Even with an unprecedented breakthrough of deep learning in electroencephalography (EEG), collecting adequate labelled samples is a critical problem due to laborious and time‐consuming labelling. Recent study proposed to solve the limited label problem via domain adaptation methods. However, they mainly focus on reducing domain discrepancy without considering task‐specific decision boundaries, which may lead to feature distribution overmatching and therefore make it hard to match within a large domain gap completely. A novel self‐training maximum classifier discrepancy method for EEG classification is proposed in this study. The proposed approach detects samples from a new subject beyond the support of the existing source subjects by maximising the discrepancies between two classifiers' outputs. Besides, a self‐training method that uses unlabelled test data to fully use knowledge from the new subject and further reduce the domain gap is proposed. Finally, a 3D Cube that incorporates the spatial and frequency information of the EEG data to create input features of a Convolutional Neural Network (CNN) is constructed. Extensive experiments on SEED and SEED‐IV are conducted. The experimental evaluations exhibit that the proposed method can effectively deal with domain transfer problems and achieve better performance.

Learning the next Point-of-Interest (POI) is a highly context-dependent human movement behavior prediction task, which has gained increasing attention with the consideration of massive spatial-temporal trajectories data or check-in data. The spatial dependency, temporal dependency, sequential dependency and social network dependency are widely considered pivotal to predict the users’ next location in the near future. However, most existing models fail to consider the influence of other users’ movement patterns and the correlation with the POIs the user has visited. Therefore, we propose a Global User Graph Enhanced Network (GUGEN) for the next POI recommendation from a global and a user perspectives. First, a trajectory learning network is designed to model the users’ short-term preference. Second, a geographical learning module is designed to model the global and user context information. From the global perspective, two graphs are designed to represent the global POI features and the geographical relationships of all POIs. From the user perspective, a user graph is constructed to describe each users’ historical POI information. We evaluated the proposed model on three real-world datasets. The experimental evaluations demonstrate that the proposed GUGEN method outperforms the state-of-the-art approaches for the next POI recommendation.

Traffic flow prediction is crucial in smart cities and traffic management, yet it presents challenges due to intricate spatial-temporal dependencies and external factors. Most existing research relied on a traditional data selection approach to represent temporal dependence. However, only considering spatial dependence in adjacent or distant regions limits the performance. In this paper, we propose an end-to-end Parallel Convolution Residual network (PCR) for grid-based traffic flow prediction. First, we introduce a novel data selection strategy to capture more temporal dependence, and then we implement an early fusion strategy without any additional operations to obtain a lighter model. Second, we propose to extract external features with feature embedding matrix operations, which can represent the interrelationships between different kinds of external data. Finally, we build a parallel residual network with concatenated features as input, which is composed of a standard residual net (SRN) to extract short spatial dependence and a dilated residual net (DRN) to extract long spatial dependence. Experiments on three traffic flow datasets TaxiBJ, BikeNYC, and TaxiCQ exhibit that the proposed method outperforms the state-of-the-art models with the most minor parameters.

With the rapid spread of mobile device, technologies in mobile cloud increased quick and introduce huge volume of mobile data and computation. Human movement between POIs are recorded in mobile cloud, which indicate personalized behaviors. Most POI recommendation method in mobile cloud proposed to utilize recurrent neural networks and self-attention mechanism to discover users’ potential movement behaviors. In this paper, we propose a graph-enhanced spatio-temporal interval aware network (GESTIAN) to recommend the next POI. In GESTIAN, we propose a graph-based general pattern learning module to learn common behavior patterns based on a global trajectory flow graph to address the challenges caused by cold start. Furthermore, we propose a heterogeneous network with spatio-temporal interval aware with self-attention and gate recurrent unit to extract users’ long-term and short-term spatio-temporal dependencies, respectively. In addition, we leverage the scale between positive and negative samples by randomly sampling negative samples. We conduct extensive experiments based on two real-world check-in datasets. The experimental evaluations demonstrate that the proposed GESTIAN outperforms most challenging baselines by approximately 2%-10%, and achieves better performance over few-check-in history users.