^{*}}$ -circuit through two lemmas and one theorem. The first lemma proves the interchangeability of CNOT gates and NOT gates by using the equivalence of quantum circuits. The second lemma proves that adding CNOT gates to the front of a quantum circuit whose initial states are all $|0\rangle $ s will not change the output states of the circuit. The theorem is used to describe what kind of $\mathfrak{C^{0}}$ -circuit can be transformed into $\mathfrak{C^{*}}$ -circuit, and the correctness of this transformation is proved. Our work will provide a theoretical basis for converting $\mathfrak{C^{0}}$ -circuit to $\mathfrak{C^{*}}$ -circuit. Then applying the theoretical analysis results to the multiplication over $\text{GF}(2^{8})$ , the constructed quantum circuit needs 27 Toffoli gates and 118 CNOT gates, which is 15 fewer Toffoli gates and 43 CNOT gates than the current best result. This shows that the method of constructing quantum circuits by using the conversion of $\mathfrak{C^{0}}$ -circuit to $\mathfrak{C^{*}}$ -circuit is very efficient.

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Distributed quantum architecture search

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Physical Review A 2024年第2期110卷 022403-022403页

作者： Haozhen Situ Zhimin He Shenggen Zheng Lvzhou Li College of Mathematics and Informatics School of Electronic and Information Engineering Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area (Guangdong) Shenzhen 518045 China Institute of Quantum Computing and Software School of Computer Science and Engineering

Variational quantum algorithms, inspired by neural networks, have become a novel approach in quantum computing. However, designing efficient parameterized quantum circuits remains a challenge. Quantum architecture search tackles this by adjusting circuit structures along with gate parameters to automatically discover high-performance circuit structures. In this study, we propose an end-to-end distributed quantum architecture search framework, where we aim to automatically design distributed quantum circuit structures for interconnected quantum processing units with specific qubit connectivity. We devise a circuit generation algorithm which incorporates TeleGate and TeleData methods to enable nonlocal gate implementation across quantum processing units. While taking into account qubit connectivity, we also incorporate qubit assignment from logical to physical qubits within our quantum architecture search framework. A two-stage progressive training-free strategy is employed to evaluate extensive circuit structures without circuit training costs. Through numerical experiments on three VQE tasks, the efficacy and efficiency of our scheme is demonstrated. Our research into discovering efficient structures for distributed quantum circuits is crucial for near-term quantum computing where a single quantum processing unit has a limited number of qubits. Distributed quantum circuits allow for breaking down complex computations into manageable parts that can be processed across multiple quantum processing units.

关键词： Machine learning Quantum circuits

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SIAT: Pedestrian trajectory prediction via social interaction-aware transformer

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Complex & Intelligent Systems 2025年第8期11卷 1-14页

作者： Wang, Chengdong Wang, Jianming Gao, Wenbo Guo, Lei School of Computer and Software Engineering Anhui Institute of Information Technology Wuhu China Yunnan Key Laboratory of Computer Technology Applications Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

Pedestrian trajectory prediction is crucial for mitigating collision risks in intelligent transportation and surveillance systems. Despite recent advances, accurately capturing and modeling complex social interactions among pedestrians remains a challenge. This paper introduces the Social Interaction-Aware Transformer (SIAT), a novel approach that leverages a Transformer encoder to process pedestrian embedding features and a Graph Convolutional Network (GCN) to construct a social graph for extracting spatial interaction features. The future pedestrian trajectory is predicted using a Transformer decoder that integrates both pedestrian embeddings and social graph features. Extensive experiments on the ETH/UCY and Stanford Drone datasets demonstrate that SIAT significantly outperforms state-of-the-art methods in terms of accuracy and robustness, particularly in densely populated environments. SIAT’s contributions include improved precision through temporal and spatial processing, deep contextual understanding of pedestrian dynamics, and robustness across various settings. The novel model framework establishes a new benchmark for mixed models in trajectory prediction.

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