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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Ke, Dejing Pu, Jiansu University of Electronic Science and Technology of China Sichuan611731 China Big Data Visual Analysis Lab University of Electronic Science and Technology of China Sichuan610000 China

ISBN: (纸本)9783031466632

Link prediction plays an important role in the research of complex networks. Its task is to predict missing links or possible new links in the future via existing information in the network. In recent years, many powerful link prediction algorithms have emerged, which have good results in prediction accuracy and interpretability. However, the existing research still cannot clearly point out the relationship between the characteristics of the network and the mechanism of link generation, and the predictability of complex networks with different features remains to be further analyzed. In view of this, this article proposes the corresponding link prediction indices Reg, DFPA and LW on regular network, scale-free network and small-world network respectively, and studies their prediction properties on these three network models. At the same time, we propose a parametric hybrid index HEM and compare the prediction accuracy of HEM and many similarity-based indices on real-world networks. The experimental results show that HEM performs better than other indices. In addition, we study the factors that play a major role in the prediction of HEM and analyze their relationship with the characteristics of real-world networks. The results show that the predictive properties of factors are closely related to the features of networks. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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A Low-Complexity Three-Dimensional Parameter Estimation Algorithm for FMCW Radar 6

A Low-Complexity Three-Dimensional Parameter Estimation Algo...

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6th IEEE International Conference on Electronic Information and Communication technology, ICEICT 2023

作者： Yuhan, Yuan Yingchun, Li Qi, Long Peng, Wu Tao, Jin Zhiquan, Zhou School of Information Science and Engineering Harbin Institute of Technology Weihai Weihai China Institute of Big Data Science and Industry Shanxi University Taiyuan China

ISBN: (纸本)9798350399059

Frequency Modulated Continuous Wave (FMCW) radar has been widely used in the field of vehicle mounted millimeter wave radar to detect the parameters of targets. A low complexity three-dimensional (3D) parameter estimation algorithm for time-division multiplexing multiple-input multiple-output (TDM-MIMO) FMCW radar is proposed to detect the range, velocity, and azimuth angle of targets. The proposed algorithm utilized the data block selection and rank-reduced technique to avoid the high computational burden of the traditional subspace-based algorithm. Moreover, the proposed algorithm solves the problem of velocity ambiguity in conventional TDM-MIMO FMCW signal processing. The simulation results show that the accuracy and resolution of the algorithm are much higher than the conventional algorithms, such as 3D-FFT, and 3D-MUSIC. © 2023 IEEE.

关键词： Parameter estimation

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Rare earth element Ce enables high thermoelectric performance in n-type SnSe polycrystals

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Journal of Materials science & technology 2023年第12期143卷 234-241页

作者： Shan Li Li Yin Yijie Liu Xiaodong Wang Chen Chen Qian Zhang School of Civil Aviation Northwestern Polytechnical UniversityXi’an 710072China School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of TechnologyShenzhen 518055China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China

SnSe exhibits excellent thermoelectric performance in both n-and p-type single crystals,but its n-type polycrystals are restricted because of the lower electrical ***,we dually introduced rare earth element Ce and PbTe to optimize the thermoelectric properties of n-type SnSe *** is demonstrated that Ce is an effective cationic dopant to convert SnSe from p-to n-type conductor,and an enhanced peak zT value of∼0.9 at 823 K was obtained in Sn 0.97 Ce 0.03 Se due to the improved power ***,PbTe alloying not only reduced the band gap to increase the carrier concentration,but also enhanced the density-of-states effective mass,and hence further increased the power factor in the whole measured temperature ***,the lattice thermal conductivity was significantly reduced owing to the enhanced phonon scattering by the mass and strain *** a result,the peak zT value was increased to∼1.3 for Sn 0.9 Pb 0.07 Ce 0.03 Se 0.93 Te 0.07 together with a high average zT value of∼0.52 in the temperature range of 300 to 823 K.

关键词： Thermoelectric N-type SnSe Ce doping PbTe alloying

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Visual Object Tracking via Cascaded RPN Fusion and Coordinate Attention

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Computer Modeling in Engineering & sciences 2022年第9期132卷 909-927页

作者： Jianming Zhang Kai Wang Yaoqi He Lidan Kuang School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China

Recently,Siamese-based trackers have achieved excellent performance in object ***,the high speed and deformation of objects in the movement process make tracking ***,we have incorporated cascaded region-proposal-network(RPN)fusion and coordinate attention into Siamese *** proposed network framework consists of three parts:a feature-extraction sub-network,coordinate attention block,and cascaded RPN *** exploit the coordinate attention block,which can embed location information into channel attention,to establish long-term spatial location dependence while maintaining channel ***,the features of different layers are enhanced by the coordinate attention *** then send these features separately into the cascaded RPN for classification and *** to the two classification and regression results,the final position of the target is *** verify the effectiveness of the proposed method,we conducted comprehensive experiments on the OTB100,VOT2016,UAV123,and GOT-10k *** with other state-of-the-art trackers,the proposed tracker achieved good performance and can run at real-time speed.

关键词： Object tracking deep learning coordinate attention cascaded RPN

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Wi-SFDAGR: WiFi-Based Cross-Domain Gesture Recognition via Source-Free Domain Adaptation

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IEEE Internet of Things Journal 2025年第13期12卷 24159-24173页

作者： Yan, Huan Zhang, Xiang Huang, Jinyang Feng, Yuanhao Li, Meng Wang, Anzhi Ou, Weihua Wang, Hongbing Liu, Zhi Guizhou Normal University School of Big Data and Computer Science Guiyang550025 China University of Science and Technology of China CAS Key Laboratory of Electromagnetic Space Information Hefei230026 China Hefei University of Technology School of Computer and Information Hefei230601 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan

WiFi Channel State Information (CSI)-based gesture recognition offers unique advantages, including cost-effectiveness and enhanced privacy protection, and has garnered significant attention in recent years. However, existing WiFi-based gesture recognition solutions exhibit poor generalization ability when deployed in new environment, orientation, or location. Although some methods combine labeled source domain and unlabeled target domain to learn domain-independent features, factors such as data privacy protection hinder access to source data during practical environment adaptation. Consequently, we consider realistic scenario where source data is unavailable during adaptation of unlabeled test data, and instead, a trained source domain model is used. In this paper, we propose Wi-SFDAGR, a WiFi-based Source-free Domain Adaptation Gesture Recognition framework. Specifically, we treat cross-domain as an unsupervised clustering problem, aiming to ensure that features within local neighborhoods exhibit similar prediction results while those farther apart display different prediction outcomes in the feature space. We theoretically analyze the effect of enhanced prediction consistency between neighbor points extracted from gestures on generalization error. Furthermore, we employ an attraction-dispersion network to strengthen prediction consistency among closely located features in the feature space while reducing it for distantly located features. To mitigate noise introduced during nearest neighbor sample selection in the feature space (where predictions may not align with the input sample’s prediction), we progressively improve nearby sample feature aggregation by estimating uncertainty to reweight local neighborhood predictions. Finally, extensive experiments are conducted on the Widar 3.0 and XRF55 datasets and the results show our proposed framework outperforms most cross-domain methods. © 2014 IEEE.

关键词： Differential privacy

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Blockchain adoption or contingent sourcing? Advancing food supply chain resilience in the post-pandemic era

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Frontiers of Engineering Management 2023年第1期10卷 107-120页

作者： Xiutian SHI Siru CHEN Xiaofan LAI School of Economics and Management Nanjing University of Science and TechnologyNanjing 210094China Institute of Big Data Intelligent Management and Decision College of ManagementShenzhen UniversityShenzhen 518060China

In the post-pandemic era, food supply chains and firms therein are facing unprecedented severe challenges, because once infection is detected, numerous products must be recalled or abandoned, and both suppliers and retailers in the supply chain suffer enormous loss. To survive under the pandemic, retailers have adopted different sourcing strategies, such as contingent sourcing, which, in turn, affect the upstream suppliers and hence the resilience of the whole supply chain. With the rapid development of digital technologies, retailers nowadays can utilize blockchain as a reliable and efficient way to reduce product risk and hence advance the resilience of food supply chains by improving product traceability and inspection accuracy, and making sourcing transparent. In this paper, we develop a game-theoretic model to investigate the interrelation between the retailer’s decisions on blockchain adoption and sourcing strategies. We consider that a retailer originally orders from a risky supplier while conducting an imperfect inspection to detect infected products before selling. The retailer may speculatively keep on ordering from the risky supplier or adopt contingent sourcing by ordering from an alternative safe supplier. The retailer also has an option to implement blockchain to improve the inspection accuracy and product traceability. We derive the optimal retail prices under different sourcing strategies with and without blockchain adoption and then analyze the incentives for sourcing strategy and blockchain adoption. Then, we identify the conditions of an all-win situation for food retailer, supplier, supply chain resilience, and consumers with/without government subsidy. Finally, we extend to consider the situation that some consumers have health-safety concerns and preferences for blockchain adoption.

关键词： food supply chain blockchain contingent sourcing supply chain resilience

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GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerators 31

GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerato...

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31st IEEE International Symposium on High Performance Computer Architecture, HPCA 2025

作者： Yang, Siling He, Shuibing Wang, Wenjiong Yin, Yanlong Wu, Tong Chen, Weijian Zhang, Xuechen Sun, Xian-He Feng, Dan The State Key Laboratory of Blockchain and Data Security Zhejiang University China Zhejiang Lab China Institute of Blockchain and Data Security China Zhejiang Key Laboratory of Big Data Intelligent Computing China Washington State University Vancouver United States Illinois Institute of Technology United States Huazhong University of Science and Technology China Wuhan National Laboratory for Optoelectronics China

ISBN: (纸本)9798331506476

Graph convolutional networks (GCNs) are popular for a variety of graph learning tasks. ReRAM-based processing-in-memory (PIM) accelerators are promising to expedite GCN training owing to their in-situ computing capability. However, existing accelerators can be severely underutilized even with pipelines, due to the oversight of the skewed execution times of various GCN stages and the ignorance of skewed degrees of graph vertices. In this work, we propose GOPIM, a GCN-oriented pipeline optimization for PIM accelerators to expedite GCN training. First, GOPIM proposes an ML-based scheme that allocates crossbar resources to the most needed stages to streamline the overall pipeline. Second, GOPIM utilizes a selective vertex updating technique that evenly distributes vertices on crossbars by interleaved mapping. These techniques collectively reduce the overall execution time without losing much accuracy. We also provide a practical architecture design for GOPIM. Our experimental results show that, GoPIM achieves up to 191 × speedup and 16.1 × energy saving, compared to the state-of-the-art work. © 2025 IEEE.

关键词： Graph neural networks

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An Overview Of Multimodal Brain Tumor MR Image Segmentation Methods 3

An Overview Of Multimodal Brain Tumor MR Image Segmentation ...

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3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Jiao, Chunxia Yang, Tiejun School of Information Science and Engineering Henan University of Technology Zhengzhou China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Magnetic resonance imaging (MRI) is one of the most common imaging methods in the diagnosis and treatment of brain tumors. Multimodal brain tumor MRI automatic segmentation is significant for assisting the diagnosis and treatment of brain tumor diseases. In this paper, we first introduce the background and status quo of multimodal brain tumor MRI segmentation. Then, we divide the multimodal brain tumor MRI segmentation methods in recent years into three ways according to the network architecture. The basic theory, development process, research status, advantages and disadvantages of these three network architectures are also summarized respectively. In addition, we also introduce several publicly available datasets and evaluation metrics of brain tumor segmentation. Finally, we summarize and prospect the segmentation methods of multimodal brain tumor MRI. This article mainly introduces the recent research progress of multimodal brain tumor MRI segmentation, which will provide some help for researchers and practitioners in this domain. © 2023 SPIE.

关键词： Image segmentation

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Finite-Time Sampled-data Control for Networked Dynamic System 42

Finite-Time Sampled-Data Control for Networked Dynamic Syste...

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42nd Chinese Control Conference, CCC 2023

作者： Zhang, Dan Wang, Hui Chen, Xuefeng Xue, Xiaojuan School of Science Yanshan University Hebei Qinhuangdao066004 China Aliyun School of Big Data Changzhou University Jiangsu Changzhou213164 China College of Data Science Taiyuan University of Technology Shanxi Taiyuan030024 China

ISBN: (纸本)9789887581543

This paper studies the finite-time stability of the networked dynamic system under sampled-data control. By taking the interconnected structure into consideration and employing input-delay approach, tractable conditions that only depend on individual subsystem are proposed to ensure the finite-time stability of the networked dynamic system. By utilizing the Finsler's lemma, the existence conditions of the decentralized state feedback controller are derived. An example of interconnected seesaw-cart system is given to illustrate the effectiveness of the proposed method. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： State feedback

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Predicting Microsatellite Instability from Pathological Images Using Self-Supervised Learning Methods

Predicting Microsatellite Instability from Pathological Imag...

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2023 International Conference on Advances in Artificial Intelligence and Applications, AAIA 2023

作者： Ruan, Ruyun Huang, Shoujin Su, Liyilei Khan, Rashid Huang, Bingding College of Big Data and Internet Shenzhen Technology University Shenzhen China College of Health Science and Environmental Engineering Shenzhen Technology University Shenzhen China

ISBN: (纸本)9798400708268

Microsatellite instability (MSI) is a phenotypic consequence of defect deoxyribonucleic acid (DNA) mismatch repair function. The MSI status is a crucial biomarker in assessing a cancer patient’s response to immunotherapy. Polymerase Chain Reaction (PCR) testing or immunohistochemical detection were the gold-standard methods for determining MSI. Recent studies have demonstrated that deep learning approaches can rapidly and cost-effectively determine MSI status from standard pathological images. This study employed several self-supervised techniques, including ResNest, Transformer, and other deep learning architectures, on The Cancer Genome Atlas (TCGA) pathological image datasets to detect MSI status. The experimental results demonstrated that all methods achieved an area under the curve (AUC) exceeding 0.90. These results surpass the performance of a single traditional network structure on the datasets, thereby validating the superiority of the self-supervision training methods to predict MSI status from pathological images. Our proposed self-supervised methods effectively bridge the knowledge gap between natural and medical image-pretrained models. © 2023 Copyright held by the owner/author(s).

关键词： Polymerase chain reaction

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