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10th IEEE Smart World Congress, SWC 2024

作者： Zheng, Jingjing Zheng, Hongqiang Chen, Xing Zhang, Jianshan Fuzhou University College of Computer and Data Science FuZhou350116 China MinJiang University College of Computer and Data Science FuZhou350116 China

ISBN: (纸本)9798331520861

The development of the Space-Air-Ground Integrated Network (SAGIN) represents a significant advancement in satellite Internet technology, primarily because it facilitates the provision of pervasive networking solutions for the Internet of Things (IoT). Central to minimizing energy consumption in this context is the strategic deployment of Unmanned Aerial Vehicles (UAVs) and the efficient offloading of computational tasks. This is due to the fact that the quality of communication between UAVs and ground-based mobile devices is a crucial determinant of both communication latency and energy usage. This study presents an in-depth exploration of a Mobile Edge Computing (MEC) framework within a SAGIN context, in which multiple UAVs serve as edge computing servers, thereby accelerating computational services to terrestrial devices. In parallel, Low Earth Orbit (LEO) satellites are leveraged to provide cloud computing services. We introduce a Dual-layer nested joint optimization technique fortified by an improved differential evolution. The outer layer of the algorithm employs a phased differential evolution technique to optimize the positioning of UAVs. Simultaneously, the inner layer adopts a greedy approach to optimize the offloading ratio for computational tasks. The effectiveness and superior performance of the proposed method are substantiated through extensive numerical simulations. Comparative analysis with existing benchmark techniques demonstrates a significant reduction in the energy consumption of the system, underscoring the potential of the proposed framework. © 2024 IEEE.

关键词： Computation offloading

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Einocchio: Efficiently Outsourcing Polynomial Computation with Verifiable Computation and Optimized Newton Interpolation

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IEEE Transactions on Information Forensics and Security 2025年 20卷 1981-1992页

作者： Pei, Xintao Chen, Yuling Long, Yangyang Sang, Haiwei Luo, Yun Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China

Cloud computing, as a promising service platform, has gained significant popularity in addressing emerging data privacy issues in applications such as machine learning and data mining. Researchers have proposed the verifiable computing that allows the cloud users to delegate their computation tasks to the cloud server. Then, the cloud server computes the cryptographic proofs that verify the correctness of the results, a process that is generally faster ompared to local manual computation. However, performing computation tasks or verifying the correctness of encrypted data, such as multivariate polynomial functions, remains a significant challenge. To solve this problem, we propose Einocchio: A verifiable computation scheme that combines the efficient Pinocchio system with homomorphic encryption, which allows the public verification of the computational results on the server side while ensuring data confidentiality and the results. Compared with the existing solutions, Einocchio does not reveal the client's input. Furthermore, we extrapolate Einocchio by optimizing the Pinocchio's quadratic arithmetic program component using a differential optimization method, which reduces the computational workload owing to the conversion from quadratic to linear complexity, thereby increasing the efficiency of the quadratic arithmetic program preprocessing stage. Security analysis demonstrates that Einocchio achieves IND-CPA security. Finally, the performance evaluation confirmed its effectiveness and suitability for cloud computing environments. Compared to the corresponding scheme based on Newton interpolation, Einocchio achieves a threefold greater computational efficiency, with the generation of interpolation polynomials for 50 data inputs occurring in a mere 0.31 ms, while simultaneously reducing the number of computations. © 2005-2012 IEEE.

关键词： Cloud platforms

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PLC-MDT: A Framework for Detecting Anomalies With Digital Twins of Industrial Control Systems

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IEEE Sensors Journal 2025年第10期25卷 17739-17749页

作者： Xu, Zidong Zhang, Zhenyong Zheqiu, Hetu Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China

The attack events (such as the Stuxnet and BlackEnergy) that targeted the industrial control system (ICS) have validated its vulnerability to cyber intrusions. The prevention of ICS from cyberattacks is undoubtedly important and practical as it operates dangerous equipment and produces products that people need. Traditional anomaly detection methods, which rely heavily on SCADA data, are often unreliable due to the limitation of such data in accurately reflecting real-time system states under attacks. Therefore, we propose PLC-MDT, a novel anomaly detection method based on digital twins (DTs), exploiting the modern communication technology and computing resources of ICS. PLC-MDT does not depend solely on SCADA data;instead, it leverages the interaction between DT and real-world ICS, utilizing both DT and field-site data to perform anomaly detection. Specifically, for PLC-MDT, the prediction model in DT is trained based on historical data, and the predicted values are used as input to the mechanism model in DT to generate simulated data. The networked structure of ICS guarantees real-time data collected from field sites for DT. Therefore, PLC-MDT detects attacks by monitoring the difference between the predicted and real-time data, revealing control channel corruption when the difference exceeds a threshold. The effectiveness of PLC-MDT is demonstrated through experiments on real-world systems, showing its potential for real-time detection of anomalies in ICS. © 2001-2012 IEEE.

关键词： SCADA systems

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Design and Implementation of a Customizable EEG dataset Construction System 17

Design and Implementation of a Customizable EEG Dataset Cons...

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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Han, Liyong Liu, Jianwen Cheng, Ye Huang, Zhihua College of Computer and Data Science Fuzhou University China

ISBN: (纸本)9798331507398

Electroencephalography (EEG) plays a pivotal role in diagnosing neurological conditions, such as epilepsy and cognitive impairments. The efficacy of EEG analysis heavily relies on the quality and diversity of the underlying dataset. A significant challenge in EEG data analysis is the pronounced class imbalance, where certain neurological patterns are underrepresented, potentially skewing predictive models and diagnostic accuracy. Moreover, the absence of negative examples in EEG datasets can lead to misclassifications and limit the generalizability of findings. To address these issues, we developed a sophisticated, modular EEG dataset construction system designed to enhance the integration of both positive and negative examples, thereby mitigating class imbalance. This system is highly flexible and scalable, allowing for the tailored creation of datasets that meet specific research and clinical demands. This paper details the design, implementation, and validation of the system, demonstrating its capability to construct balanced datasets that are essential for robust EEG analysis. The results affirm the system's efficacy in fostering more accurate and comprehensive diagnostic and research outcomes in the field of neurology. © 2024 IEEE.

关键词： Electroencephalography

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DeepSwarm:towards swarm deep learning with bi-directional optimization of data acquisition and processing

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Frontiers of computer science 2025年第3期19卷 125-127页

作者： Sicong LIU Bin GUO Ziqi WANG Lehao WANG Zimu ZHOU Xiaochen LI Zhiwen YU School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’an 710072China School of Data Science City University of Hong KongHong Kong 999077China College Of Computer Science And Technology Harbin Engineering UniversityHarbin 150001China

1 Introduction On-device deep learning(DL)on mobile and embedded IoT devices drives various applications[1]like robotics image recognition[2]and drone swarm classification[3].Efficient local data processing preserves privacy,enhances responsiveness,and saves ***,current ondevice DL relies on predefined patterns,leading to accuracy and efficiency *** is difficult to provide feedback on data processing performance during the data acquisition stage,as processing typically occurs after data acquisition.

关键词： drone swarm classification efficient local data processing data processing deep learning dl device deep learning bi directional optimization iot devices swarm deep learning

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Novel Reliability Indicators From the Perspective of data Center Networks

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IEEE Transactions on Reliability 2025年第1期74卷 2459-2472页

作者： Zhuang, Hongbin Li, Xiao-Yan Lin, Cheng-Kuan Liu, Ximeng Jia, Xiaohua Fuzhou University College of Computer and Data Science Fuzhou350108 China National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong

Modern large-scale computing systems always demand better connectivity indicators for reliability evaluation. However, as more processing units have been rapidly incorporated into emerging computing systems, existing indicators (e.g., -component edge connectivity and -extra edge connectivity) have gradually failed to provide the required fault tolerance. In addition, these indicators require, for example, that the faulty network should have at least components (or that each component should have at least nodes). These fault assumptions are not flexible enough to deal with diversified structural demands in practice circumstances. In order to address these challenges simultaneously, this article proposes two novel indicators for network reliability by utilizing the partition matroid technique, named matroidal connectivity and conditional matroidal connectivity. We first investigate the accurate values of (conditional) matroidal connectivity of k -ary n -cube Qnk, which is an appealing option as the underlying topology for modern parallel computing systems. Moreover, we propose an O(kn-1) algorithm for determining structural features of minimum edge sets whose cardinality is the conditional matroidal connectivity of Qnk. Simulation results are presented to verify our algorithm's correctness and further investigate the distribution pattern of edge sets subject to the restriction of partition matroid. We also present comparative analyses illustrating the superior edge fault tolerance of our findings in relation to prior research, which even exhibits an exponential enhancement when k ≥ 4. © 1963-2012 IEEE.

关键词： Fault tolerance

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Towards Adapting CLIP for Gaze Object Prediction

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Proceedings of the ACM on Human-computer Interaction 2024年第ISS期8卷 155-164页

作者： Chen, Dazhi Gou, Gang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

This paper aims to investigate the problem of gaze object prediction in single images. We propose an application-friendly network based on CLIP for gaze object prediction. To avoid domain bias, we utilize a shallow feature adapter that transfers pre-trained features to target-oriented ones. Secondly, we introduce a pooling attention block to exploit the joint representation of multimodal elements, reducing gaze point deviation. Additionally, we introduce a loss that measures the prediction quality by comparing the distribution difference between the model's predictions heatmaps and the ground truth. Extensive experiments demonstrate the superior performance of our model compared to previous models. We will provide the method code at: https://***/fadaishaitaiyang/***. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Prediction models

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Blockchain-Enabled Communication Framework for Secure and Scalable Healthcare data Processing

Blockchain-Enabled Communication Framework for Secure and Sc...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Das, Debashis Landrum, La Chiara Chatterjee, Pushpita Ghosh, Uttam Hussain, Sajid Meharry Medical College Department of Computer Science and Data Science NashvilleTN United States

ISBN: (纸本)9798331504847

The need for secure and efficient communication between connected devices continues to grow in healthcare systems within smart cities. Secure communication of healthcare data in Internet of Things (IoT) systems is critical to ensure patient privacy and data integrity. Problems with healthcare communication, like data breaches, integrity issues, scalability issues, and cyber threats, make it harder for people to trust doctors, cause costs to rise, stop people from using new technology, and put private data at risk. So, this paper presents a blockchain-based hybrid method for sending secure healthcare data that combines IoT systems with blockchain technology and high-tech encryption techniques like elliptic curve cryptography (ECC). The proposed method uses the public key of a smart contract to encrypt private data to protect its privacy. It also uses cryptographic hashing and digital signatures to make sure that the data is correct and real. The framework stores metadata (e.g., hashes and signatures) on-chain, and large data uses off-chain storage like IPFS to reduce costs and improve scalability. It also incorporates a mechanism to authenticate IoT devices and enable secure communication across heterogeneous networks. Moreover, this work bridges gaps in existing solutions by providing an end-to-end secure communication system for healthcare applications. It provides strong data security and efficient storage for a reliable and scalable way to handle healthcare data safely in IoT ecosystems. © 2025 IEEE.

关键词： Sorting

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Game Theory-Based Fastest Route Plan Method for Transportation Network Applications 6

Game Theory-Based Fastest Route Plan Method for Transportati...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Liu, Zheng-Tan Wu, Ling Chen, Chi-Hua College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350306613

This paper presents a vehicle route planning method based on game theory principles and innovative utility functions. By addressing the complexities of real-Time traffic congestion, the proposed framework offers a dynamic allocation strategy for rational decision-making. The utility function, which inte-grates traffic flow volume, road capacity, and congestion effects, provides accurate travel time estimations. Mathematical analysis and validation, including genetic algorithms, underscore the framework's robustness. Equilibrium solutions reveal allocation strategies responsive to varying road conditions. Comparative scenarios demonstrate the utility function's effectiveness in guiding enterprises' decisions. This research extends beyond static models, envisioning a future of data integration, multi-objective optimization, adaptive learning, and eco-friendly navigation. By converging these routes, the paper sets the stage for a smarter, more sustainable transportation landscape. © 2024 IEEE.

关键词： game theory Nash equilibrium route plan transportation network applications

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Cross-Modal Feature Learning for Point Cloud Classification 14

Cross-Modal Feature Learning for Point Cloud Classification

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14th International Conference on Information science and Technology, ICIST 2024

作者： Li, Wan-Hua Zhang, Chun-Yang College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350353334

Traditional 3D shape classification methods face challenges due to the complexity and variability of point cloud data. To address this issue, we propose CFL framework that integrates proxy weights from two modalities through an average fusion approach and adopts a proxy-based contrastive learning strategy to enhance feature representation. By using the average fusion method. we can effectively capture both texture features and geometric features via integrating complementary information from different modalities. Furthermore, the proxy-based contrastive learning method is designed to acquire representations by learning a unified space. Experimental results demonstrate that our CFL method significantly improves classification performance on the ModelNet10 dataset. Meanwhile, we conduct ablation studies on ModelNet10 dataset to confirm the pivotal role of the average fusion method and the proxy-based contrastive learning method, highlighting the potential of cross-modal feature learning method in advancing 3D shape classification tasks. © 2024 IEEE.

关键词： Contrastive Learning

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