Extended reality (XR) is a general term for virtual reality (VR), augmented reality (AR), and mixed reality (MR). By converting abstract digital expressions into intelligent feedback through figures, one can effectively compensate for the poor performance of traditional learning in deep cognitive processing and operational skills training. However, the extant results are uncertain, and only a limited number of studies have investigated the influence mechanism of heterogeneity among VR, AR, and MR on procedural knowledge learning, higher-level presence and generative cognitive processing, higher-quality operational behaviour and knowledge transfer. This study explored and analysed the influence of XR heterogeneity on procedural knowledge gain and operation training. Participants (n = 127) were recruited from among junior undergraduate students majoring in rail traffic signal and control. Based on the procedural disassembly and assembly of the ZD6 electric switch machine, an XR pre-training system and a 3D-printed combined virtual-real procedural training system were developed to collect three types of data regarding knowledge gain, procedural training and a set of questionnaires. Subsequently, a one-way analysis of variance was conducted with XR as the moderator variable. The results indicate that fully immersive VR is more conducive to procedural operation training; however, space and real factors must be considered. AR is consistent with the learner's operating habits, but the corresponding lack of immersion and interactivity restricts the improvement of procedural learning effects. MR multimodal perception leads to an increase in cognitive load, but moderate cognitive pressure is conducive to the cultivation of procedural cognition skills. Furthermore, this study proposes suggestions for using XR to intensify the perception and understanding of procedural cognitive actions.

The nonrigid registration algorithm based on B-spline Free-Form Deformation (FFD) plays a key role and is widely applied in medical image processing due to the good flexibility and robustness. However, it requires a tremendous amount of computing time to obtain more accurate registration results especially for a large amount of medical image data. To address the issue, a parallel nonrigid registration algorithm based on B-spline is proposed in this paper. First, the Logarithm Squared Difference (LSD) is considered as the similarity metric in the B-spline registration algorithm to improve registration precision. After that, we create a parallel computing strategy and lookup tables (LUTs) to reduce the complexity of the B-spline registration algorithm. As a result, the computing time of three time-consuming steps including B-splines interpolation, LSD computation, and the analytic gradient computation of LSD, is efficiently reduced, for the B-spline registration algorithm employs the Nonlinear Conjugate Gradient (NCG) optimization method. Experimental results of registration quality and execution efficiency on the large amount of medical images show that our algorithm achieves a better registration accuracy in terms of the differences between the best deformation fields and ground truth and a speedup of 17 times over the single-threaded CPU implementation due to the powerful parallel computing ability of Graphics Processing Unit (GPU).

Object 6D pose estimation, as a key technology in applications such as augmented reality (AR), virtual reality (VR), robotics, and autonomous driving, requires the prediction of the 3D position and 3D pose of objects robustly from complex scene images. However, complex environmental factors such as occlusion, noise, weak texture, and lighting changes may affect the accuracy and robustness of object 6D pose estimation. We propose a robust CoS-PVNet (complex scenarios pixel-wise voting network) pose estimation network for complex scenes. By adding a pixel-weight layer based on the PVNet network, more accurate pixel point vectors are selected, and dilated convolution and adaptive weighting strategies are used to capture local and global contextual information of the input feature map. At the same time, the perspective-n-point localization algorithm is used to accurately locate 2D key points to solve the pose of 6D objects, and then, the transformation relationship matrix of 6D pose projection is solved. The research results indicate that on the LineMod and Occlusion LineMod datasets, CoS-PVNet has high accuracy and can achieve stable and robust 6D pose estimation even in complex scenes.

Aspect-level sentiment classification aims to identify the sentiment polarity of a review expressed toward a target. In recent years, neural network-based methods have achieved success in aspect-level sentiment classification, and these methods fall into two types: the first takes the target information into account for context modelling, and the second models the context without considering the target information. It is concluded that the former is better than the latter. However, most of the target-related models just focus on the impact of the target on context modelling, while ignoring the role of context in target modelling. In this study, we introduce an interactive neural network model named LT-T-TR, which divided a review into three parts: the left context with target phrase, the target phrase, and the right context with target phrase. And the interaction between the left/right context and the target phrase is utilized by an attention mechanism to learn the representations of the left/right context and the target phrase separately. As a result, the most important words in the left/right context or in the target phrase are captured, and the results on laptop and restaurant datasets demonstrate that our model outperforms the state-of-the-art methods.

We propose a new method for learning of radial basis function (RBF) neural networks which is based on subtractive clustering algorithm (SCA) and its application to face recognition. The rationale behind the method learning center parameters of RBF by means of SCA is that, for SCA, number of data points and calculation is irrelevant to the dimension of the clustered information and face images are high-dimensional. The algorithm can effectively improve training speed and accuracy. Experiments on face recognition using ORL database show feasibility of the method. Results present that RBF neural networks classifier using proposed algorithm is more precise and faster than corresponding one using general K-means clustering algorithm