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In view of the problems of data sparsity and the diversity in academic paper recom-mender systems,based on CONVNCF, this paper proposed an algorithm of convolution with word and heterogeneous information network for academic paper recommendation ( WN -APR) . Firstly, WN -APR algorithm learned user and paper' s diverse features from different semantics to alleviate the sparsity problem. Then it designed an outer product fusing way to seamlessly combine user features with paper features. Replacing of 2D CNN, this algorithm applied 3 D convolution to mine the influence of different features on the performance. Finally, it modified the BPR loss function to enhance diversity in recommendations. Experimental results on CiteULike-a and CiteULike-t datasets show that WN-APR improves the performance of accuracy and diversity over the baseline models. [ABSTRACT FROM AUTHOR]

The U-turn task is one of the contents of autonomous driving research, and most of the solutions under the standard roads in cities cannot be implemented on non-standard roads. Aiming at solving this problem, this paper establishes a vehicle U-turn dynamical model and designs a multi-scale convolutional neural network to extract feature maps as the input of the agent. In addition, for the sparse reward problem in the U-turn task, this paper proposes a hierarchical proximal policy optimization algorithm that combines hierarchical reinforcement learning and proximal policy optimization algorithm. In experiments with simple and complex scenarios, this algorithm learns policies faster and has a higher success rate of U-turn compared to other algorithms. [ABSTRACT FROM AUTHOR]

Facial local detail information plays an important role in facial expression recognition（FER）.However, most of the existing methods only focus on the high-level semantic information of facial expressions, while ignoring the fine-grained information of local facial regions. To solve this problem, this paper proposed a deep convolutional neural network fusing local feature and two-stage attention weight learning（FLF-TAWL）,which could adaptively capture important facial regions to improve the effectiveness of facial expression recognition. The FLF-TAWL model was composed of a dual-branch framework, one branch extracted local features from image blocks, and the other branch extracted global features from the entire expression image. Firstly, this paper proposed a two-stage attention weight learning strategy. In the first stage, it roughly learned the importance weights of global and local features, in the second stage, it further refined the attention weight, and fused the local and global features. Secondly, the model used a region-biased loss function to encourage the most important regions to obtain higher attention weights. Finally, this paper carried out extensive experiments on FERPlus, Cohn-Kanada（CK+） and JAFFE datasets to obtain accuracy rates of 90.92%,98.90% and 97.39% respectively. The experimental results verify the effectiveness and feasibility of the FLF-TAWL model. [ABSTRACT FROM AUTHOR]

Traffic condition prediction is an important part of intelligent transportation system, and traffic flow is the most direct embodiment of traffic condition. Therefore, traffic flow prediction has important application value. On the one hand, the roads in the city have spatial topological properties, on the other hand, the traffic flow changes dynamically with time. Therefore, the key to the prediction of traffic flow is to model the time and space dependence in the data. In view of this characteristic, this paper used neural network model and attention mechanism to explore the temporal and spatial dependence relationship in traffic flow data, and proposed a traffic flow prediction model based on time map attention. In terms of spatial dependence, it used a learning algorithm combining graph convolution network and attention to assign different weights to nodes with different influence degrees, and added node adaptive learning to effectively extract spatial features. In terms of time dependence, it used the temporal convolution network to extract the temporal features, and expanded the sensing domain by expanding convolution, so as to capture the features of longer time series data. A spatial-temporal network layer was composed of graph attention network and time convolution network, which was finally connected to the output layer to output the prediction results. The model used the combination of graph convolution neural network and attention mechanism to extract spatial features, fully considered the spatial relationship between roads, and used temporal convolution network to capture temporal features. After experiments on two real datasets, it is found that it has good performance in the next 15, 30 and 60 minutes, and the results are better than the existing baselines. [ABSTRACT FROM AUTHOR]

Aiming at the problem of intra-modality feature representation and inter modality feature fusion in multimodal sentiment analysis, this paper proposed a multi-level hybrid fusion multi-modal sentiment analysis model based on attention mechanism and multi-task learning. Firstly, the model used convolution neural network and bi-directional gated unit to extract the single-modality internal feature. Secondly, it used the cross-modality attention mechanism to realize the pairwise feature fusion between modalities. Thirdly, it used the self-attention mechanism to select the modality Finally, combining with multi-task learning, the model obtained both sentiment and emotion classification results. The experimental results on CMU-MOSEI dataset show that this method can improve the accuracy and F 1-score of sentiment and emotion classification. [ABSTRACT FROM AUTHOR]

Because convolutional neural networks( CNN) mostly focus on global feature learning and ignore local feature information containing more details, it is difficult to improve the accuracy of indoor scene recognition. To solve this problem, this paper proposed an indoor scene recognition method based on improved global-local attention network ( GLANet). Firstly, it used GIAN et to capture the global and local features of the scene image to increase the detail information of the image features. Then, it introduced a non-local attention module into the local network to further preserve the detail features of the image through the convolution of attention diagram and feature graph. Finally, it used various features of different stages of the network for classification. Through training and verification on the MIT lndoor67 and the SUN397 dataset, the recognition accuracy of the proposed method was increased by l. 98% and 3. 07% respectively compared with the LGN method. Experimental results show that the algorithm can effectively capture global semantic information and fine spatial details, and significantly improves the recognition accuracy. [ABSTRACT FROM AUTHOR]

Objective: Develop a machine learning model, and to evaluate its accuracy in the diagnosis of primary bone tumor around the knee joint. Methods: In this paper, the deep learning method of deep convolutional neural network（DCNN） was applied to the imagomics analysis of knee X-ray images, its clinical value in assisting the diagnosis of primary bone tumor around the knee joint were explored. Results: The deep learning model showed excellent diagnostic accuracy in differentiating normal images from tumor images.The overall accuracy of 5 rounds of testing using the DCNN model was（99.8±0.4） %, while the positive predictive value and negative predictive value were（100.0±0.0） % and（99.6±0.8） % respectively. The area under the curve（AUC） of each dataset was 0.99, 1.00,1.00, 1.0 and 1.0 respectively, average AUC was（0.998±0.004). A further 10 rounds of testing using the DCNN model showed an overall accuracy of（71.2±1.6） % in differentiating benign from malignant bone tumors, and a strong positive predictive value（91.9±8.5） %was achieved in differentiating benign and malignant bone tumor, the AUC of each dataset was 0.63, 0.63, 0.58, 0.69, 0.55, 0.63, 0.54,0.57, 0.73, 0.63 respectively, average AUC was（0.62±0.06). Conclusion: This is the first study on the application of artificial intelligence technology in the imaging analysis of X-ray images for bone tumor diagnosis, Artificial intelligence imaging models can help doctors to automatically and quickly screen bone tumors and identify benign or malignant tumors, with a high positive predictive value. MoreReset [ABSTRACT FROM AUTHOR]