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作者： Jiang, Xutong Dou, Ruihan He, Qiang Zhang, Xuyun Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Faculty of Mathematics University of Waterloo Waterloo Canada Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Department of Computing Macquarie University Sydney Australia College of Big Data and Intelligent Engineering Southwest Forestry University China

Edge computing is regarded as an extension of cloud computing that brings computing and storage resources to the network edge. For some Industrial Internet of Things (IIoT) applications such as supply-chain supervision and collaboration, Internet of Vehicles, real-time video analysis and so forth, users should be authenticated before visiting the geographically distributed edge servers. Limited by the considerable latency between the cloud and edge servers, and the limited capacity of edge servers, it is infeasible to copy the authentication method from cloud servers when users are authenticated in edge servers. In view of this challenge, this paper proposes a novel token-based authentication scheme, named EdgeAuth, that enables fast edge user authentication through collaboration among cloud servers and edge servers. Under the EdgeAuth scheme, edge servers can rapidly verify the credentials of users who have been authenticated by the cloud server. EdgeAuth can also protect users from a series of authentication attacks, for example, the replay attack, DoS attack and man-in-the-middle attack. The results of experiments conducted on a simulated edge computing environment validate the usefulness of EdgeAuth through a comparison in latency and throughput against two baseline schemes. © 2023 John Wiley & Sons Ltd.

关键词： Authentication

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Performance Verification of Smith Predictor Control Using IMC Scheme via Chemical Reaction Networks and DNA Strand Displacement Reaction 9

Performance Verification of Smith Predictor Control Using IM...

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9th IEEE Smart World Congress, SWC 2023

作者： Yao, Jingwang Lv, Hui Xiao, Yijun Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China

ISBN: (纸本)9798350319804

Biochemical process control is often achieved through feedback control strategies for theoretical analysis and optimization of biochemical reaction processes. Here, the design improves the robustness of the biochemical process by taking advantage of the Smith prediction structure and internal mode control within the chemical reaction network theory framework to intervene against the time delays and perturbations present in the biochemical process. First, an IMC representation of Smith prediction is constructed using the presence of parameter rectification of the control method, and then an IMC-MSP control scheme is proposed. A first-order time-lag controlled object is established in the construction of the IMC-MSP control scheme. Besides, CRNs and kinetic representations are established based on the control structure to facilitate the evaluation of the control system performance. In this paper, CRNs representations of parabolic functions as signal inputs are defined for the first time and optimized for stepping signals to facilitate the design and study of biomolecular circuits. Notably, for the CRNs-based IMC-MSP control scheme, on the one hand, numerical simulation experiments are designed to verify the effectiveness of the whole scheme from multiple perspectives of time delays, external perturbations, and filter tuning parameters;on the other hand, using DNA strand displacement is reflected to verify the feasibility of the whole scheme. © 2023 IEEE.

关键词： Chemical reaction networks (CRNs) DNA strand displacement (DSD) Internal model control (IMC control) Smith predictor

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Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

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Designing Uncorrelated Address Constrain for DNA Storage by DMVO Algorithm

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2022年第2期19卷 866-877页

作者： Cao, Ben Ii, Xue Zhang, Xiaokang Wang, Bin Zhang, Qiang Wei, Xiaopeng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Ministry of Education School of Software Engineering Dalian University Dalian116622 China School of Computer Science and Technology Dalian University of Technology Dalian116024 China

At present, huge amounts of data are being produced every second, a situation that will gradually overwhelm current storage technology. DNA is a storage medium that features high storage density and long-term stability and is now considered to be a feasible storage solution. Errors are easily made during the sequencing and synthesis of DNA, however. In order to reduce the error rate, novel uncorrelated address constrain are reported, and a Damping Multi-Verse Optimizer (DMVO)algorithm is proposed to construct a set of DNA coding, which is used as the non-payload. The DMVO algorithm exchanges objects through black/white holes in order to achieve a stable state and adds damping factors as disturbances. Compared with previous work, the coding set obtained by the DMVO algorithm is larger in size and of higher quality. The results of this study reveal that the size of the DNA storage coding set obtained by the DMVO algorithm increased by 4-16 percent, and the variance of the melting temperature decreased by 3-18 percent. © 2004-2012 IEEE.

关键词： Damping

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An Active Visual Perception Methodology for the Safety of Human-Robot Collaborations

An Active Visual Perception Methodology for the Safety of Hu...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Siyuan Cheng Pengfei Yi Mengmeng Chen Jing Dong Rui Liu Ling Wang Key Laboratory of Advanced Design and Intelligent Computing (Ministry of Education) School of Software Engineering Dalian University Dalian China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

In industrial settings with multiple robotic arms, ensuring human-robot interaction safety through visual feedback is essential. However, existing methods use multiple pre-set cameras to avoid occlusion, making implementation challenging in compact spaces. We propose an adaptive active visual perception method for safety in human-robot collaboration by equipping a single RGBD camera on an idle robotic arm. This method eliminates the need to add additional brackets, is low cost, and is suitable for compact environments compared to current methods. Firstly, to capture comprehensive and accurate global information as much as possible, an active visual perception strategy integrating static visual perception and proprioceptive pose optimization is designed; then, an optimization-based robot position and pose decision method is proposed by using the real-time robots state and obstacles state, which enables the camera at the end of the robot to acquire more effective views; finally, a dual-robot following strategy is proposed to ensure that it can work in tandem. The validity was proved by experimentation. The video are available at https://***/03lwb.

关键词： Visualization Service robots Robot vision systems Human-robot interaction Streaming media Cameras Manipulators

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SC-LSTM Based Human Action Recognition

SC-LSTM Based Human Action Recognition

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Naixin Kang Pengfei Yi Qingshu Geng Jing Dong Rui Liu Ling Wang Key Laboratory of Advanced Design and Intelligent Computing (Ministry of Education) School of Software Engineering Dalian University Dalian China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Human Action Recognition is one of the most applied research directions in the field of Computer Vision, which is widely used in human-computer interaction, Augmented Reality (AR) technology, security monitoring, and other scenarios. However, due to the complexity of human action gestures, existing Human Action Recognition methods have certain deficiencies in dealing with variable human gestures and action information, and the accuracy needs to be improved. To improve the accuracy, We propose a multi-dimensional network model based on SC-LSTM(Skip-Connection + LSTM). First, a Temporal Feature Extraction Module is designed based on SC-LSTM, and a Spatial Feature Extraction Module is designed based on CNN and Multi-Attention Mechanism to extract potential human action features from both temporal and spatial dimensions, respectively. Then, a separate SC-LSTM classification network is utilized to process these spatio-temporal features to obtain the final HAR results. The experimental results show that compared to other algorithms, the present model can more fully utilize the information in the temporal dimension, and thus performs better in terms of HAR accuracy.

关键词： Human computer interaction Computer vision Accuracy Image recognition Feature extraction Complexity theory Human activity recognition

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Research on PCB tiny displacement estimation method for ORB feature-guided FREAK descriptor enhancement 4

Research on PCB tiny displacement estimation method for ORB ...

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2024 4th International Conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2024

作者： Yang, Duo Su, Yang Wu, Hongwei Yi, Pengfei Key Laboratory for Advanced Design and Intelligent Computing Ministry of Education School of Mechanical Engineering and Software Engineering Dalian University Dalian116622 China Olympic Circuit Technology Co. Ltd. Guangdong Jiangmen529700 China Co. Ltd. Dalian116622 China

Given the hurdles posed by existing techniques for identifying microscopic offsets in miniature printed circuit boards (PCBs), including their low accuracy and high iteration count, this study presents an innovative approach that measures micrometer offsets by utilizing the ORB feature-guided FREAK descriptor enhancement. It improves matching outcomes with a brute-force matcher and RANSAC via ORB feature extraction to guide FREAK descriptors for better feature uniqueness. A PCB small offset detection algorithm is developed with a homography matrix and a theoretical offset model. A novel distance threshold performance evaluation method for this suggested matching algorithm that objectively evaluates matching results is also proposed in this article. The proposed matching method outperforms alternative algorithms in precision and recall, according to its experiments. This research conducts simulation detection of PCB calibration bare boards and instance verification of real PCB boards by constructing a PCB tiny offset detection experimental system. The results show that micron-level detection precision can be achieved without iteration. © Published under licence by IOP Publishing Ltd.

关键词： engineering education

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HybridFL: Hybrid Approach Toward Privacy-Preserving Federated Learning 6th

HybridFL: Hybrid Approach Toward Privacy-Preserving Federate...

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6th International Conference on Security and Privacy in New computing Environments, SPNCE 2023

作者： Ali, Sheraz Mamoon, Saqib Usman, Areeba Abidin, Zain ul Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan250022 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China University of Sargodha Sargodha Pakistan Shandong Provincial Key Laboratory of Network-Based Intelligent Computing University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Software Engineering University of Jinan Jinan250022 China

ISBN: (纸本)9783031736988

In this study, we introduce a novel Hybrid Federated Learning (HybridFL) approach aimed at enhancing privacy and accuracy in collaborative machine learning scenarios. Our methodology integrates Differential Privacy (DP) and secret sharing techniques to address inference risks during training and protect against information leakage in the output model. Drawing inspiration from recent advances, we present a HybridFL framework that combines the strengths of Homomorphic Encryption (HE) and Multi-Party Computation (MPC) to achieve secure computation without the computational overhead of pure HE methods. Our contributions include a privacy-preserving design for Federated Learning (FL) that ensures local data privacy through secret sharing while leveraging DP mechanisms for noise addition. The system offers resilience against unreliable participants and is evaluated using various machine learning models, including Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), and linear regression. Furthermore, we address potential external threats by deploying predictive model outputs as robust services against inference attacks. Experimental results demonstrate improved accuracy and convergence speed, establishing the viability of HybridFL as an effective solution for collaborative machine learning with enhanced privacy guarantees. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Federated learning

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Humanoid Robot Collaborative Lifting Integrating Executable Judgment

Humanoid Robot Collaborative Lifting Integrating Executable ...

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2022 IEEE International Symposium on Product Compliance engineering - Asia, ISPCE-ASIA 2022

作者： Chang, Hua Yi, Pengfei Liu, Rui Dong, Jing Hou, Yaqing Zhou, Dongsheng The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China Dalian University of Technology Dalian China

ISBN: (数字)9798350332483

ISBN: (纸本)9798350332483

Humanoid robot collaborative lifting can be used in a variety of scenarios that require repetitive lifting tasks. Most existing studies of humanoid robot collaboration often assume that objects can always be lifted, which may result in damage to both the robot and the object if objects are too heavy to lift. To avoid such situations as much as possible, a collaborative lifting approach integrating executable judgment is proposed. First, a target search and localization method is constructed using monocular vision and marker points to identify the task object. Then, an executable judgment strategy is designed to determine whether the object is overweight or not according to robot force analysis. Finally, a multi-robot joint control model is proposed based on collaborative communication to perform collaborative tasks with different loads based on the judgment results. Experiments on two humanoid robots for different types and weights of targets show the effectiveness of the proposed approach. © 2022 IEEE.

关键词： Anthropomorphic robots

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MSTF: enhancing long-term forecasting with multi-scale temporal fusion in time series forecasting

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The Journal of Supercomputing 2025年第9期81卷 1-21页

作者： Zhou, Chengjie Jiang, Ke Liu, Yunjiong Che, Chao Zhang, Qiang School of Computer Science and Technology Dalian University of Technology Dalian China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China

Long-term time series forecasting plays a vital role in applications like financial market prediction, energy usage forecasting, and traffic flow analysis. Deep learning models, though widely used in this task, often struggle to capture long-term dependencies and periodic patterns due to the overlapping of variables with different periods. To this end, we propose a multi-scale temporal fusion model named MSTF, which extracts sequential features and periodic patterns across multiple scales using a Time Reverse and Transform block and a Dynamic Combination Reconstruction block. Unlike traditional Transformer-based models, MSTF emphasizes overall temporal continuity rather than individual time point values. Experiments on seven datasets against ten advanced models demonstrate MSTF’s superior performance, faster training time, and reduced model complexity, particularly excelling on high-dimensional datasets.

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