Chinese poetry, a pinnacle of cultural expression, encapsulates human emotions and societal narratives in succinct, evocative language. Its unique blend of linguistic constraints and musicality makes analyzing its pronunciation's emotional features crucial for enriching children's linguistic prowess and artistic appreciation. To this end, we propose CPFformer, a novel deep learning framework, merges phonetics, sentiment analysis in order to analyze and predict the emotional feature of Chinese poetry pronunciation effectively. CPFformer, which consists of anomaly detection in spatial network (ADSN), spatial-temporal learning (STL), dimension segmentation and embedding (DSW), extraction of temporal attention (ETA), and encoder-decoder module (EDSM) modules, employs graph structures to capture the global and local consistency of emotional features across spatial and temporal, and a multiscale Mel feature extraction technique ensures comprehensive analysis of speech dynamics, enhancing emotional expression understanding. The mean square error (mse), mean absolute error (MAE), residual standard error (RSE), and $R$ -square ( $R^{2}$ ) of experiments reach 0.3572, 0.2486, 0.2014, and 0.9822, respectively, demonstrating its feasibility and effectiveness, exhibiting its superiority to the state-of-the-art approaches. The creation of a dedicated Chinese poetry pronunciation dataset marks a significant contribution, facilitating further research. The potential of CPFformer in speech technology and education heralds a new era, fostering the integration of traditional culture and artificial intelligence, and promoting the advancement of emotional literacy and smart learning environments. Its interdisciplinary implications promise exciting avenues for research and application.

Automatic assessment of driver autonomic nervous system conditions is crucial for enhancing driving safety and healthcare. We present a novel approach that combines a dual optical fiber sensor system and a sophisticated deep learning framework, VHDP, with a strong emphasis on graph learning and spatiotemporal modeling techniques. The proposed fiber interferometer based dual optical fiber sensor system can effectively monitor driver vital signs in various environments. The VHDP framework, a significant innovation in deep learning, first utilizes the EMGLCN module to extract spatial and temporal features from the acquired heart rate variability (HRV) data for graph modeling. Then, through the dynamic spatial-temporal multi-graph method and the temporal-awareness attention module (TAA), it captures cross-time and dimensional correlations. Finally, the prior knowledge guided recalibration fusion module (PKGRF) generates accurate outputs. Experimental results show that the mean square error (MSE), mean absolute error (MAE) and R-square (R2) reach 2.354, 0.896 and 0.9857 respectively, outperforming state-of-the-art approaches. This work not only provides a new method for long-term driver HRV assessment and forecasting but also demonstrates the potential of graph learning and spatiotemporal modeling in the fields of medical monitoring and artificial intelligence, offering valuable insights for the development of portable vital signs monitoring devices in the context of the Internet of Medical Things (IoMT).

We present a framework for learning cross-modal video representations by directly pre-training on raw data to facilitate various downstream video-text tasks. Our main contributions lie in the pre-training framework and proxy tasks. First, based on the shortcomings of two mainstream pixel-level pre-training architectures (limited applications or less efficient), we propose Shared Network Pre-training (SNP). By employing one shared BERT-type network to refine textual and cross-modal features simultaneously, SNP is lightweight and could support various downstream applications. Second, based on the intuition that people always pay attention to several “significant words” when understanding a sentence, we propose the Significant Semantic Strengthening (S3) strategy, which includes a novel masking and matching proxy task to promote the pre-training performance. Experiments conducted on three downstream video-text tasks and six datasets demonstrate that, we establish a new state-of-the-art in pixel-level video-text pre-training; we also achieve a satisfactory balance between the pre-training efficiency and the fine-tuning performance. The codebase and pre-trained models are available athttps://github.com/dongxingning/SNPS3.

Gene expression data can serve for analyzing the genes with changed expressions, the correlation between genes and the influence of different circumstance on gene activities. However, labeling a large number of gene expression data is laborious and time-consuming. The insufficient labeled data pose a challenge to construct the deep learning model. Currently, some graph neural networks (GNN) based on semi-supervised learning mechanism only focus on the feature space and sample space of gene expression data, possibly affecting the accuracy. This article puts forward a novel semi-supervised graph neural network model (SFWN). Firstly, we use the external knowledge of gene expression data for constructing a feature graph, a similarity kernel, and a sample graph for the first time. Later, a novel semi-supervised learning algorithm (SGA) is proposed to extract the data relationship and obtain the global sample structure better. A graph sparse module (SGCN) is also proposed to process sparse representation with gene expression data classification. To overcome the over smoothing problem, a new feature calculation method based on two spaces is proposed to feature representation analysis and calculation in this model. According to a lot of experiments and ablation studies conducted on several public datasets, SFWN exhibits a better effect and is superior to the state-of-the-art approaches (the accuracy and F1-Score are 0.9993 and 0.9899, respectively). Experimental results showed that the proposed SFWN model has strong gene expression feature learning and representation ability, and may provide a new insight and tool for relevant disease diagnosis and clinic practice.

Traffic flow prediction significantly affects the intelligent transportation for digitized urban transportation management and urban traffic control. Considering the complexity and strong non-linearity shown by traffic flow data, the establishment of model regarding spatial correlations as well as time dynamics can remarkably help to accurately predict traffic flow. A lot of current methods are mainly focused on using the historical time series information of observations to extract sequence features. Such forecasting will cause the lack of information and lead to poor accuracy of the forecast results. Although some studies applied spatial-temporal information, but they are not very accurate. In network-based problems, we would consider the constraint of road networks. Specifically, intersection flows, road speed and travel time are related to road networks. Also, they restrict the long-term prediction of traffic flow. For addressing above issues, a graph multi-head attention neural network (GMHANN) is proposed for the purpose of traffic flow prediction. In design, the GMHANN has an encoder-decoder structure. By the encoder, the data are compressed into a hidden space representation, which, relying on the decoder, is reconstructed as output. Furthermore, we put forward a novel gated recurrent unit (GRU) module (AGRU) based on multi-head attention for the effective extraction of the spatial and temporal features exhibited by traffic flow data. Other state-of-the-art methods are employed for evaluating four public datasets, which reveals that our proposed method outperforms others.

With the development of artificial intelligence, speech recognition and prediction have become one of the important research domains with wild applications, such as intelligent control, education, individual identification, and emotion analysis. Chinese poetry reading contains rich features of continuous pronunciations, such as mood, emotion, rhythm schemes, lyric reading, and artistic expression. Therefore, the prediction of the pronunciation characteristics of a Chinese poetry reading is the significance for the presentation of high-level machine intelligence and has the potential to create a high-level intelligent system for teaching children to read Tang poetry. Mel frequency cepstral coefficient (MFCC) is currently used to present important speech features. Due to the complexity and high degree of nonlinearity in poetry reading, however, there is a tough challenge facing accurate pronunciation feature prediction, that is, how to model complex spatial correlations and time dynamics, such as rhyme schemes. As for many current methods, they ignore the spatial and temporal characteristics in MFCC presentation. In addition, these methods are subjected to certain limitations on prediction for long-term performance. In order to solve these problems, we propose a novel spatial-temporal graph model (STGM-MHA) based on multihead attention for the purpose of pronunciation feature prediction of Chinese poetry. The STGM-MHA is designed using an encoder-decoder structure. The encoder compresses the data into a hidden space representation, while the decoder reconstructs the hidden space representation as output. In the model, a novel gated recurrent unit (GRU) module (AGRU) based on multihead attention is proposed to extract the spatial and temporal features of MFCC data effectively. The evaluation comparison of our proposed model versus state-of-the-art methods in six datasets reveals the clear advantage of the proposed model.