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Recently, semantic communication emerged as one of the enabling technologies for intelligent communications and multimodal information fusion plays an important role therein, where various sensors collect environment information to generate complementary multimodal data. In this article, we propose a new multimodal information fusion scheme for multi-user semantic communications, namely channel-level information fusion, taking into account the nature of signal transmission in wireless channels. In the proposed scheme, wireless channel acts as a medium to fuse multi-modal data and the received signal is viewed as the fused information, such that a receiver can retrieve semantic information without a need to perform multiuser signal detection. In addition, semantic precoding is used to mitigate the randomness effect of wireless channels in fusion. A case study is conducted to validate the effectiveness of the proposed fusion scheme, followed by a discussion on the open issues and challenges to be tackled.

• A decomposition-based ensemble CNN framework is proposed for fatigue recognition. • The use of four decomposition methods is compared for EEG decoding. • A CSBN layer is employed to reduce subject variability. • Three architectures of CNN in the framework is compared. Electroencephalogram (EEG) has become increasingly popular in driver fatigue monitoring systems. Several decomposition methods have been attempted to analyze the EEG signals that are complex, nonlinear and non-stationary and improve the EEG decoding performance in different applications. However, it remains challenging to extract more distinguishable features from different decomposed components for driver fatigue recognition. In this work, we propose a novel decomposition-based hybrid ensemble convolutional neural network (CNN) framework to enhance the capability of decoding EEG signals. Four decomposition methods are employed to disassemble the EEG signals into components of different complexity. Instead of handcraft features, the CNNs in this framework directly learn from the decomposed components. In addition, a component-specific batch normalization layer is employed to reduce subject variability. Moreover, we employ two ensemble modes to integrate the outputs of all CNNs, comprehensively exploiting the diverse information of the decomposed components. Against the challenging cross-subject driver fatigue recognition task, the models under the framework all showed better performance than the strong baselines. Specifically, the performance of different decomposition methods and ensemble modes was further compared. The results indicated that discrete wavelet transform-based ensemble CNN achieved the highest average classification accuracy of 83.48% among the compared methods. The proposed framework can be extended to any CNN architecture and be applied to any EEG-related tasks, opening the possibility of extracting more beneficial features from complex EEG data. [ABSTRACT FROM AUTHOR]

• An adaptive forecast combination of highly diversified models is proposed. • The hyperparameter tuning is conducted through Bayesian optimization. • Our model significantly outperforms the classic and latest methods on extensive datasets. Among various time series (TS) forecasting methods, ensemble forecast is extensively acknowledged as a promising ensemble approach achieving great success in research and industry. Due to the high diversification of individual model assumptions, heterogeneous information fusion contributes to generating effective and robust forecasts for Economics, Meteorology, and Transportation. This paper proposes a Bayesian optimization-based dynamic ensemble (BODE) that overcomes the single model-based methods limitation and provides a dynamic ensemble forecast combination for TS with time-varying underlying patterns. The proposed BODE method combines ten disparate model candidates, including statistical methods, machine learning (ML)-based models, and the latest deep neural networks (DNN). We take into consideration their prediction performance for the recent past to adjust their weights for combination and apply the model-based Bayesian optimization algorithm (BOA) for the combination hyperparameter (HP) tuning to endow our method with higher adaptability and better generalization performance. Besides, the frequency impact of TS data on the ensemble forecast methods is under-researched in the current literature. Therefore, four groups of distinct seasonal TS datasets are investigated in this paper. The empirical result demonstrates that our method performs robustly better performance with the main reasons analyzed in a detailed ablation study. [ABSTRACT FROM AUTHOR]

Transformer architectures have been applied to graph-specific data such as protein structure and shopper lists, and they perform accurately on graph/node classification and prediction tasks. Researchers have proved that the attention matrix in Transformers has low-rank properties, and the self-attention plays a scoring role in the aggregation function of the Transformers. However, it can not solve the issues such as heterophily and over-smoothing. The low-rank properties and the limitations of Transformers inspire this work to propose a Global Representation (GR) based attention mechanism to alleviate the two heterophily and over-smoothing issues. First, this GR-based model integrates geometric information of the nodes of interest that conveys the structural properties of the graph. Unlike a typical Transformer where a node feature forms a Key , we propose to use GR to construct the Key , which discovers the relation between the nodes and the structural representation of the graph. Next, we present various compositions of GR emanating from nodes of interest and α -hop neighbors. Then, we explore this attention property with an extensive experimental test to assess the performance and the possible direction of improvements for future works. Additionally, we provide mathematical proof showing the efficient feature update in our proposed method. Finally, we verify and validate the performance of the model on eight benchmark datasets that show the effectiveness of the proposed method. • We propose a Global-Representation-based attention mechanism for graph neural network. • The low-rank mechanism provides structural information on neighbor nodes. • We address over-smoothing and heterophily issues by various compositions of mechanism. • We justify its effectiveness and superiority with eight widely used tasks. • The proposed model ranks first by statistical analysis compared with seven algorithms. [ABSTRACT FROM AUTHOR]