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Re-HGNM: a repeat aware hypergraph neural machine for session-based recommendation

Neural Computing and Applications 2024年第17期36卷 9661-9674页

作者： Peng, Yuze Xu, Shengjun Huang, Yihua School of Electronics and Information Technology Sun Yat-Sen University Guangdong Guangzhou510006 China The Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electronics and Information Technology Sun Yat-Sen University Guangdong Guangzhou510006 China Southern Marine Science and Engineering Guangdong Laboratory Guangdong Zhuhai519080 China

Hypergraph neural network (HGNN) for session-based recommendation (SBR) is quite rare but has been rewarded with promising performance. However, under the hypergraph framework, no works have emphasized the importance of repeat consumption, which is an important consumption pattern in SBR (e.g., people watch movies or listen to music repeatedly over time) and limits the performance of SBR in a beyond graph topology manner. For filling this gap and better recommendation performance, this paper incorporates repeat consumption into HGNNs and proposes a new recommendation machine, named Re-HGNM, which leverages the powerful expressiveness of HGNNs in representation learning and takes repeat consumption into account by considering the relationship between repeat behaviors and modeling repeat recommendations. Specially, in the encoder of Re-HGNM, we process sessions as hyperedges, in which the repeated behaviors are merged, to constitute hypergraph that conveys not only the complex relations between unique items but the relationships between repeat behaviors. After learning item representation by hypergraph convolution, a repeat-explore module is employed to determine whether to make repeat recommendation. In repeat mode, the next click must be in old items;thereby, the shrunk prediction space makes it easier to find the target item. Therefore, in the decoding stage, two decoders correspondingly work under two separate modes. Finally, compared with the current best baseline, Re-HGNM relatively improved P@20 by 58.37%, 8.70%, 1.28%, and 1.16% and MRR@20 by 25.52%, 10.31%, 10.76%, and 2.76% on the Tmall, RetailRocket, Nowplaying, and Diginetica, to be the current state-of-the-art model for SBR. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023.

关键词： Topology

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C2P-Net: Comprehensive Depth Map to Planar Depth Conversion for Room Layout Estimation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第07期47卷 5350-5367页

作者： Zhang, Weidong Zhou, Mengjie Cheng, Jiyu Liu, Ying Zhang, Wei Xi'an University of Posts & Telecommunications School of Communications and Information Engineering China Xi'an Key Laboratory of Image Processing Technology and Applications for Public Security China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China

Room layout estimation seeks to infer the overall spatial configuration of indoor scenes using perspective or panoramic images. As the layout is determined by the dominant indoor planes, this problem inherently requires the reconstruction of these planes. Some studies reconstruct indoor planes from perspective images by learning pixel-level or instance-level plane parameters. However, directly learning these parameters has the problems of susceptibility to occlusions and position dependency. In this paper, we introduce the Comprehensive depth map to Planar depth (C2P) conversion, which reformulates planar depth reconstruction into the prediction of a comprehensive depth map and planar visibility confidence. Based on the parametric representation of planar depth we propose, the C2P conversion is applicable to both panoramic and perspective images. Accordingly, we present an effective framework for room layout estimation that jointly learns the comprehensive depth map and planar visibility confidence. Due to the differentiability of the C2P conversion, our network autonomously learns planar visibility confidence by constraining the estimated plane parameters and reconstructed planar depth map. We further propose a novel approach for 3D layout generation through sequential planar depth map integration. Experimental results demonstrate the superiority of our method across all evaluated panoramic and perspective datasets. © 1979-2012 IEEE.

关键词： Layout Three Dimensional Displays Image Reconstruction Estimation Cameras Floors Cognition Transforms Training Semantics Room Layout Estimation Monocular Depth Estimation Planar Depth Reconstruction Planar Visibility Confidence Function Of Depth Comprehensive Function Room Layout Depth Planes Depth Conversion Layout Estimation Sequence Mapping Integration Of Sequences Joint Learning Panoramic Images Planar Graphs Parameter Plane Visible Light Validation Set Intersection Over Union Probability Function Training Images Weight Coefficient Output Channels Depth Values Adjacent Walls Planar Regions Semantic Labels Pixel Coordinates Dice Loss Non Planar Surfaces Depth Estimation Global Vision Camera Pose Camera Coordinate System

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An RCG-based study of aspect-level sentiment analysis for hybrid attention 3

An RCG-based study of aspect-level sentiment analysis for hy...

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3rd International Conference on Machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Lu, Baoqing Yu, Hongzhi Wan, Fucheng Min, Chen Wei, Bin Fen, Yulin Key Laboratory of Linguistic and Cultural Computing Ministry of Education Gansu Lanzhou730000 China Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Gansu Lanzhou730000 China Lanzhou technology and business college Gansu Lanzhou730000 China

ISBN: (纸本)9781510681880

Aspect-based sentiment analysis (ABSA) is a crucial granular task within sentiment analysis, focusing on the precise identification of sentiment orientations for specific aspects within text. Recognizing that identical context words can express opposing sentiment polarities in different situations, it's essential to delve into the nuanced interactions between target and context words. This study introduces an RCG-based Hybrid Attention Network, a novel architecture that adeptly utilizes lexical attention mechanisms to extract lexical features and fortify the relationship between aspects and their corresponding target words. To assess the efficacy of our proposed approach, we conducted experiments on a well-known public dataset. The results show a significant 3.37% enhancement in accuracy and a 1.38% improvement in Macro-F1 scores compared to related methods, affirming the superiority of our technique in enhancing the performance of aspect-level sentiment analysis. © 2024 SPIE.

关键词： Economic and social effects

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Four development stages of collective intelligence

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Frontiers of information technology & Electronic Engineering 2024年第7期25卷 903-916页

作者： Renbin XIAO School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Image Information Processing and Intelligent Control Ministry of EducationWuhan 430074China

The new generation of artificial intelligence(AI)research initiated by Chinese scholars conforms to the needs of a new information environment changes,and strives to advance traditional artificial intelligence(AI 1.0)to a new stage of AI *** one of the important components of AI,collective intelligence(CI 1.0),i.e.,swarm intelligence,is developing to the stage of CI 2.0(crowd intelligence).Through in-depth analysis and informative argumentation,it is found that an incompatibility exists between CI 1.0 and CI ***,CI 1.5 is introduced to build a bridge between the above two stages,which is based on biocollaborative behavioral *** 1.5 is the transition from CI 1.0 to CI 2.0,which contributes to the compatibility of the two ***,a new interpretation of the meta-synthesis of wisdom proposed by Qian Xuesen is *** meta-synthesis of wisdom,as an improvement of crowd intelligence,is an advanced stage of bionic intelligence,i.e.,CI *** is pointed out that the dual-wheel drive of large language models and big data with deep uncertainty is an evolutionary path from CI 2.0 to CI 3.0,and some elaboration is *** a result,we propose four development stages(CI 1.0,CI 1.5,CI 2.0,and CI 3.0),which form a complete framework for the development of *** different stages are progressively improved and have good *** to the dominant role of cooperation in the development stages of CI,three types of cooperation in CI are discussed:indirect regulatory cooperation in lower organisms,direct communicative cooperation in higher organisms,and shared intention based collaboration in *** division is the main form of achieving cooperation and,for this reason,this paper investigates the relationship between the complexity of behavior and types of labor ***,based on the overall understanding of the four development stages of CI,the future development direction and research issues of CI are explored.

关键词： Collective intelligence Meta-synthesis of wisdom Incompatibility Labor division Cooperative behavior Collective intelligence emergence Large language model

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Multimodal emotion recognition based on multilevel acoustic and textual information 5

Multimodal emotion recognition based on multilevel acoustic ...

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5th International Conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Zhou, Ya Xing, Yuyi Huang, Guimin Guo, Qingkai Deng, Nanxiao School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (纸本)9781510668621

The study and application of multimodal emotion recognition have gained significant popularity in recent years, representing one of the challenging tasks in the field of affective computing. We propose a multimodal speech emotion recognition model that utilizes multiple acoustic and textual information layers. This model incorporates transcribed textual data to complement speech data and enable accurate emotion recognition. In the unimodal model, we employ AlexNet, BiGRU, and HuBERT to extract multi-layer acoustic feature information from speech, and the RoBERTa encoder to extract text features. Additionally, we perform fusion between speech and text by utilizing the co-attentive mechanism to extract complementary information across modalities and eliminate inter-modality noise. This process ultimately enhances the emotional representation of the target modality. Finally, the fused features are utilized to predict the emotion category. Our model achieved a weighted sentiment recognition accuracy of 77.41% and an unweighted accuracy of 78.66%. © 2023 SPIE.

关键词： Emotion Recognition

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Distracted driving behaviour recognition based on transfer learning and model fusion

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International Journal of Wireless and Mobile Computing 2023年第2期24卷 159-168页

作者： Luo, Guantai Xiao, Wanghui Chen, Xinwei Tao, Jin Zhang, Chentao -HIT Research Institute of Engineering and Technology Quanzhou Fujian China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou Fujian China Department of Instrumental and Electrical Engineering Xiamen University Fujian Xiamen China

In the recognition of distracted driving behaviour, traditional manual feature extraction is subjective and complex;single deep convolutional network also has problems such as insufficient generalisation performance and stability. To solve the above problems, this paper proposes a distracted driving behaviour recognition method based on transfer learning and model fusion. First, based on the transfer learning method, the deep convolutional neural network models ResNet18 and ResNet34 are used to extract the features of some images, respectively. Furthermore, the pre-trained model is fine-tuned to obtain four deep convolutional neural network models. Finally, the four network models are fused by stacking method, using 5-fold cross-validation method to reduce over-fitting. Experimental results show that the recognition accuracy of distracted driving behaviour after model fusion reaches 95.47%. The fusion model has higher model generalisation performance and recognition accuracy, which can provide certain technical support for the research of distracted driving behaviour recognition. © 2023 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

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Nucleation growth quenching for superior cluster catalysts

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Nano Research 2022年第9期15卷 7933-7939页

作者： Ruyue Wang Feng Cheng Yonggang Wang Dongyu Fan Bohan Deng Yuanzheng Long Haolin Tang Kai Huang Zhaowei Qu Ming Lei Binghui Ge Hui Wu State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China State Key Laboratory of Information Photonics and Optical Communications School of ScienceBeijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China Beijing Key Laboratory of Space-ground Interconnection and Convergence Beijing University of Posts and Telecommunications(BUPT)Beijing 100876China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of EducationInstitutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China

Cluster catalysts are rapidly growing into an important sub-field in heterogeneous catalysis,owing to their distinct geometric structure,neighboring metal sites,and unique electronic *** the thermodynamics and kinetics of the formation of nanoparticles have been largely investigated,the precise synthesis of clusters in wet chemical methods still faces great *** the study,a quenching strategy of asymmetric temperature in solution for the rapid generation of vacancy-defect rich clusters is *** quenching process can be used to synthesize multitudinous metal compound clusters,including metal oxides,fluorides,oxygen-sulfur compounds,and *** oxygen evolution reaction(OER),IrO_(2)clusters with abundant oxygen vacancies were obtained and uniformly dispersed in the *** to commercial IrO_(2),the prepared IrO_(2)cluster can be directly loaded on carbon paper and used as binder-free electrodes,which exhibit higher OER activity and long-term operational stability in alkaline *** quenching strategy provides a simple and efficient method for the synthesis of clusters,which has tremendous potential for industrial-scale preparation and application,especially can be further applied to flow electrochemical generators.

关键词： rapid-chilling cluster structure transition metal compounds binder-free electrode oxygen evolution reaction

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Exploring Invariance Matters for Domain Generalization

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IEEE Transactions on Image processing 2025年 34卷 3336-3351页

作者： Wang, Shanshan He, Houmeng Yang, Xun Liu, Zhipu Zhong, Yuanhong Zhang, Xingyi Wang, Meng Anhui University National Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology Institutes of Physical Science and Information Technology Hefei230601 China University of Science and Technology of China School of Information Science and Technology Department of Electronic Engineering and Information Science Hefei230026 China Chongqing University of Technology School of Computer Science and Engineering Chongqing400054 China Chongqing University School of Microelectronics and Communication Engineering Chongqing400030 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230009 China

Domain generalization (DG) aims to solve the problem of significant performance degradation when target domain data collected from the Out-Of-Distribution (O.O.D). Previous efforts try to exploit invariant features in the source domain through CNN networks. However, inspired by causal mechanisms, we find that the complex spurious-invariant information is still hidden in this view invariant features, and the impact of domain and class discrepancies on extracting invariance has not been effectively mitigated. To alleviate these issues, we propose a self-weighted multi-view mining invariance domain generalization framework (SMIDG). On the one hand, to make up for the insufficiency of traditional single-view convolutional feature extraction networks, we propose to mine features from another frequency view and use the self-adaptive adversarial masks to eliminate some spurious correlations, ensuring causal invariance in the coarse-grained generalization. However, due to inconsistencies in discriminative information between interdomain and intra-domain samples, as well as inter-class and intra-class samples, the coarse-grained elimination of spurious associations does not fully resolve this issue. On the other hand, we also consider the fine-grained generalization from two aspects. Firstly, to better tackle the domain discrepancies, we propose a novel progressive contrastive learning strategy that learns the underlying specific features of samples while gradually mitigating domain discrepancies, thereby ensuring domain invariance in fine-grained generalization. Secondly, due to the issue of feature inconsistency, we adopt a self-adaptive hard sample mining method with information gain to ensure that the model pays more attention on hard disentangled samples, thus maintaining feature invariance. Extensive experiments on five benchmark datasets demonstrate that our method outperforms state-of-the-art approaches. © 1992-2012 IEEE.

关键词： Contrastive learning Feature extraction Frequency-domain analysis Training Data models Data mining Correlation Data augmentation Convolutional neural networks Computer science

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A Transformer-Assisted Cascade Learning Network for Choroidal Vessel Segmentation

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Journal of Computer Science & technology 2024年第2期39卷 286-304页

作者：温阳吴依林毕磊石武祯刘潇骁许毓鹏许迅曹文明冯大淦 Guangdong Provincial Key Laboratory of Intelligent Information Processing School of Electronics and Information EngineeringShenzhen UniversityShenzhen 518060China Institute of Translational Medicine Shanghai Jiao Tong UniversityShanghai 200240China Department of Ophthalmology Shanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai 200080China School of Information Technologies The University of SydneySydney 2006Australia

As a highly vascular eye part,the choroid is crucial in various eye disease ***,limited research has focused on the inner structure of the choroid due to the challenges in obtaining sufficient accurate label data,particularly for the choroidal ***,the existing direct choroidal vessel segmentation methods for the intelligent diagnosis of vascular assisted ophthalmic diseases are still unsatisfactory due to noise data,while the synergistic segmentation methods compromise vessel segmentation performance for the choroid layer segmentation *** cascaded structures grapple with error propagation during *** address these challenges,we propose a cascade learning segmentation method for the inner vessel structures of the choroid in this ***,we propose TransformerAssisted Cascade Learning Network(TACLNet)for choroidal vessel segmentation,which comprises a two-stage training strategy:pre-training for choroid layer segmentation and joint training for choroid layer and choroidal vessel *** also enhance the skip connection structures by introducing a multi-scale subtraction connection module designated as MSC,capturing differential and detailed information ***,we implement an auxiliary Transformer branch named ATB to integrate global features into the segmentation *** results exhibit that our method achieves the state-of-the-art performance for choroidal vessel ***,we further validate the significant superiority of the proposed method for retinal fluid segmentation in optical coherence tomography(OCT)scans on a publicly available *** these fully prove that our TACLNet contributes to the advancement of choroidal vessel segmentation and is of great significance for ophthalmic research and clinical application.

关键词： choroidal vessel segmentation optical coherence tomography(OCT) Transformer-assisted cascade learning retinal fluid segmentation multi-scale feature extraction

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A method for locating noise sources in an industrial environment 24

A method for locating noise sources in an industrial environ...

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6th International Conference on Control and Computer Vision, ICCCV 2024

作者： Tan, H. Wang, D.X. Cao, Y.D. Zhou, C.X. Yang, W.W. Liaoning University of Technology School of Electronics & Information Engineering China Tianjin University of Technology and Education Tianjin Key Laboratory Of Information Sensing And Intelligent Control China

ISBN: (纸本)9798400718045

An improved inverse convolution beamforming algorithm based on compressed matched sample tracking (ICoSaMP-DAMAS) is proposed for real industrial production environments with strong noises such as stamping shops. In order to improve the accuracy of noise source localization, the algorithm firstly decomposes the mutual spectrum matrix of the microphone array by features, and then filters out the possible noise components corresponding to smaller eigenvalues, constructs the higher-order matrix function, and optimizes the inverse convolution beam formation by combining with Tikhonov regularization;secondly, in order to shorten the algorithm’s computation time, the adaptive iterative threshold segmentation is used to preprocess the ambient noise with a smaller sound pressure level;finally, the Then it is improved by using compression perception, and the algorithm of using compressed matched sampling tracking to de-solve the anti-convolution equation (ICoSaMP-DAMAS) is proposed. The experimental results prove the effectiveness of the algorithm. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Eigenvalues and eigenfunctions

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