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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]

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]

In order to realize the accurate evaluation of queue posture and action, aiming at the problem that it is difficult to accurately measure the three-dimensional human posture characteristics such as kick height in the training scene, this paper proposed a queue three-dimensional posture feature detection method based on binocular information. The method is divided into two stages; the 2D pose estimation stage and binocular stereo matching stage. In order to improve the accuracy of 2D human pose detection, it designed a 2D pose estimation model based on the improved HRNet network. Firstly, it introduced spatial channel attention in the back-bone network to enhance feature extraction capabilities. The feature fusion layer used an adaptive spatial feature fusion module to fuse multi-scale features of the network. Secondly, it used the unbiased data processing method to encode and decode the heat map to reduce the statistical error of the data. Finally, it adopted a coarse-to-fine multi-stage supervision method during model training to improve the detection accuracy of key points. Based on the 2D pose estimation model, it used the standard correlation matching function to achieve binocular stereo matching, and then obtained the 3D human pose through coordinate transformation. The experimental results show that the improved pose estimation network has better accuracy, the accuracy of the COCO dataset reaches 77.1%, and the detection accuracy of the self-made queue training dataset reaches 86.3%, which are respectively 2.2% and 3.1% higher than the original network. In the kick height experiment of the three-dimensional human body posture, the average relative error measured by the proposed method is 2.5%, which fully verifies the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

To address the issue of user preference drift and capture dynamic user preferences more accurately in sequence recommendation, this paper proposed a novel model named SILSSRec. The model initially leveraged categories and frequencies of items as side information to generate personalized user embeddings based on users' historical interaction sequences. Then it created personalized temporal interval embeddings by considering the time intervals between historical and current interactions, and integrated these embeddings with item feature embeddings to form personalized temporal embeddings. The model employed attention mechanisms and gated recurrent networks to extract users' long-term and short-term preferences from the embedding representations. Furthermore, it used contrastive learning to reinforce the feature representation of preferences, and an adaptive aggregation network dynamically combined these two types of preferences to form the final preference representation of users. Experiments on eight public datasets demonstrate that SILSSRec outperforms existing baseline models on evaluation metrics, with an average increase of 3.82% in AUC, 7.2% in recall rate, and 0.3% in precision. The results validate that SILSSRec performs well in various scenarios, effectively mitigating the preference drift issue and enhancing recommendation performance. [ABSTRACT FROM AUTHOR]

Mainstream knowledge graph-based recommendation model rarely consider the relationship between source nodes and target nodes when fusing high-order information, leading to the introduction of too much noise information and thus affecting recommendation performance in complex network scenarios. To address this problem, this paper proposed a knowledge graph recommendation model with integrated meta-graph neighborhoods, with the goal of reducing the impact of noise information by constructing and integrating meta-graph neighborhoods, thereby improving recommendation performance. Firstly, the model obtained the initial similar sequence of the source node based on meta-graph similarity. Then, the model enhanced the initial sequence using self-attention networks and linear networks, which resulted in a set of enhanced feature vectors that serve as the meta-graph neighborhoods of the node. Secondly, the model designed an attention mechanism based on the user s different preferences for each meta-graph to perform convolution and aggregation on the resulting meta-graph neighborhoods. Then, the model integrated the meta-graph neighborhoods into the source node to enhance the feature representation of the source node. Finally, the model used the inner product of the enhanced vector and the user vector as the probability of user interaction with the item, which was then utilized to complete the recommendation. Experimental results on the MovieLens-20M and Last-FM datasets show that the proposed model achieves an AUC of 97.3% and 94.3%, and F₁-score of 83.1% and 75. 6%, respectively. The recall@50 are 35.4% and 31.7%, respectively. These performance metrics outperform models such as NGCF, KGCN, LKGR, and other models. The results demonstrate that the knowledge graph recommendation model with integrated meta-graph neighborhoods is effective in improving recommendation performance. [ABSTRACT FROM AUTHOR]

Addressing problems such as low success rates, instability, and inefficient traffic flow in autonomous decision-making models at intersections, this paper proposed an intersection decision model based on the GA-TD3 (GRU attention twin delayed deep deterministic policy gradient) algorithm by improving TD3 algorithm from two aspects. Firstly, it introduced a memory module which using GRU neural network to improve the success rate of the decision model. Secondly, it introduced a social attention mechanism in the state space to focus on interactions with social vehicles, which ensured the stability of the model while improving the traffic efficiency of vehicles. After 20 000 rounds of training in the CARLA simulator, the TD3 algorithm achieves a success rate of 92.4%, while the success rate of the GA-TD3 algorithm is 97.6%. Additionally, the vehicle's travel time is shortened by 3.36 s. GA-TD3 algorithm improves both learning efficiency and traffic efficiency, which can alleviate traffic pressure in urban scenes and improve driving efficiency. [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]

Predictive business process monitoring(PBPM) represents a vital research field within BPM that aims to accurately predict future behavioral events. At present, deep learning methods are widely used in PBPM research. However, most of these methods consider only a single event-control flow perspective and do not fuse the attribute-data flow perspective for process prediction. To address this issue, this paper proposed a method called the fusion multi-perspective(FMP) framework based on a two-layer BERT neural network. Firstly, the first layer of BERT was used to learn attribute-data flow information. Subsequently, the second layer of BERT learnt event-behavior control flow information. Finally, the FMP framework combined data flow and control flow to achieve multi-perspective process prediction. Experimental results on real event logs demonstrate that, compared to other research methods, the FPM framework yields higher accuracy in predicting the next event activity. This validates that the FPM framework, which merges multi-perspective views of processes, enables a more comprehensive and in-depth analysis of complex process behaviors while enhancing predictive performance. [ABSTRACT FROM AUTHOR]

To address the issue that existing video colorization methods are complicated to guarantee both coloring quality and temporal consistency, this paper proposed a video colorization method AMVC-GAN combining attention mechanism and multi-scale feature fusion. Firstly, it proposed a GAN-based video colorization network model. It designed a multi-scale feature fusion module in the generator of GAN with a cyclic time network as the main body to obtain information of different time frequencies. Secondly, to effectively consider the relationship between adjacent frames, it used the features extracted from different time frequencies to strengthen the connection between frames as a way to enhance the temporal consistency of colorization. Finally, to obtain more helpful information, it introduced an attention module in the upsampling part, and optimally trained the results by utilizing PatchGAN to enhance the final colorization effect. Comparing with the state-of-the-art automatic video colorization methods on DAVIS and VIDEVO datasets, the results show that AMVC-GAN ranks first in multiple indicators, with better time consistency and colorization effect. Compared with other methods, AMVC-GAN can effectively reduce time flicker, while ensuring more real and natural colorization effect. [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]

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]

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]