The key to improve the service effect of virtual digital human is the personality trait recognition technology of users. However, the complexity of Chinese and the sparsity of short texts limit the accuracy of personality trait recognition. Therefore, we applied emotional features to the research of personality trait recognition and constructed a deep learning model for Chinese corpus personality trait recognition to improve the accuracy of personality trait recognition. The model ([Formula: see text] extracted semantic features and embedded emotional features through RoBERTa-wwm-ext Chinese pre-training model, BiLSTM neural network, and attention mechanism. Some experiments were carried out using two Chinese datasets with different scenes. The comparative experiments and ablation experiments showed that the overall effect of the personality trait recognition model ([Formula: see text] is the best, and the Macro-F1 value reaches 75.3%, which is significantly better than the existing models. The results showed that emotional features can significantly improve the accuracy of multi-label personality trait classification in both Chinese daily life scenarios and online scenarios. The proposed model can integrate emotional features and Chinese semantic features to improve the accuracy of personality traits recognition. These findings support the development of personality trait recognition and the application of virtual digital human.

Caching the most popular contents at the wireless network edge such as small-cell base stations (SBSs) is a smart way of reducing duplicated content transmissions and offloading the mobile data traffic in the network backhaul. Currently, most small-cell caching strategies are conceived, designed, and optimized based on the global content request probability (GCRP), with very limited consideration of the individual content request probability (ICRP) reflecting personal preferences. To enable more efficient wireless caching, in this paper, we propose a novel localized deterministic caching framework, drawing upon the recent advances in recommendation systems based on machine learning techniques. By introducing the concept of the rating matrix, we first propose a new Bayesian learning method to predict personal preferences and estimate the ICRP. This crucial information is then incorporated into our caching strategy for maximizing the system throughput, or equivalently, minimizing the download latency, where a deterministic caching algorithm based on reinforcement learning is proposed to optimize the content placement. To this end, we extend the framework to enable device-to-device (D2D) connections to further reduce the download delay, and also design a feedback mechanism to improve the accuracy in the ICRP estimation. Our simulation results verified that with the estimated ICRP and the proposed caching strategy, the proposed framework can significantly outperform the existing methods in terms of hit rate and system throughput.

Spectrum sensing in heterogeneous networks is very challenging as it usually requires a large number of static secondary users (SUs) to capture the global spectrum states. In this paper, we tackle the spectrum sensing in heterogeneous networks from a new perspective. We exploit the mobility of multiple SUs to simultaneously collect spatial-temporal spectrum sensing data. Then, we propose a new non-parametric Bayesian learning model, referred to as beta process hidden Markov model to capture the spatio-temporal correlation in the collected spectrum data. Finally, Bayesian inference is carried out to establish the global spectrum picture. Simulation results show that the proposed algorithm can achieve a significant spectrum sensing performance improvement in terms of receiver operating characteristic curve and detection accuracy compared with other existing spectrum sensing algorithm.