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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]

U-Net has achieved great success in the field of image segmentation. However, some of the position information is lost in the process of convolution and downsampling, model is difficult to learn global and long-range semantic interaction information and lacks the ability to integrate global and local information. To extract rich local detail and contextual information, this paper proposed an image segmentation network called MLFCNet, combining a convolutional module and a capsule encoder. Based on the U-Net, this paper introduced a capsule network module to learn target positional information and the relationships between local and global information. At the same time, the proposed attention mechanism could retain the information discarded by the network pooling layer. This paper designed a new attention mechanism so that multi-scale features could be fused, where global information was captured and background noise was suppressed. In addition, it proposed a new local feature co-occurrence algorithm to better learn the relationship between local features. The proposed method was compared with nine methods on two public datasets, mIoU improves 4.7% and Dice coefficient improves 1.7% in liver medical images compared to the second highest performing model. Experimental results on the dataset of liver and dataset of human show that under the same experimental conditions, the proposed network is superior to U-Net and other mainstream image segmentation networks. [ABSTRACT FROM AUTHOR]

Addressing the shortcomings of existing knowledge completion models based on graph neural networks in handling the heterogeneity of knowledge graphs and the limitations posed by most models’ adoption of a single static entity representation, this paper introduces a model based on the graph attention mechanism for disentangled neighborhood information aggregation. Initially, this paper learns the disentangled representation of each entity,providing a multi-component representation for the latent influential factors of entities. Using the attention mechanism,the model selects the most influential latent factors as connection elements for two connected entities. Subsequently,by leveraging a relation-aware attention mechanism, the model adaptively aggregates neighborhood messages at the entity factor level, effectively reducing interference from irrelevant information during aggregation and significantly enhancing the model’s semantic representation capability. Moreover, to focus on the most relevant entity component in the scoring process with a given relation, this paper introduces an adaptive scoring coefficient, enabling the model to perceive the relevance between the given relationship and various entity components adaptively. Experimental results on the WN18RR and FB15K-237 datasets indicate that the proposed model outperforms other advanced baseline models in knowledge graph completion tasks, substantially enhancing the model’s expressive power. [ABSTRACT FROM AUTHOR]

With the development of deep learning, object detection has gradually replaced manual quality inspection as the primary direction for industrial quality control. However, in the complex texture of the tile surface, there are more lowvisibility small defects, and the interference from the complex textured background is serious. This results in a high false detection and false alarm rate using traditional object detection methods. To enhance the efficiency of defect detection, this paper proposed a defect detection approach on complex textured tile surfaces based on the joint attention mechanisms of channels and spatial. Firstly, to enhance the feature expression of small defects, it proposed a selective feature fusion method by modeling the relationship between deep and shallow feature channels. Secondly, it designed a joint channel and spatial attention module that selects key feature channels and suppresses texture regions through channel and spatial attention,enabling the model to focus on learning defect features and enhancing its ability to discriminate between defects and texture.Finally, it validated the approach on a dataset of complexly textured ceramic tile surface defects. The experimental results demonstrate that compared to the AFF and CBAM methods, the selective feature fusion method and channel ＆ spatial joint attention achieved improvements of 5.3AP and 6.32AP, respectively. In addition, this paper compared the overall approach with the existing tile detection method YOLOv5 and texture fabric defect detection method AFAM, showing that it outperformed these methods, with respective improvements of 1.32AP and 2.12AP. [ABSTRACT FROM AUTHOR]

In order to alleviate the inherent information differences between different user social spaces and interest spaces in recommendation systems and the problem of ignoring high-order neighbors, this paper proposed AGCRSR. Firstly, the algorithm used a mapping matrix in the embedding layer to convert the initial feature vectors into adaptive embedding. Secondly, it intro- duced an attention mechanism to aggregate different aspects of user embeddings, and used a graph convolutional network to li- nearly learn the potential representations of users and items. Finally, it used the adaptive module to aggregate user representations and predict the final recommendation results of users for the project using the inner product function. This paper conducted comparative experiments on the LastFM and Ciao datasets and compared with other baseline algorithms. The experimental results show that the recommendation performance of the AGCRSR is significantly improved compared to other algorithms. [ABSTRACT FROM AUTHOR]

Low-light image segmentation is always the difficulty of image segmentation. The low contrast and high fuzziness caused by low light make this kind of image segmentation much more difficult than general image segmentation. In order to improve the accuracy of semantic segmentation in low light environment, this paper proposed a semantic segmentation model of low light scene with filtering light style under noise guidance(SFIS) according to the characteristics of low-light image. The model comprehensively used signal-to-noise ratio as prior knowledge, and adopted different distance interaction for different noise regions in the image by guiding the self-attention operation in the long distance branch and the feature fusion of long/short distance branches. This paper also further designed an illumination filter, which was a module that further extracted the illumination style information from the overall style of the image. By alternately training the illumination filter and the semantic segmentation model, the lighting style gap between different lighting conditions was gradually reduced, so that the segmentation network could learn illumination invariant features. The proposed model outperforms the previous work on the dataset LLRGBD and achieves the best results. The mIoU on the real dataset LLRGBD-real reaches 66.8%, it shows that the proposed long and short distance branch module and the illumination filter module can effectively improve the semantic segmentation ability of the model in low light environment. [ABSTRACT FROM AUTHOR]

The current advanced session recommendation algorithms mainly use graph neural network to mine the pairwise transformation relationships of items from the global and target sessions, and compress the target session into a fixed vector representation, ignoring the complex high-order information between items and the impact of target items on user preference diversity. To this end, this paper proposed a hypergraph convolution network and target multi-intention perception for session-based recommendation algorithm HCN-TMP. This algorithm expressed user preference by learning session representation. Firstly, it constructed a session graph based on the target session, and constructed a hypergraph based on the global session. It transformed the original item embedding representation that reflected the user's coupling intention into a multi factor embedding representation of the item through intention disentanglement technology. Then, it learned the item representations of the session level and global level of the target session node through graph attention network and hypergraph convolutional network respectively, and used the distance correlation loss function to enhance the independence between the multi-factor embedded blocks. Next, it embedded the node location information in the target session, weighted the attention weight of each node, and got the session representation of the global level and session level. It used comparative learning to maximize the mutual information of the two. Through the target multi-intention perception, it adaptively learned the multi-intention user preferences in the target session for different target items, obtained the session representation of the target perception level. Finally, it linearly fused the three level session representations to obtain the final session representation. This paper carried out the experiments on two public data sets, Tmall and Nowplaying. The experimental results verify the effectiveness of the HCN-TMP algorithm. [ABSTRACT FROM AUTHOR]

The success of emotional dialogue systems relies on the ability to comprehend, perceive, and express emotions, while facial expressions and personality can also help. However, despite the crucial importance of this multi-modal information in multi-turn emotional dialogues, existing systems still need to be improved to leverage multi-modal information's advantages and overlook the significance of contextual relevance. To address this issue, this paper proposed a multi-turn emotional dialogue generation model based on a dua-decoding method(MEDG-DD). The model utilized a heterogeneous graph neural network encoder to integrate historical dialogue, facial expressions, emotion flow, and speaker information, obtaining a more comprehensive dialogue context. Subsequently, it employed a dual-decoding mechanism based on attention to generate emotionally rich expressions relevant to the dialogue context. Experimental results demonstrate that the proposed model effectively integrates multi-modal information, achieving more accurate, natural, and coherent emotional expressions. Compared to the traditional ReCoSa model, this model exhibits significant improvements across various evaluation metrics. [ABSTRACT FROM AUTHOR]

To address the problems of large amount of noise and lack of contextual information in short texts on the Web, this paper proposed a text sentiment analysis model based on BERT and hypergraph with dual attention mechanism. This method first utilized BERT for dynamic feature extraction of sentiment texts. Meanwhile it mined the contextual, topic and semantic dependency information of the text to model it into a hypergraph, and then aggregated the above information through the dual graph attention mechanism. Finally, it spliced the features extracted by BERT and Hypergraph with dual attention network, and obtained the prediction result after softmax layer. The accuracy of this model on the e-commerce review dataset and the microblog text dataset reaches 95.49% and 79.83% respectively,which is 2.27%-3.45% and 6.97%-11.69% higher than the baselines, respectively. The experimental results show that the model can significantly improve the accuracy of sentiment analysis for Chinese web short texts. [ABSTRACT FROM AUTHOR]

At present, most of the aspect-based sentiment analysis methods based on graph convolutional networks use the syntactic knowledge, semantic knowledge and sentiment knowledge of text to construct text dependency. However,there are few studies on the use of text word order knowledge to build text dependency, which leads to the inability of graph convolutional networks to effectively use text word order knowledge to guide aspects to learn contextual sentiment information, thus limiting its performance. Aiming at the above problems, this paper proposes an aspect-based sentiment analysis model of Dual-channel Graph Convolutional Network with Word-order Knowledge (WKDGCN), which is composed of Word-order Graph Convolutional Network (WoGCN) and the Sentiment and Attention enhanced syntactic Graph Convolutional Network (SAGCN). Specifically, WoGCN constructs a graph convolutional network based on the word-order knowledge of text, and learns contextual sentiment information by relying on the characteristics of the guiding aspect of the text word order. SAGCN uses the sentiment knowledge in SenticNet combined with the attention mechanism to enhance syntactic dependence, and uses the enhanced syntactic dependence to construct a graph convolutional network to guide the aspect features to learn contextual emotional information. Finally, the features of two graph convolutional networks are fused for sentiment classification. In addition,a weight allocation strategy is designed to keep the context weights consistent while amplifying the weights of the terms to avoid erroneous calculation of the semantic correlation between the features of the terms and the important features.Finally, experimental results on several public data sets show that the proposed method is superior to the comparison model. [ABSTRACT FROM AUTHOR]

Atmospheric turbulence is a crucial factor that affects the quality of long-distance imaging. Though current deep learning models can effectively suppress geometric displacement and spatial blurring caused by atmospheric turbulence, such models require a large number of parameters and computational resources. To tackle this problem, this paper proposed a lightweight atmospheric turbulence degraded image restoration model based on generative inversion that entailed three core mo-dules: the DeBlur module, the remove shift module, and the turbulence regeneration module. The DeBlur module used high-dimensional feature mapping blocks, detail feature extraction blocks, and feature compensation blocks to suppress image blurring caused by turbulence. The remove shift module compensated for pixel displacement caused by turbulence using two convolutional layers. The turbulence regeneration module regenerated turbulence degraded images through convolutional operations. In the DeBlur module, it designed an attention-based feature compensation module that integrated the channel attention mechanism and the spatial mixed attention mechanism to focus on essential detail information in the image during training. The proposed model achieved peak signal-to-noise ratios of 19.94 dB and 23.51 dB, and structural similarity values of 0.688 2 and 0.752 1 on publicly available dataset Heat Chamber and self-built dataset Helen, respectively. Furthermore, it reduced the number of parameters and computational resources, compared to the current state-of-the-art(SOTA) method. The experimental results demonstrate the effectiveness of this method in restoring atmospheric turbulence degraded images. [ABSTRACT FROM AUTHOR]

Traditional traffic flow prediction models model historical data in terms of time and space, ignoring the internal potential temporal periodicity of traffic data and the distance characteristics and similarity spatial characteristics of nodes between traffic networks. Based on this, this paper proposed a multi-channel spatio-temporal encoder model MC-STGNN for traffic flow prediction to improve the accuracy of traffic flow prediction. Firstly, it processed the traffic data into a three channel periodic time series, and encoded the overall sequence data with temporal and adaptive spatial positions to extract dynamic correlations between road network nodes. Secondly, it introduced a multi-heads self-attention mechanism with convolutional structure to capture varying degrees of temporal correlation of periodic data to a greater extent. Finally, it proposed a graph generator to generate a new spatiotemporal map, extracting similarity and distance features between road network nodes, and integrating the spatial information of the original map and the new spatiotemporal map using a gated graph convolutional network. It conducted comprehensive traffic flow prediction experiments for an hour on the highway datasets PEMS03 and PEMS08. The experimental results show that the MC-STGNN model has better performance indicators compared to other baseline models, indicating that the MC-STGNN model has better modeling ability. [ABSTRACT FROM AUTHOR]