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Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Academic paper recommendation aims to provide users with personalized paper resources. Collaborative filtering methods face the problems of highly sparse data and lack of negative samples. Considering the above challenges, an academic paper recommendation algorithm TAGAN(title and abstract GAN) which incorporated fine-grained semantic features was presented. Firstly, based on titles and abstracts provide abundant semantic features, convolutional neural networks (CNN) was used to extract the global features of the titles, a two-layer long and short-term memory network (LSTM) was built to model abstract words separately. At the same time, the attention mechanism was proposed to associate the title and the abstract semantically. Then, the semantic features of the paper were integrated into the recommendation framework based on generative adversarial network (GAN). The generative model will fit the user's interest preferences and can effectively replace the negative sampling process. Finally, through the experimental comparison on the public dataset, TAGAN is better than the baseline models in all indicators, which verifies the effectiveness of TAGAN. [ABSTRACT FROM AUTHOR]

Automatic segmentation of retinal vessels plays an important role in computer-aided diagnosis of ophthalmology and cardiovascular diseases. Attention mechanism can improve the efficiency and accuracy of image feature extraction in classical neural network models, so attention mechanism is widely used in retinal vessel segmentation models. This paper firstly reviews the commonly used datasets and evaluation metrics for retinal vessel segmentation, subsequently, attention mechanisms are categorized into two types: selective attention mechanisms and self-attention mechanisms, based on their working principles. Meanwhile, according to the data domain of computer vision tasks, attention methods are divided into three categories: channel attention, spatial attention, and mixed attention. Combined with the task of retinal vessel segmentation, the paper highlights the specific applications of representative attention models of these three types and conducts performance comparisons and evaluations of relevant models. Finally, the problems of attention mechanism and the development trend in the future are discussed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

User preference modeling is a key factor in improving the quality of personalized news recommendations. Existing researches usually model the task as click-through rate estimation tasks, starting from the user' s explicit feedback signal to construct an interest representation. However, due to the lack of explicit feedback signals and the variety of user interests, current news recommendation methods often have the problem of data sparseness and information cocoons. This paper proposes a news recommendation algorithm based on implicit feedback signals and multi-dimensional interests. By introducing implicit feedback signals such as user exposure unclicked, the data sparsity problem in modeling the recommendation model is alleviated, and a comparative attention mechanism is proposed to model the fusion of user clicked and unclicked news. Besides, this paper also proposes user dynamic interest modeling based on candidate news perception and contrast learning modeling of dynamic and static interests. Multi-dimensional user interests achieve rich and dynamic user preference accurate localization. This study conducts extensive experiments on a real dataset. Three evaluation metrics and a variety of performance tests are used to compare with other baseline methods to verify that the proposed model outperforms other methods. [ABSTRACT FROM AUTHOR]

It is very important for autonomous driving vehicles to accurately detect objects such as vehicles and pedestrians on the road in real time. Aiming at the problems of missed detection and poor detection effect of small targets in the autonomous driving scene, this paper proposes an automatic driving target detection algorithm that improves the YOLOv7 algorithm. Firstly, it modifies the modules in the network to expand the receptive field, reduces the size of the receptive field module, and improves the speed of the model and enhances the ability to extract image feature information. Secondly, the paper introduces the BRA attention mechanism at the output of the backbone network to improve the model's ability to small target objects. Finally, it replaces the original CIOU loss function of the algorithm with the EIOU loss function to minimize the difference between the height and width of the predicted frame and the real frame, and speeds up the convergence of the model while achieving better positioning results. The experimental results show that: on the KITTI dataset, when the improved YOLOv7 algorithm performs target detection, its mAP reaches 94.7%, which is 3.1 percentage points higher than the original YOLOv7 algorithm, and it has achieved higher detection accuracy in small target object detection. It effectively improves the model's detection effect on small targets. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Transformer is a revolutionary neural network architecture initially designed for natural language processing. However, its outstanding performance and versatility have led to widespread applications in the field of computer vision. While there is a wealth of research and literature on Transformer applications in natural language processing, there remains a relative scarcity of specialized reviews focusing on low-level visual tasks. In light of this, this paper begins by providing a brief introduction to the principles of Transformer and analyzing several variants. Subsequently, the focus shifts to the application of Transformer in low-level visual tasks, specifically in the key areas of image restoration, image enhancement, and image generation. Through a detailed analysis of the performance of different models in these tasks, this paper explores the variations in their effectiveness on commonly used datasets. This includes achievements in restoring damaged images, improving image quality, and generating realistic images. Finally, this paper summarizes and forecasts the development trends of Transformer in the field of low-level visual tasks. It suggests directions for future research to further drive innovation and advancement in Transformer applications. The rapid progress in this field promises breakthroughs for computer vision and image processing, providing more powerful and efficient solutions for practical applications. [ABSTRACT FROM AUTHOR]

Remote sensing scene classification is a hot research topic in the field of computer vision, and it is of great significance to semantic understanding of remote sensing images. At present, remote sensing scene classification methods based on deep learning occupy a dominant position in this field. However, it suffers from the lack of samples and poor model generalization ability in actual application scenarios. Therefore, this paper proposes a few-shot remote scene classification method based on attention mechanism, and designs a structure of dual- branches similarity measurement. This method is based on the meta-learning training strategy to divide the dataset into tasks. At the meantime, the input images are divided into blocks in order to preserve the feature distribution in the remote sensing image. Then the lightweight attention module is introduced into the feature extraction network to reduce the risk of overfitting and ensure the acquisition of discriminative features. Finally, based on earth mover's distance (EMD), a dual-branches similarity measurement module is added to improve the discriminative ability of the classifier. The results show that compared with the classic smallsample learning method, the few-shot remote scene classification method proposed in this paper can significantly improve the classification performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To eliminate the defects of incomplete detection and high false detection rate caused by insignificant pedestrian target features, dense small targets and complex background in infrared images, this paper proposes an infrared pedestrian target detection algorithm based on improved YOLOv7. Firstly, the original Spatial Pyramid Pooling (SPP) module is replaced by the Channel Attention based Spatial Pyramid Pooling (CASPP) module based on the YOLOv7-tiny model, so that the model could pay more attention to the extraction of pedestrian features; then, the convolution module CBM based on the Meta-ACON activation function is introduced, which further suppressed the background noise and preserved the details of the pedestrians; finally, an alpha fusion data enhancement method is proposed to enrich the diversity of samples and improve the stability of the model in complex environments. The validation based on the FLIR dataset shows that the proposed method improves the accuracy by 3% and reduces the computation by 38% compared with the YOLOv7-tiny algorithm, which is more suitable for infrared pedestrian target detection scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of East China University of Science & Technology is the property of Journal of East China University of Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Chinese medical Q&A is easily affected by the noise of medical-specific terminology, making it more challenging than open-domain Q&A. Previous studies on Chinese medical Q&A mainly relied on character-level fine-grained information, neglecting word-level coarse-grained information that carries more semantic information. In addition, introducing external medical knowledge graph can further enrich the fine-grained information in Q&A sentences, but most existing studies usually adopt a simple way of joint representation of sentences and external knowledge. Therefore, this paper proposes a Chinese medical Q&A matching model based on multi-granularity semantic information and knowledge graph (CMQA-MGSI). The model employs a Lattice network to select the most relevant character-level and word-level sequences from the Q&A sentences, and leverages Word2Vec and BERT to enhance the semantic information; to better exploit the external domain knowledge, a dual-channel attention mechanism is devised to capture the multi-angle knowledge representations between the Q&A sentences and the entity embeddings and relation embeddings in the knowledge graph. Experiments on the cMedQA1.0 and cMedQA2.0 datasets demonstrate that the proposed model outperforms existing Chinese medical Q&A matching models. [ABSTRACT FROM AUTHOR]

In order to improve the spatial resolution of brain magnetic resonance imaging (MRI) images, the super resolution (SR) reconstruction method based on deep convolutional neural network (CNN) has achieved remarkable results. Increasing network depth or width usually can expand the receptive field of the network to improve the quality of reconstruction, but it is difficult to train the network due to the large parameter quantity and huge computing requirements. Meanwhile, the network does not pay enough attention to the medium and high frequency features of the image, which affects the quality of reconstruction. To solve the above problems, this paper proposes a cascaded channels-space attention and encode-decode network for 3D-MRI image SR reconstruction. Firstly, feature extraction is carried out in the low resolution space, and the image features are extracted by the symmetric connected encoder-decoder with dilated convolution to alleviate the checkerboard artifacts. Secondly, a serial connected channel-space attention module is constructed to capture the interdependence between the feature channels by channel attention, and the spatial attention is increased to assign weight to information of different positions, effectively enhancing the network' s learning of medium and high frequency information. Finally, subpixel convolution is used to sample the feature map to the target image size and meanwhile reduce memory consumption. Experimental results on public Kirby21 and BraTS datasets show that the proposed method is superior in both quantitative peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and subjective visual quality when compared with traditional SR algorithms and mainstream CNN-based SR algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The study of generic knowledge extraction methods in industrial manufacturing is of great significance for real- izing the automated construction of industrial knowledge bases. Aiming at the problem that industrial ontology definition requires a large amount of labour cost and expert experience for guidance, a semi-automatic ontology construction method is designed based on the sememe analysis, and finally the definition of ontology, ontology class, cascade and relationship is completed, using automobile manufacturing fault repair data as an example. Subsequently, in order to solve the problems of relationship nesting and cascade model error propagation that exist in relationship extraction in the industrial domain, a semantic- aware relationship extraction model is designed. In the three links of potential relationship mining, subject extraction and object extraction in this model, different leading interrogatives are spliced based on reading comprehension methods to obtain sentence codes applicable to different links.In order to make use of the a priori knowledge of the subject, the attention mechanism is incorporated into the object extraction module to improve the expressiveness of the codes in this module. Three sessions of joint optimised training to improve extraction results. Experiments are conducted on the automotive manufacturing fault repair dataset, the automotive industry fault pattern extraction evaluation dataset, and the equipment manufacturing dataset, and the model in this paper achieves better results than other relational extraction baseline models. [ABSTRACT FROM AUTHOR]

In recent years, the classification and segmentation research of point cloud mostly adopts the method of extracting the features of point cloud with multi-level architecture, and obtains relatively stable high-level semantic features. However, the extraction of global features and neighborhood features are insufficient, and the feature fusion of context information is lacking. Therefore, a new LAM-EdgeCNN network is proposed in this paper, which adopts the cascade of edge convolution and attention mechanism to extract multi-level feature information from point clouds and obtain high-level feature information. Firstly, in order to enhance the capture of specific channel features and key spatial points, a lightweight LAM attention mechanism is proposed, which uses CAM feature channel attention to acquire the correlation of each channel and locate the capture of key channel features, so that the network pays more attention to specific channel features to reduce the information dispersion and feature redundancy. Secondly, SAM spatial attention mechanism is introduced to obtain the attention weight of the location information of the point space and increase the granularity of the shallow information. Finally, a combination of attention mechanism and edge convolution EdgeConv is used to enhance context awareness, fully extract and fuse the local features and context features of point cloud, and obtain the downstream task-oriented point cloud features. The model is applied to the public data set, and the experiment shows that the model has good effect and robustness in the tasks of point cloud classification, component segmentation and semantic segmentation. [ABSTRACT FROM AUTHOR]

The point cloud of the real scene not only has the spatial geometric information of the point cloud, but also has the color information of the 3D object. The existing network cannot effectively use the local features and spatial geometric feature information of the real scene. Therefore, a dual-channel feature fusion of real scene for point cloud semantic segmentation DCFNet can be used for indoor and outdoor scene semantic segmentation in different scenarios. More specifically, in order to solve the problem that the color information of the point cloud in the real scene cannot be fully extracted, the method uses two input channels, and the channel adopts the same feature extraction network structure. The input of the upper channel is the complete RGB color and point cloud coordinate information, and the channel mainly focuses on the scene features of complex objects, while the lower channel only inputs the point cloud coordinate information. This channel mainly focuses on the spatial geometric characteristics of the point cloud. In each channel, in order to better extract local and global information and improve network performance, the inter-layer fusion module and the Transformer channel feature expansion module are introduced. At the same time, the existing 3D point cloud semantic segmentation methods lack of attention to the relationship between local features and global features, which leads to poor segmentation results for complex scenes. In this paper, the features extracted from the upper and lower channels are fused by the DCFFS (dual-channel feature fusion segmentation) module, and the semantic segmentation of the real scene is performed. The experimental results show that the mean intersection over union (MIOU) of the proposed DCFNet segmentation method on the S3DIS Area5 indoor scene dataset and the STPLS3D outdoor scene dataset reaches 71.18% and 48.87% respectively. The mean average precision (MACC) and overall accuracy (OACC) reach 77.01% and 86.91% respectively, achieving high-precision point cloud semantic segmentation in real scenes. [ABSTRACT FROM AUTHOR]

The crack image on the rail surface has a small target, many background interference information and a high resolution, making its target detection effect not ideal. In this regard, the paper proposes an improved target detection algorithm of YOLOV5s to improve the detection effect of rail cracks. The VOV-GSCSP module is introduced in the neck network, and the ordinary convolution is replaced by the lighter convolution method GSconv to reduce the computational effort of the network while retaining more detailed information. The feature pyramid structure is improved and a multipath cross-layer fusion structure is proposed to incorporate the information of the backbone network across layers in the process of feature pyramid downsampling to retain more original feature information and improve the accuracy of target detection. Meanwhile, the CA attention module and Transformer structure are introduced to further enhance the information extraction of higher-order semantics. The experimental results show that the improved YOLOV5s algorithm achieves an average mean accuracy (mAP) of 62.4%, which is 6.2 percentage points better than the original YOLOV5s algorithm; the recall rate (Recall) is 92.2%, which is 4.4 percentage points better. [ABSTRACT FROM AUTHOR]

Copyright of South China Fisheries Science is the property of South China Fisheries Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Graphics is the property of Journal of Graphics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Tropical Meteorology (1004-4965) is the property of Guangzhou Institute of Tropical & Oceanic Meteorology, China Meteorological Administration and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem of low accuracy of traditional social recommendation, this paper proposes a use consistent social recommendation model based on multi-view fusion. The social recommendation model takes into account the inconsistency of users in social networks and the influence of single view information on the recommendation results. It uses the attention mechanism to dynamically filter out inconsistent social neighbors, and combines user-item interaction information to learn user feature expression. At the same time, the feature representation of the project in low-dimensional space is learned from multiple views such as knowledge graph and user-project history interaction information. Finally, the characteristics of users and items are represented by inner product operation to complete the final recommendation task. In order to verify the effectiveness of the proposed recommendation algorithm, six baseline models are compared on two public datasets of Douban and Yelp, and the recall, normalized discounted cumulative gain (NDCG) and precision are used as evaluation indicators. The experimental results show that the performance of the proposed social recommendation model is better than other models. [ABSTRACT FROM AUTHOR]

Explore how neural networks combine attention mechanisms and their variants to better learn complex and implicit relationships between users and items, thereby improving the accuracy and personalization of recommendations. Starting from six typical types of neural networks: multi-layer perceptron, convolutional neural network, recurrent neural network, autoencoder, graph neural network, and backpropagation neural network, this paper studies the process of combining them with the attention mechanism for recommendation. Specifically, the advantages and disadvantages are analyzed based on typical application scenarios such as click-through rate prediction, tag recommendation, and review rating prediction. By combining neural networks with attention mechanisms, the model can focus on key information in the input data, reduce attention to secondary information, and even directly filter out irrelevant information. Existing recommendation models that combine attention mechanisms with neural networks, to a large extent, can meet the needs of common recommendation tasks. However, this type of model still faces some challenges in complex recommendation scenarios such as cross-domain recommendation, deep reinforcement learning recommendation, and multi-modal recommendation. For example, cross-domain recommendation requires the model with the ability of transfer learning, and reinforcement learning recommendation requires long-term reward modeling. [ABSTRACT FROM AUTHOR]

[Objectives]Intelligent detection of diseased wheat kernels is important for the efficient, rapid, and accurate evaluation of wheat kernel quality. Existing deep neural network models for the classification of diseased wheat kernels have disadvantages such as large numbers of parameters and complexity of operations, which make it unsuitable for the deployment of the model on edge computing devices, thus affecting the efficiency of on-site classification of diseased wheat kernels. In this paper, a lightweight neural network algorithm for diseased wheat kernel classification was proposed. [Methods]In this study, the model was developed based on the lightweight network MobileNetV2 and added an improved CBAM(convolutional block attention module)attention mechanism. The improved model was fully integer quantized and deployed to mobile devices. Moreover, the proposed model was applied to classify four types of wheat kernels(fusarium-damaged kernels, common bunt of wheat kernels, broken kernels, and normal kernels). [Results]Compared to the previous MobileNetV2 network, the model combining the improved attention mechanism and MobileNetV2 network was improved, and the accuracy, precision, and recall rates for the model were improved by 3.15%, 3% and 3%, respectively. The improved model after full integer quantization achieved 99%, 94%, 99% and 96% recognition accuracy for fusarium-damaged kernels, common bunt of wheat kernels, broken kernels, and normal kernels, respectively. In addition, the size of the model was 2.36 MB, and the single inference time of this model at the edge computing device was 96.95 ms. [Conclusions]The improved algorithm of this paper has increased the model accuracy, reduced the size of the model, and accelerated the model inference speed. This study can provide guidance for the de-redundancy of fusarium-damaged kernel classification models. [ABSTRACT FROM AUTHOR]

In order to solve the problem that it is difficult for the model to perceive implicit information due to the lack of external knowledge in rumor detection, which limits the ability of the model to mine deep information, this paper proposed knowledge graph based multi-feature fusion rumor detection (KGMRD) method. Firstly, for each event, it constructed posts and comments together into a text sequence and used a classifier to extract the emotional features. This paper constructed a knowledge graph based on text using ConceptNet and aggregated the entity representation in the knowledge graph with the semantic features of text using the attention mechanism, so as to obtain the enhanced semantic feature representation. Secondly, in terms of communication structure, for each event, this paper built its communication structure diagram based on the propagation and forwarding relationship of the post, and used DropEdge to prune the communication structure diagram, so as to obtain more effective communication structure characteristics. Finally, it fused the obtained features to get a new representation and compared seven models including SVM-RBF on three real datasets of Weibo, Twitter15 and Twitter16. The experimental results show that compared with the current baseline with the best effect, the KGMRD method has the best ACC on the Weibo dataset and improves the ACC by 1.1%, and there is a 2.2% improvement on Twitter15 and Twitter16 dataset in ACC. The experiment proves that the KGMRD method is reasonable and effective. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of the Beijing Institute of Fashion Technology (Natural Science Edition) is the property of Beijing Institute of Fashion Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)