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51st International Conference on Very Large data Bases, VLDB 2025

作者： Ye, Junhao Li, Jiahui Chen, Lu Mao, Yuren Gao, Yunjun Li, Tianyi Zhejiang University China Zhejiang University Zhejiang Key Laboratory of Big Data Intelligent Computing China Aalborg University Denmark

Selecting a good execution plan can significantly improve the query efficiency of Spark SQL. Several machine learning-based techniques have been proposed to select good execution plans for DBMS, but none of them perform well on Spark SQL due to the following issues.(1) Limited compatibility with Spark SQL: these approaches rely on physical operator enumeration, while Spark SQL doesn’t support it;(2) Unreliable cost estimation: they often select execution plans with poor performance due to inaccurate cost estimation;(3) Time-consuming plan enumeration: they take much time to generate a large number of candidate execution plans in Spark *** overcome these issues, in this paper, we propose LEAP, the first learned query optimizer tailored for Spark SQL, which can be integrated seamlessly into Spark SQL and solves the compatibility issue. Also, to avoid the unreliable cost value estimation, LEAP selects execution plans with an estimation-free method, which directly performs comparisons between the plans. Furthermore, LEAP employs an efficient progressive plan enumeration algorithm with pruning techniques to find better plans with fewer enumerations. Extensive experiments on three public benchmarks show the effectiveness of LEAP. It reduces the end-to-end execution time of the native optimizer by up to 54% and other learned methods by up to 94%. © 2025, VLDB Endowment, All rights reserved.

关键词： Cost benefit analysis

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Tensor Graph Convolutional Network for Dynamic Graph Representation Learning 7

Tensor Graph Convolutional Network for Dynamic Graph Represe...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Wang, Ling Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application For Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China College of Computer and Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9798350363173

Dynamic graphs (DG) represent evolving interactions between entities in various real-world scenarios. Many existing DG representation learning models employ a combination of graph convolutional networks and sequence neural networks to capture spatial-temporal dependencies. However, this hybrid approach often fails to effectively capture the spatial-temporal continuity inherent in DGs. In this paper, we propose a novel approach called the Tensor Graph Convolutional Network (TGCN) to learn DG representations within a unified convolution framework based on tensor products. Our approach is founded on two key principles: (a) representing DG information in tensor form, and (b) leveraging tensor product operations to design a graph convolutional network that simultaneously models spatial and temporal features. Experimental results on three real-world DG datasets demonstrate that our proposed model achieves state-of-the-art performance. © 2024 IEEE.

关键词： Tensors

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Gradient-Based Differential kWTA Network With Application to Competitive Coordination of Multiple Robots

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1452-1463页

作者： Mei Liu Xiaoyan Zhang Mingsheng Shang Long Jin School of Information Science and Engineering Lanzhou UniversityLanzhou 730000 State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining 810016China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Shenzhen Research Institute Lanzhou UniversityShenzhen 518063China

Aiming at the k-winners-take-all(kWTA)operation,this paper proposes a gradient-based differential kWTA(GDk WTA)*** obtaining the network,theorems and related proofs are provided to guarantee the exponential convergence and noise resistance of the proposed GD-kWTA ***,numerical simulations are conducted to substantiate the preferable performance of the proposed network as compared with the traditional ***,the GD-k WTA network,backed with a consensus filter,is utilized as a robust control scheme for modeling the competition behavior in the multi-robot coordination,thereby further demonstrating its effectiveness and feasibility.

关键词： Consensus filter k-winners-take-all(kWTA) multirobot coordination noise resistance

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A Feature Fusion Network with Multiscale Adaptively Attentional for Object Detection in Complex Traffic Scenes

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年

作者： Cai, Fei Qu, Zhong Yin, Xuehui Chongqing Key Laboratory of Computational Intelligence The Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education The Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

Currently, small object detection in complex traffic scenarios remains a major challenge due to variations in object shape, scales, and external environment, as well as a high inter-class similarity. Although the latest algorithms achieve high accuracy, the results for small objects are still unsatisfactory. To obtain satisfactory small object detection results, the YOLOv8 algorithm is used to construct a feature fusion network with multiscale adaptively attentional (MAAFFNet). Firstly, the C2f module is modified to replace the simple summation operation with Multiscale Iterative Attentional Feature Fusion (MSIAFF), which enhances the ability of multi-scale aggregation of the C2f module and suppresses the interference of irrelevant information, resulting in a richer semantic feature map. Secondly, the adaptive spatial feature fusion (ASFF) module is introduced to deal with the feature maps of different scales, allocate the weights of spatial features of different levels, and enhance the consistency of semantic information between each layer of the feature maps, so as to improve the detail richness of the network. Finally, to achieve more accurate classification of small objects, a Slide loss function is introduced to achieve object classification and localization. Our proposed algorithm has been extensively experimented on KITTI, COCO-traffic and VOC 2012 datasets, respectively, comparing the proposed method to popular two-stage and single-stage object detection algorithms, the proposed method performs well in terms of accuracy. © 2024 IEEE.

关键词： Object recognition

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Multi-View Fusion Networks for Multimodal Fake News Detection 11

Multi-View Fusion Networks for Multimodal Fake News Detectio...

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11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Cui, Wei Li, Pu Zhang, Xuerui College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Chongqing Institute of Green and Intelligent Technology Chongqing Key Laboratory of Big Data and Intelligent Computing Chinese Academy of Sciences Chongqing China

ISBN: (纸本)9798350333664

The proliferation of multimedia fake news misleads social opinion, damages social harmony, and seriously challenges the authority of news media. Current multimodal fake news detection methods focus on extracting better unimodal features using pre-trained models and developing complex networks for visual and textual feature fusion, mostly ignoring social contextual information. In the text, we present a novel Multi-View Fusion Network (MVFN) for fake news detection, which attempts to achieve fusion learning of heterogeneous multimodal features. The model not only explores cross-modal graph interactions between visual regions and sentence words, but also investigates the effect of social context on feature learning. In addition, we consider the alignment relationship of features in both perspectives for better feature fusion. Experimental results on Chinese and English datasets show that the proposed model outperforms current multimodal detection approaches. © 2023 IEEE.

关键词： fake news graph multi view multimodal

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Glare-SNet: Unsupervised Glare Suppression Balance Network 27th

Glare-SNet: Unsupervised Glare Suppression Balance Network

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Pei Zuo, Chengyu Wei, Wangjuan Pan, Xiaoying Wang, Zhanhao School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an China Shaanxi Key Laboratory of Intelligent Media Computing and interaction Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an China

ISBN: (纸本)9783031781681

In light of the problems associated with glare and halo effects in low-light images, as well as the inadequacy of existing processing algorithms in handling details, a glare suppression balance network based on unsupervised learning has been designed and implemented in this study. A network that perceives brightness through light decomposition has been proposed, and an illumination learning network has been designed. The idea of integration has been employed to form an unsupervised glare suppression balance network, aiming to tackle the issues of glare and the accompanying halo effects. Through objective and subjective evaluations of real images, it has been proven that the method introduced in this paper surpasses other methods in suppressing glare and enhancing the overall quality of low-light images. Furthermore, it has been shown to exhibit good performance and effectiveness in target detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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A Hybrid Multi-attribute Group Decision-Making Method Using Normal Cloud Model and Multi-granularity Information 11th

A Hybrid Multi-attribute Group Decision-Making Method Using ...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Jiang, Zhihui Zhang, Qinghua Wang, Yutai School of Science Chongqing University of Posts and Telecommunications Chongqing 400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing 400065 China

ISBN: (纸本)9783031927430

In multi-attribute group decision making (MAGDM), it is important to deal with heterogeneous data and integrate the evaluation information from different decision makers (DMs). To more accurately capture the uncertain information in the real decision-making environment, a hybrid MAGDM method using normal cloud model (NCM) and multi-granularity information is proposed. First, utilizing the theta scaling method, multi-granularity linguistic term sets are established. Considering the exact numbers, interval numbers, multi-granularity linguistic terms, and multi-granularity linguistic expressions, the concept of multi-granularity information is further expanded. Second, these data with different granularity are uniformly converted into NCMs, which can simultaneously preserve both magnitude information and uncertainty information. Third, based on the internal consensus index (ICI) and the external consensus index (ECI), a novel group analytic hierarchy process (GAHP) method is proposed for calculating attribute weights. Finally, NCM-extended VIKOR method is proposed for ranking the alternatives. The flexibility and superiority of the proposed method are confirmed through a case study and comparisons with previous methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Group analytic hierarchy process Multi-granularity information Multi-granularity linguistic term sets Normal cloud model

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4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1485-1499页

作者： Gang Wang Weisheng Li Mingliang Zhou Haobo Zhu Guang Yang Choon Hwai Yap Chongqing Key Laborotory of Image Rocognition Chongqing University of Posts and TelecommunicationsChongqingChina School of Computing and Data Engineering NingboTech UniversityNingboChina Department of Bioengineering Imperial College LondonLondonUK Key Laboratory of Cyberspace Big Data Intelligent Security(Chongqing University of Posts and Telecommunications) Ministry of EducationChongqingChina School of Computer Science Chongqing UniversityChongqingChina University of Oxford OxfordUK Cardiovascular Research Centre Royal Brompton HospitalLondonUK National Heart and Lung Institute Imperial College LondonLondonUK

Hypoplastic left heart syndrome(HLHS)is a rare,complex,and incredibly foetal congenital heart *** decrease neonatal mortality,evolving HLHS(eHLHS)in pregnant women should be critically diagnosed as soon as ***,diagnosis is currently heavily dependent on skilled medical professionals using foetal cardiac ultrasound images,making it difficult to rapidly and easily examine for this ***,the authors propose a cost-effective deep learning framework for rapid diagnosis of eHLHS(RDeH),which we have named ***,the framework implements a coarseto-fine two-stage detection approach,with a structure classification network for 4D human foetal cardiac ultrasound images from various spatial and temporal domains,and a fine detection module with weakly-supervised localisation for high-precision nidus localisation and physician *** experiments extensively compare the authors’network with other state-of-the-art methods on a 4D human foetal cardiac ultrasound image dataset and show two main benefits:(1)it achieved superior average accuracy of 99.37%on three categories of foetal ultrasound images from different cases;(2)it demonstrates visually fine detection performance with weakly supervised *** framework could be used to accelerate the diagnosis of eHLHS,and hence significantly lessen reliance on experienced medical physicians.

关键词： 4D deep learning fetal cardiac ultrasound hypoplastic left heart syndrome weakly-supervised learning

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RGCNU: Recurrent Graph Convolutional Network With Uncertainty Estimation for Remaining Useful Life Prediction

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IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1640-1642页

作者： Qiwu Zhu Qingyu Xiong Zhengyi Yang Yang Yu School of Big Data&Software Engineering Chongqing UniversityChongqing 400044 Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of EducationChongqing 400044China School of Biology and Engineering Guizhou Medical UniversityGuiyang 550025China

Dear Editor,This letter focuses on the problem of remaining useful life(RUL)prediction of equipment. Existing graph neural network(GCN)-based approaches merely provide the point estimation of RUL. However,the estimated RUL often varies widely due to the model parameters and the noise in data. It is important to know the uncertainty in predictions for reliable risk analysis and maintenance decision *** map the relationship between noisy condition monitoring data and RUL with uncertainty.

关键词： letter estimation prediction

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AMHF-TP:Multifunctional therapeutic peptides prediction based on multi-granularity hierarchical features

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Quantitative Biology 2025年第1期13卷 127-141页

作者： Shouheng Tuo YanLing Zhu Jiangkun Lin Jiewei Jiang School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’anChina Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’anChina Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’anChina School of Electronic Engineering Xi’an University of Posts and TelecommunicationsXi’anChina

Multifunctional therapeutic peptides(MFTP)hold immense potential in diverse therapeutic contexts,yet their prediction and identification remain challenging due to the limitations of traditional methodologies,such as extensive training durations,limited sample sizes,and inadequate generalization *** address these issues,we present AMHF-TP,an advanced method for MFTP recognition that utilizes attention mechanisms and multi-granularity hierarchical features to enhance *** AMHF-TP is composed of four key components:a migration learning module that leverages pretrained models to extract atomic compositional features of MFTP sequences;a convolutional neural network and selfattention module that refine feature extraction from amino acid sequences and their secondary structures;a hypergraph module that constructs a hypergraph for complex similarity representation between MFTP sequences;and a hierarchical feature extraction module that integrates multimodal peptide sequence *** with leading methods,the proposed AMHF-TP demonstrates superior precision,accuracy,and coverage,underscoring its effectiveness and robustness in MFTP *** comparative analysis of separate hierarchical models and the combined model,as well as with five contemporary models,reveals AMHFTP’s exceptional performance and stability in recognition tasks.

关键词： deep learning hypergraph multifunctional therapeutic peptides multi-granularity hierarchical features

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