Emotion recognition in conversation (ERC) has promising potential in many fields, such as recommendation systems, man–machine interaction, and medical care. In contrast to other emotion identification tasks, conversation is essentially a process of dynamic interaction in which people often convey emotional messages relying on context and common-sense knowledge. In this article, we propose a context- and sentiment-aware framework, termed Sentic GAT, to solve this challenge. In Sentic GAT, common-sense knowledge is dynamically represented by the context- and sentiment-aware graph attention mechanism based on sentimental consistency, and context information is captured by the dialogue transformer (DT) with hierarchical multihead attention (HMAT), where HMAT is used to obtain the dependency of historical utterances on themselves and other utterances for better context representation. Additionally, we explore a contrastive loss to discriminate context-free and context-sensitive utterances in emotion identification to enhance context representation in straightforward conversations that directly express ideas. The experimental results show that context and sentimental information can promote the representation of common-sense knowledge, and the intra- and inter-dependency of contextual utterances effectively improve the performance of Sentic GAT. Moreover, our Sentic GAT using emotional intensity outperforms the most advanced model on the tested datasets.

Short‐term traffic flow forecasting is a fundamental and challenging task since it is required for the successful deployment of intelligent transportation systems and the traffic flow is dramatically changing through time. This study presents a novel hybrid dual Kalman filter (H‐KF ² ) for accurate and timely short‐term traffic flow forecasting. To achieve this, the H‐KF ² first models the propagation of the discrepancy between the predictions of the traditional Kalman filter and the random walk model. By estimating the a posteriori state of the prediction errors of both models, the calibrated discrepancy is exploited to compensate the preliminary predictions. The H‐KF ² works with competitive time and space to traditional Kalman filter. Four real‐world datasets and various experiments are employed to evaluate the authors’ model. The experimental results demonstrate the H‐KF ² outperforms the state‐of‐the‐art parametric and non‐parametric models.

Incomplete multi-view clustering (IMVC) is challenging, as it requires adequately exploring complementary and consistency information under the incompleteness of data. Most existing approaches attempt to overcome the incompleteness at instance-level. In this work, we develop a new approach to facilitate IMVC from a new perspective. Specifically, we transfer the issue of missing instances to a similarity graph completion problem for incomplete views, and propose a self-supervised multi-view graph completion algorithm to infer the associated missing entries. Further, by incorporating constrained feature learning, the inferred graph can be naturally leveraged in representation learning. We theoretically show that our feature learning process performs an Auto-Regressive filter function by encoding the learned similarity graph, which could yield discriminative representation for a clustering task. Extensive experiments demonstrate the effectiveness of the proposed method in comparison with state-of-the-art methods.

This paper presents a spatial-temporal deep learning network, termed ST-TrafficNet, for traffic flow forecasting. Recent deep learning methods highly relate accurate predetermined graph structure for the complex spatial dependencies of traffic flow, and ineffectively harvest high dimensional temporal features of the traffic flow. In this paper, a novel multi-diffusion convolution block constructed by an attentive diffusion convolution and bidirectional diffusion convolution is proposed, which is capable to extract precise potential spatial dependencies. Moreover, a stacked Long Short-Term Memory (LSTM) block is adopted to capture high-dimensional temporal features. By integrating the two blocks, the ST-TrafficNet can learn the spatial-temporal dependencies of intricate traffic data accurately. The performance of the ST-TrafficNet has been evaluated on two real-world benchmark datasets by comparing it with three commonly-used methods and seven state-of-the-art ones. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) of the proposed method outperform not only the commonly-used methods, but also the state-of-the-art ones in 15 min, 30 min, and 60 min time-steps.

Multi-modality sentiment analysis in the social internet of things is a developing field, which is basic to empathetic mechanisms, affective computing, and artificial intelligence. Current works in this domain do not explicitly consider the influence of contextual information fusion based on correlation coefficient and memory network with branch structure for sentiment analysis. Unlike present works, this paper presents a Hierarchical Self-attention Fusion (H-SATF) model for capturing contextual information better among utterances, a Contextual Self-attention Temporal Convolutional Network (CSAT-TCN) for the sentiment recognition in social internet of things, and a Multi Branches Memory (MBM) network that stores self-speaker and inter-speaker sentimental states into global memories. For the MOSI datasets, the hybrid H-SATF-CSAT-TCN-MBM model outperforms the state-of-art networks and shows 0.31 9.93% improvement.