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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Feiyu Zhou, Jian-Tao College of Computer Science Inner Mongolia University Inner Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350337020

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads. © 2023 IEEE.

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Computation Offloading Algorithm Based on Deep Reinforcement Learning and Multi-Task Dependency for Edge Computing 25th

Computation Offloading Algorithm Based on Deep Reinforcemen...

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25th International computer Symposium on New Trends in computer Technologies and Applications, ICS 2022

作者： Lin, Tengxiang Lin, Cheng-Kuan Chen, Zhen Cheng, Hongju College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan

ISBN: (纸本)9789811995811

Edge computing is an emerging promising computing paradigm that brings computation and storage resources to the network edge, significantly reducing service latency. In this paper, we aim to divide the task into several sub-tasks through its inherent interrelation, guided by the idea of high concurrency for synchronization, and then offload sub-tasks to other edge servers so that they can be processed to minimize the cost. Furthermore, we propose a DRL-based Multi-Task Dependency Offloading Algorithm (MTDOA) to solve challenges caused by dependencies between sub-tasks and dynamic working scenes. Firstly, we model the Markov decision process as the task offloading decision. Then, we use the graph attention network to extract the dependency information of different tasks and combine Long Short-term Memory (LSTM) with Deep Q Network (DQN) to deal with time-dependent problems. Finally, simulation experiments demonstrate that the proposed algorithm boasts good convergence ability and is superior to several other baseline algorithms, proving this algorithm’s effectiveness and reliability. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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DHCT-DT: Dual-branch Hybrid CNN-Transformer Combined with Dual-Teacher Semi-Supervised Network for Kidney Ultrasound Image Segmentation

DHCT-DT: Dual-branch Hybrid CNN-Transformer Combined with Du...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yang Luo Jun Liu Mengqian Ding School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Accurate segmentation of kidney ultrasound (KUS) images is of great significance for the diagnosis of kidney diseases. However, KUS images have challenges such as low contrast, large size variations, and acoustic shadow interference. In addition, obtaining labeled data is a major challenge in the field of medical image segmentation. This paper proposes a dual-branch hybrid CNN Transformer combined with a dual teacher semi-supervised segmentation network (DHCT-DT) based on the MT model. The reliability of guidance can be improved between two teachers by constructing consistency regularization constraints. The U-Net architecture is employed between the main teacher and student model. The auxiliary teacher model adopts a dual-branch hybrid CNN Transformer architecture. The multi-scale channel fusion attention mechanism (MSCF) is introduced to capture the feature correlation information of KUS images at different scales and channels. Two different training methods can combine their advantages to fully extract local and global information from images. To demonstrate the applicability of the proposed method, experiments were conducted on two datasets: a KUS dataset and a breast ultrasound (BUS) dataset. Both datasets demonstrated the most competitive segmentation performance. For example, in a semi-supervised experiment using only 10% labeled data in the BUS dataset, the Dice coefficient, Jaccard coefficient, HD, and ASSD reached 82.28%, 76,36%, 13.36mm, and 3.71mm.

关键词： Training Image segmentation Ultrasonic imaging Attention mechanisms Accuracy Predictive models Network architecture Transformers Reliability Kidney

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A Lightweight and key Escrow-Free Access Control Scheme on Encrypted Data in Edge Computing

A Lightweight and Key Escrow-Free Access Control Scheme on E...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Heng He Yuejia Chen Jianfeng Lu Jiacheng Zheng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Attribute-based encryption provides fine-grained access control, ensuring data security in edge computing. However, existing access control schemes face issues such as high computational complexity due to bilinear pairing and key escrow with a single authority, which limit their effectiveness for lightweight terminal devices. To address these challenges, we propose a lightweight and key escrow-free access control scheme on encrypted data. This scheme replaces bilinear pairing with fast elliptic curve scalar multiplication, transferring most computational tasks to edge servers while preserving privacy, thus alleviating the computational burden on terminal devices. Additionally, we design a blockchain-based distributed key management method that allows multiple edge servers to distribute private keys collaboratively, resolving the key-escrow issue. Security analysis and performance evaluation demonstrate that our scheme effectively ensures data confidentiality, resists collusion attacks, and achieves high computational efficiency on the user side with low storage overheads, making it highly suitable for lightweight devices in edge computing.

关键词： Access control Performance evaluation Privacy Elliptic curves Process control Transforms Resists Encryption Servers Edge computing

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Improving generalization in offline reinforcement learning via adversarial data splitting 24

Improving generalization in offline reinforcement learning v...

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Proceedings of the 41st International Conference on Machine Learning

作者： Da Wang Lin Li Wei Wei Qixian Yu Jianye Hao Jiye Liang Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Taiyuan China College of Intelligence and Computing Tianjin University Tianjin China

Offline Reinforcement Learning (RL) commonly suffers from the out-of-distribution (OOD) overestimation issue due to the distribution shift. Prior work gradually shifts their focus from suppressing OOD overestimation to avoiding overly conservative learning from suboptimal behavior policies to improve generalization. However, most approaches explicitly delimit boundaries for OOD actions based on the support in the dataset, which can potentially impede the data near these boundaries from acquiring realistic estimates. This paper investigates how to loosen the rigid demarcation of OOD boundaries, adaptively extracting knowledge from empirical data to implicitly improve the model's generalization to nearby unseen data. We introduce an adversarial data splitting (ADS) framework that enforces the model to generalize the distribution shifts simulated from the train=validation subsets splitting of the dataset. Specifically, ADS is modeled as a min-max optimization problem inspired by metalearning and solved by iterating over the following two steps. First, we train the model on the train-subset to minimize its loss on the validation-subset. Then, we adversarially generate the "hardest" train=validation subsets with the maximum distribution shift, making the model incapable of generalization at that splitting. We derive a generalization error bound for theoretically understanding ADS and verify the effectiveness with extensive experiments. Code is available at https://***/DkING-lv6/ADS.

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Delay-Aware Digital Twin Synchronization in Mobile Edge Networks With Semantic Communications

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IEEE Transactions on Vehicular technology 2025年

作者： Li, Bin Cai, Haichen Liu, Lei Fei, Zesong Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Ministry of Education Guilin University of Electronic Technology Key Laboratory of Cognitive Radio and Information Processing Guilin541004 China Xidian University Guangzhou Institute of Technology Guangzhou510555 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China

The synchronization of digital twins (DT) serves as the cornerstone for effective operation of the DT framework. However, the limitations of channel capacity can greatly affect the data transmission efficiency of wireless communication. Unlike traditional communication methods, semantic communication transmits the intended meanings of physical objects instead of raw data, effectively saving bandwidth resource and reducing DT synchronization latency. Hence, we are committed to integrating semantic communication into the DT synchronization framework within the mobile edge computing system, aiming to enhance the DT synchronization efficiency of user devices (UDs). Our goal is to minimize the average DT synchronization latency of all UDs by jointly optimizing the synchronization strategy, transmission power of UDs, and computational resource allocation for both UDs and base station. The formulated problem involves sequential decision-making across multiple coherent time slots. Furthermore, the mobility of UDs introduces uncertainties into the decision-making process. To solve this challenging optimization problem efficiently, we propose a soft actor-critic-based deep reinforcement learning algorithm to optimize synchronization strategy and resource allocation. Numerical results demonstrate that our proposed algorithm can reduce synchronization latency by up to 13.2% and improve synchronization efficiency compared to other benchmark schemes. © 1967-2012 IEEE.

关键词： Resource allocation

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The Dual Path Multi-Feature Fusion Model combining CNN and Transformer for Age Estimation of Caenorhabditis elegans

The Dual Path Multi-Feature Fusion Model combining CNN and T...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mengqian Ding Jun Liu Yang Luo School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The relatively simple genome, short life cycle, and easy observation of Caenorhabditis elegans (C. elegans) make it an excellent model organism for aging research. Age-stage prediction of C. elegans can help to assess the effectiveness of anti-aging interventions, better explore the role of individual differences in the aging process, and thus promote the development of medicine. However, there are still some problems in accurately predicting the age stages of C. elegans. On the one hand, there may be individual differences among C. elegans due to natural variation and environmental factors. On the other hand, nematode data sets are scarce and image information is complex. In this paper, we perform the age prediction task by studying the body contour morphology, biomarkers, and internal organ changes of C. elegans. Specifically, to address the effects of individual differences and datasets, this paper combines the body contour features, protein aggregation features, and pharyngeal terminal bulb features of C. elegans. In addition, considering the complex information of C. elegans images, this paper proposes a hybrid CNN and Transformer feature extraction method (DFCT), which can fully extract the global and local features of the image. A Dual Path Adaptive Feature Fusion Module (DAFM) is used to fuse the features. A series of experiments are conducted to verify the effectiveness of the proposed method. The experimental results show that the method proposed in this paper outperforms other methods in terms of accuracy and precision, especially in dividing 25 age stages into 3 categories, with an accuracy of 85.93%.

关键词： Proteins Hands Accuracy Estimation Biomarkers Aging Feature extraction Transformers Pharynx Biomedical imaging

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PMC-CNN: Trajectory Prediction Method Fusing Pedestrian Motion Features

PMC-CNN: Trajectory Prediction Method Fusing Pedestrian Moti...

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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Xiling Cao Yan Li Hong Liu School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China Inspur (Shandong) Computer Technology Co Ltd. Jinan China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

Predicting how pedestrians will move is a valuable and challenging task. It can be applied to various domains, such as crowd evacuation and autonomous driving. However, in practice, scene information and pedestrian interaction make it difficult to accurately predict pedestrian trajectories. Therefore, more effective processing of information affecting pedestrian trajectories is particularly important. Extraction of pedestrian motion features is insufficient in current research in this area. To address this problem, a neural network trajectory prediction method, PMC-CNN, is proposed. This method incorporates pedestrian motion features and stores the heterogeneous information required for prediction within a knowledge graph. Furthermore, it combines this information with the proposed IN-EX pedestrian motion model, resulting in an improvement in prediction accuracy. In addition, this research applies the prediction framework to a crowd evacuation scenario to improve the efficiency of crowd evacuation. This verifies the effectiveness and practicality of our proposed model.

关键词： Pedestrians Accuracy Urban planning Neural networks Safety management Knowledge graphs Predictive models Feature extraction Real-time systems Trajectory

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Fully Differentiable Correlation-Driven 2D/3D Registration for X-Ray to CT Image Fusion

Fully Differentiable Correlation-Driven 2D/3D Registration f...

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IEEE International Symposium on Biomedical Imaging

作者： Minheng Chen Zhirun Zhang Shuheng Gu Zhangyang Ge Youyong Kong School of Computer Science and Engineering Southeast University China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University China Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Image-based rigid 2D/3D registration is a critical technique for fluoroscopic guided surgical interventions. In recent years, some learning-based fully differentiable methods have produced beneficial outcomes while the process of feature extraction and gradient flow transmission still lack controllability and interpretability. To alleviate these problems, in this work, we propose a novel fully differentiable correlation-driven network using a dual-branch CNN-transformer encoder which enables the network to extract and separate low-frequency global features from high-frequency local features. A correlation-driven loss is further proposed for low-frequency feature and high-frequency feature decomposition based on embedded information. Besides, a training strategy that learns to approximate a convex-shape similarity function is applied in our work. We test our approach on a in-house dataset and show that it outperforms both existing fully differentiable learning-based registration approaches and the conventional optimization-based baseline. Our code is available at https://***/m1nhengChen/cdreg.

关键词： Training Correlation Computed tomography Surgery Process control Feature extraction Controllability

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Characteristic Mode Cancellation for Radiation Pattern Decoupling of Asymmetric Dipole Antennas

Characteristic Mode Cancellation for Radiation Pattern Decou...

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Cross Strait Quad-Regional Radio Science and Wireless technology Conference (CSRWTC)

作者： Yu-Zhuo Deng Nan Yang Kai Lu Jingchang Nan Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electronics and Information Technology (School of Microelectronics) Sun Yat-sen University Guangzhou China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Technical University Huludao China

ISBN: (数字)9798331507794

ISBN: (纸本)9798331507800

1 × 2 asymmectric dipole antennas with radiation pattern decoupling (RPD) characteristic are presented in this paper. These two dipole antennas are connected by a cross shaped strip. Characteristic mode analysis (CMA) is used to analyze and design this kind of antenna. Observed with CMA, a pair of common and differential characteristic modes superposed together to obtain little current distribution under the coupled dipole antenna. Therefore, RPD characteristic can be realized with high isolation. To verify the concept, a prototype is designed, fabricated, and measured, with good agreement observed. A measured 22.5% 10-dB impedance bandwidth can be obtained, over which the measured isolation is higher than 23 dB. It should be mentioned that RPD effect can be found over entire impedance band, which is highly desired for practical applications.

关键词： Antenna measurements Wireless communication Impedance measurement Philosophical considerations Dipole antennas Prototypes Bandwidth Impedance Gain Antenna radiation patterns

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