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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lu, Kai Zhao, Jing Ding, Lichao Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan China Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Entity alignment(EA) aims to identify equivalent entities in different knowledge graphs or data sources and establish correspondences between them. In recent years, embedding-based methods have made significant progress in the field of entity alignment, yet the shortage of adequate training data (i.e., seed entity pairs) continues to be a primary challenge. Traditional semi-supervised methods typically address this issue by generating potential mappings (i.e., pseudo-mappings) for unlabeled entities. However, these methods often suffer from the influence of erroneous potential mappings or overlook the uncertainty of potential mappings. Therefore, we propose a novel semi-supervised entity alignment method called JTEA. This method combines seed entity pairs with potential mappings through end-to-end joint training, providing strong guidance for model training. In constructing potential mappings, we propose a bidirectional matching strategy (BMS), which generates more detailed potential mappings by jointly adjusting decisions in different directions and uses a joint matching confidence score to characterize its uncertainty. Furthermore, we also introduce a method of matching diversity alignment selection, aiming to screen out more reliable entity alignments to further improve the performance and stability of the model. We conducted a large number of experiments on the benchmark dataset to fully validate the effectiveness of the JTEA method. © 2024 IEEE.

关键词： Mapping

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Leveraging UAV-RIS Reflects to Improve the Security Performance of Wireless Network Systems

IEEE Networking Letters

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IEEE Networking Letters 2023年第2期5卷 81-85页

作者： Sun, Jianwei Zhang, Heng Wang, Xin Yang, Ming Zhang, Jian Li, Hongran Gong, Chenglong Jiangsu Ocean University School of Electronic Engineering Lianyungang222000 China Jiangsu Ocean University School of Computer Engineering Lianyungang222000 China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center Jinan250014 China

In this letter, a novel application method of reconfigurable intelligent surfaces (RIS) is proposed, which installs a RIS on an unmanned aerial vehicle (UAV) rather than fixed to buildings. When the location information of the eavesdropper is unknown, the UAV's rapid deployment and RIS's ability to change the transmission channel are utilized to dynamically optimize the UAV's flight trajectory and RIS's phase shift. The trajectory of the UAV and phase shift of the RIS are optimized to avoid the risk of potential eavesdropping and improve the average downlink secrecy rate of the system. This letter utilizes Q-learning and Deep Q-network (DQN) algorithms to solve the problem. Experiments show that RIS can reduce the flight distance of the UAV on the premise of ensuring security. The scenarios where the two algorithms are applicable are analyzed, and the DQN algorithm is more suitable for the scenarios of large state and action spaces. Also, the experimental results imply that the RIS-assisted UAV improves the average downlink secrecy rate and system security. © 2019 IEEE.

关键词： Signal to noise ratio

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Research on drill bit sound anomaly detection method based on deep learning 9

Research on drill bit sound anomaly detection method based o...

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9th International Symposium on Advances in Electrical, Electronics, and computer Engineering, ISAEECE 2024

作者： Zhang, Xu Wang, Fuqiang Zhao, Wei Yang, Shumei Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781510683303

Detecting anomalies in mechanical equipment is crucial in the manufacturing industry, which helps to prevent accidents and thus minimize economic losses. However, due to the complexity of drilling sound waveform, short detection time, coupled with the small size of the balanced dataset, the traditional machine learning method is a bit challenging to extract the most valuable features. This study introduces a novel approach for identifying faults in drilling machines based on analyzing the sound of the drill bit. The method combines the improved Global Context Block (GCBlock) with Convolutional Neural networks (CNN) to obtain the most critical information from the spectrum. Besides, Gated Recurrent Unit (GRU) and attention mechanism is proposed to focus on abnormal phenomena in sound. In particular, in order to increase the quantity and diversity of the dataset, data augmentation methods were used. The experimental data demonstrates that this method excels in accuracy on the small-scale, short-duration drill bit sound dataset, and the overall accuracy of the abnormal and normal classes are 95.08%. Moreover, experiments conducted on a dataset containing a diverse range of classes and prolonged sound signals also yield high accuracy. This method has been proven to exhibit high efficiency and resilience in the face of challenges, demonstrating its generalization ability and reliability. © 2024 SPIE.

关键词： Drills

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Heterogeneous many-core optimization method for PipeBCGs solver in PETSc on SW26010-pro 3

Heterogeneous many-core optimization method for PipeBCGs sol...

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3rd International Conference on Electronic Information Engineering, Big Data, and computer technology, EIBDCT 2024

作者： Wang, Xinhua Pan, Jingshan Tian, Min Xu, Chaoshuai Liu, Huazeng Wei, Zhenguo Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781510680449

The PETSc (Portable, Extensible Toolkit for Scientific Computation) library is one of the fundamental general-purpose numerical libraries in high-performance computing environments. It is widely employed for solving problems related to partial differential equations, sparse linear algebra, and other related issues. PETSc plays a crucial role in assisting developers in rapidly creating parallel programs, thereby enhancing the computational efficiency of high-performance computing. This paper initially ports the PETSc program onto the SW26010-pro processor. Following that, we choose a representative solver from the KSP module of PETSc. Addressing four hotspot functions invoked by this solver, we implement many-core optimization for its execution under the Sunway heterogeneous architecture. The experimental results show that the maximum speedup on a single node can reach 62.3 after many-core optimizations of core hotspot functions. This indicates that the many-core optimization of hotspot functions for PETSc linear solvers demonstrates excellent parallel efficiency on the Sunway supercomputer. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Computational efficiency

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Research on a robust sensor maneuvering target tracking algorithm 4

Research on a robust sensor maneuvering target tracking algo...

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4th International Conference on Digital Signal and computer Communications, DSCC 2024

作者： Xu, Gexing Wang, Fuqiang Zhao, Wei Zhang, Jianan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781510681538

In multi-sensor tracking, tracking targets stably and accurately is one of the primary tasks. Aiming at the problems of outlier interference during measurement and unstable tracking filtering in complex scenarios, this paper proposes a robust maneuvering target tracking algorithm. The algorithm combines the probabilistic data association algorithm with the interactive multi-model algorithm, and utilizes the extended Kalman filter for the filtering operation. The accuracy of the algorithm's state estimation during target maneuvering is verified through simulation experiments, which proves that the algorithm can meet the needs of target tracking in complex scenes. © 2024 SPIE.

关键词： Target tracking

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Adversarial example generation method based on normal distribution function optimization iterative gradient 9

Adversarial example generation method based on normal distri...

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9th International Symposium on Advances in Electrical, Electronics, and computer Engineering, ISAEECE 2024

作者： Jin, Xing Wang, Fuqiang Zhao, Wei Wang, Kun Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781510683303

Adversarial attack refers to the destruction of deep learning models by adding perturbations to the image. At present, there remains room for improvement in black box attacks, particularly when dealing with the models equipped with defense mechanisms. Our propose a new attack way for making confrontation sample using the optimal iterative gradient of normal distribution functions. In each iteration, the sampled image is predicted several times from the vicinity of the current image, and their gradient information is calculated. Then, the correction gradient is calculated based on the normal distribution function, and this correction gradient is used for correction in the next iteration. This iterative approach ensures stability in the update direction, reduces overfitting, improves the generalization ability of adversarial examples. We conducted various analyses on the ImageNet1K dataset[7] to prove the usefulness of our method. The results showed that our method has improved the probability of successful attacks © 2024 SPIE.

关键词： Distribution functions

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A Drug Box Recognition System Based on Deep Learning and Cloud-Edge Collaboration for Pharmacy Automation 29

A Drug Box Recognition System Based on Deep Learning and Clo...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhu, Honglei Dong, Anming Yu, Jiguo School of Computer Science and Technology Jinan China Big Data Institute Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Drug box recognition is an integral part of the pharmacy automation system (PAS), which checks for discrepancies between the drugs provided by the system and the doctor's prescription with the help of modern image recognition techniques. Although deep learning has facilitated the development of pharmacy automation systems, practical applications still face challenges. First, pharmacies update drugs frequently, and many pharmacies across the country are trained independently, which requires consuming massive computational resources. Secondly, the equipment performance of pharmacies is poor, and it's hard to guarantee the real-time and accuracy of recognition. Therefore, we propose a cloud-side collaborative drug box recognition system. When new drugs are added, the new model is trained in the cloud and uniformly distributed to each edge through this architecture. In terms of real-time recognition, an improved lightweight YOLOv5 model is added to the edge part, and a text recognition module is deployed in the cloud to validate the recognition results at the edge. This cloud-edge collaboration architecture not only ensures the accuracy of recognition, but also reduces the burden on the compute edge, ensures the real-time *** results show that the proposed cloud-edge architecture reduces the recognition latency by 37.18% and achieves a precision of 97.47%. The improved YOLOv5 model reduces GFLOPs by 45% while achieving a 1.6% precision improvement. © 2023 IEEE.

关键词： Cloud-Edge Collaboration Image Recognition Pharmacy Automation Text Recognition

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Probabilistic Reliability via Subsystem Structures of Arrangement Graph networks

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IEEE Transactions on Reliability 2024年第1期73卷 279-289页

作者： Huang, Yanze Lin, Limei Xu, Li Hsieh, Sun-Yuan Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Fujian Normal University College of Computer and Cyber Security Key Laboratory of Network Security and Cryptology Fuzhou350117 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

With the rapid growth of the number of processors in a multiprocessor system, faulty processors occur in it with a probability that rises quickly. The probability of a subsystem with an appropriate size being fault-free in a definite time interval is a significant and practical measure of the reliability for a multiprocessor system, which characterizes the functionality of a multiprocessor system well. Motivated by the study of subgraph reliability, as well as the attractive structure and fault tolerance properties of (n, k)-arrangement graph A n, k, we focus on the subgraph reliability for A n, k under the probabilistic fault model in this article. First, we investigate intersections of no more than four subgraphs in A n, k, and classify all the intersecting modes. Second, we focus on the probability P(q, A n, k n-1, k-1) with which at least one (n-1, k-1)-subarrangement graph is fault-free in A n, k, when given a uniform probability q with which a single vertex is fault-free, and we establish the P(q, A n, kn-1, k-1) by adopting the principle of inclusion-exclusion under the probabilistic fault model. Finally, we study the probabilistic fault model involving a nonuniform probability with which a single vertex is fault-free, and we prove that the P(q, A n, kn-1, k-1) under both models is very close to the asymptotic value by both theoretical arguments and experimental results. © 1963-2012 IEEE.

关键词： Fault tolerance

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Face Forgery Detection Based on Fine-Grained Clues and Noise Inconsistency

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 144-158页

作者： Zhang, Dengyong He, Ruiyi Liao, Xin Li, Feng Chen, Jiaxin Yang, Gaobo Changsha University of Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation the School of Computer and Communication Engineering Changsha410114 China Hunan University Changsha410082 China

Deepfake detection has gained increasing research attention in media forensics, and a variety of works have been produced. However, subtle artifacts might be eliminated by compression, and the convolutional neural networks (CNNs)-based detectors are invalidated for fake face images with compression. In this work, we propose a two-stream network for deepfake detection. We observed that high-frequency noise features and spatial features are inherently complementary to each other. Thus, both spatial features and high-frequency noise features are exploited for face forgery detection. Specifically, we design a double-frequency transformer module (DFTM) to guide the learning of spatial features from local artifact regions. To effectively fuse spatial features and high-frequency noise features, a dual-domain attention fusion module (DDAFM) is designed. We also introduce a local relationship constraint loss, which requires only image-level labels, for model training. We evaluate the proposed approach on five large-scale benchmark datasets, and extensive experimental results demonstrate the proposed approach outperforms most SOTA works. © 2024 IEEE.

关键词： Frequency domain analysis

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Ferroelectrically gated two-dimensional bismuth oxyselenides for strain-invariant flexible synaptic thin-film transistors

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science China(Information sciences) 2025年第5期68卷 396-403页

作者： Jie WEN Fei XIAO Zheng-Dong LUO Dongxin TAN Xuetao GAN Dawei ZHANG Zhufei CHU Yinshui XIA Yan LIU Genquan HAN Hangzhou Institute of Technology Xidian University State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology School of MicroelectronicsXidian University Key Laboratory of Light Field Manipulation and Information Acquisition Ministry of Industry and Information Technology Shaanxi Key Laboratory of Optical Information Technology School of Physical Science and Technology Northwestern Polytechnical University School of Materials Science and Engineering The University of New South Wales ARC Centre of Excellence in Future Low-Energy Electronics Technologies (FLEET) The University of New South Wales Faculty of Electrical Engineering Computer Science Ningbo University

Two-dimensional van der Waals（2D vdW） semiconductors have proven to be of great importance for flexible thinfilm transistors（TFTs） owing to their intrinsic mechanical flexibility and superior electronic *** particular,bismuth oxyselenide（Bi2O2Se）,featuring ultrahigh electron mobility along with facile scalable thin-film growth methods,could offer a new option to deliver massively enhanced potential for flexible ***,it has remained a challenge to achieve nonvolatile flexible memory devices based on Bi2O2Se TFTs,thereby hindering the extension of Bi2O2Se TFTs to storage and emerging neuromorphic computing ***,a flexible synaptic TFT is demonstrated through the creation of a Bi2O2Se-based ferroelectric field-effect transistor（FeFET） structure on the flexible mica *** proposed device exhibits excellent nonvolatile memory characteristics,including a large memory window,excellent current modulation ratio,great retention,and strong ***,the Bi2O2Se-based FeFET can be operated as a synaptic device with analog conductance-modulating *** to the superior mechanical flexibility of the component materials and the mica substrate,the Bi2O2Se-based FeFETs can retain their performance against various bending states,showing a straininvariant electrical *** study marks the advancement of Bi2O2Se-based TFTs toward flexible nonvolatile memories and synaptic devices.

关键词： Bi$_{2}$O$_{2}$Se FeFET nonvolatile memory flexible device synaptic transistor

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