Subjective measurement remains one of the most widely used approaches for evaluating Quality of Experience (QoE) in tactile virtual environments. However, its reliability is often compromised by factors such as conscious bias, variations in user expressiveness, and contextual influences, which may distort the accuracy of evaluation outcomes. In light of the fact that Electroencephalography (EEG) provides a direct window into neurophysiological correlates of emotion and cognitive states, this article proposes an objective QoE evaluation method for Virtual Reality (VR) with vibrotactile feedback based on Inter-user Brain Spatial Information (IBSI). The proposed IBSI feature extraction method enhances the conventional Common Spatial Pattern (CSP) algorithm through the introduction of a regularization constraint term designed to mitigate overfitting. Moreover, the covariance matrix of each non-target user is weighted according to its Kullback–Leibler divergence from the target user, enhancing cross-user alignment and supporting effective transfer of neural information. To validate the performance of the proposed method, we design a VR shooting interaction experiment involving 64 participants. The study comprises three main phases: preparation, interaction, and subjective feedback. During the preparation phase, participants receive task explanations and familiarize themselves with the VR environment. In the interaction phase, participants complete a standardized shooting task, while EEG data are recorded synchronously. Finally, subjective feedback is collected through questionnaires. QoE assessment is accomplished by classifying IBSI features using classical classifiers, with subjective QoE ratings serving as ground truth. Experimental results show that our method outperforms existing methods in classification accuracy, and the dominant activation pattern in the alpha rhythm is consistent with neural mechanisms associated with motor perception. Meanwhile, the mutual interpretability between subjective and objective data characterizing QoE further validates the rationality of the experimental paradigm.

Addressing class-imbalanced graphs is a challenging task due to the involvement of both node attributes and graph structures. Existing works on class-imbalanced graphs simply assume that all node attributes are available. However, in real-world graphs, many nodes may lack attributes due to privacy issues or missing data, making class-imbalanced graph learning more challenging. In this paper, we propose GraphIAM, a novel two-stage algorithm for improving class-imbalanced node classification on attribute-missing graphs. In the pre-training phase, GraphIAM adopts graph contrastive learning with oversampling to tackle both attribute-missing and class-imbalanced issues. During fine-tuning, an adapter mechanism is introduced to learn node representations, alleviating the generalization gap between pre-training and downstream tasks. Experimental results on benchmark datasets demonstrate that our method achieves state-of-the-art performance, outperforming class-imbalanced graph learning approaches by 5% in F Score on graphs with severe attribute missingness.

Exploiting the correlation between multimodal data to generate tactile data has become a preferred approach to enhance tactile rendering fidelity. Nevertheless, existing studies have often overlooked the temporal dynamics of force tactile data. To fill this gap in the literature, this paper introduces a joint visual-audio approach to generate a temporal tactile data (VA2T) algorithm, focusing on the temporal and long-term dependencies of force tactile data. VA2T uses a feature extraction network to extract audio and image features and then uses an attention mechanism and decoder to fuse these features. The tactile reconstructor generates temporal friction and a normal force, with dilated causal convolution securing the temporal dependencies in the force tactile data. Simulation experiments on the LMT dataset demonstrate that compared with the transformer and audio-visual-aided haptic signal reconstruction (AVHR) algorithms, the VA2T algorithm reduces the RMSE for generated friction by 29.44% and 32.37%, respectively, and for normal forces by 23.30% and 35.43%, respectively. In addition, we developed a haptic rendering approach that combines electrovibration and mechanical vibration to render the generated friction and normal force. The subjective experimental results showed that the rendering fidelity of the data generated using the VA2T method was significantly higher than that of the data generated using the transformer and AVHR methods.

Quality of experience (QoE) has been widely recognized as the primary metric to evaluate user experience in multimedia applications. However, the QoE assessment of tactile virtual environments is still highly dependent on subjective measures. Inspired by the fact that physiological signals can characterize the user's emotional state, we propose a QoE measurement method for virtual reality (VR) with vibrotactile feedback based on frontal lobe power asymmetry (FLPA). The subjective score of vibrotactile experience in VR is used as the ground truth of QoE. The selection of QoE measurement indicators consists of two steps. First, the relationship between FLPA phenomenon and scores of QoE is preliminarily established by statistical methods and Spearman Correlation Coefficient. Then, the most important FLPA feature is selected by random forest, which is the best indicator for QoE measurement. The brain neural images show that vibrotactile feedback in VR can evoke FLPA phenomenon. Correlation analysis shows that there is a significant correlation between subjective scores of QoE and FLPA features. The classification results show that the selected best FLPA feature can be used as a physiological indicator to measure and predict QoE. We achieve mutual interpretation of EEG-based physiological measurements and subjective cognitive outcomes of QoE.

Variational graph autoencoder (VGAE) is a promising deep probabilistic model in graph representation learning. However, most existing VGAEs adopt the mean-field assumption, and cannot characterize the graphs with noise well. In this paper, we propose a novel deep probabilistic model for graph analysis, termed Multi-head Variational Graph Autoencoder Constrained by Sum-product Networks (named SPN-MVGAE), which helps to relax the mean-field assumption and learns better latent representation with fault tolerance. Our proposed model SPN-MVGAE uses conditional sum-product networks as constraints to learn the dependencies between latent factors in an end-to-end manner. Furthermore, we introduce the superposition of the latent representations learned by multiple variational networks to represent the final latent representations of nodes. Our model is the first use sum-product networks for graph representation learning, extending the scope of sum-product networks applications. Experimental results show that compared with other baseline methods, our model has competitive advantages in link prediction, fault tolerance, node classification, and graph visualization on real datasets.

Change detection is a hot issue and is of great significance in remote sensing. The logarithm operation is a valid way to reduce the influence of multiplicative noise in the Synthetic Aperture Radar (SAR) image. However, changed areas with high gray level values will be weakened due to the nature of the logarithmic function. In this paper, a SAR image change detection framework based on visual attention is proposed. In the proposed method, the SAR image change detection is finished with an extreme method with darkness and brightness on the vision. The main process can be divided into two parts according to dark and bright image patches. The dark changed areas are validly detected via a weighted logarithmic function, which has strong noise immunity. The weak bright changes are taken as noise. The saliency extraction is applied on the initial SAR image patches to enhance the bright changed areas whereas others present murky background. Then bright changed areas can be validly detected using kernel fuzzy c-means (KFCM), in which the cross-time similarities function between image patches is used. Finally, two change maps can be added to obtain final result. The real SAR image pairs of Suzhou area are used to verify proposed change detection method. The experimental results demonstrate the effectiveness of the proposed method.