Virtual human is widely employed in various industries, including personal assistance, intelligent customer service, and online education, thanks to the rapid development of artificial intelligence. An anthropomorphic digital human can quickly contact people and enhance user experience in human–computer interaction. Hence, we design the human–computer interaction system framework, which includes speech recognition, text-to-speech, dialogue systems, and virtual human generation. Next, we classify the model of talking-head video generation by the virtual human deep generation framework. Meanwhile, we systematically review the past five years’ worth of technological advancements and trends in talking-head video generation, highlight the critical works and summarize the dataset.

Virtual simulation can solve the challenges of high cost, long cycle time, and inaccessibility in traditional experimental teaching, which is far-reaching for talent training. This study combines bibliometric visualization theory with AHP (Analytic Hierarchy Process). It establishes a hierarchical evaluation model of a virtual simulation experimental teaching platform based on 842 questionnaires and 4787 articles, including 68,306 citation records, and deconstructing the complex evaluation problem into several multidimensional factors by attributes and relationships. Based on this, a virtual simulation experimental teaching platform construction scheme for IP protocol analysis based on a network covert communication perspective is outputted, which is compatible with the research results. The experimental platform takes a task-driven teaching method as the core, mainly including four modules of context creation, task determination, independent learning, and effect evaluation. The experience of building this platform can be extended to other disciplines, leading the teaching reform exploration of practice-based, innovation-focused, and engineering-critical, helping to implement the flipped classroom, and promoting the development of education modernization.

Named entity recognition (NER) is a subfield of natural language processing (NLP) that identifies and classifies entities from plain text, such as people, organizations, locations, and other types. NER is a fundamental task in information extraction, information retrieval, and text summarization, as it helps to organize the relevant information in a structured way. The current approaches to Chinese named entity recognition do not consider the category information of matched Chinese words, which limits their ability to capture the correlation between words. This makes Chinese NER more challenging than English NER, which already has well-defined word boundaries. To improve Chinese NER, it is necessary to develop new approaches that take into account category features of matched Chinese words, and the category information would help to effectively capture the relationship between words. This paper proposes a Prompt-based Word-level Information Injection BERT (PWII-BERT) to integrate prompt-guided lexicon information into a pre-trained language model. Specifically, we engineer a Word-level Information Injection Adapter (WIIA) through the original Transformer encoder and prompt-guided Transformer layers. Thus, the ability of PWII-BERT to explicitly obtain fine-grained character-to-word relevant information according to the category prompt is one of its key advantages. In experiments on four benchmark datasets, PWII-BERT outperforms the baselines, demonstrating the significance of fully utilizing the advantages of fusing the category information and lexicon feature to implement Chinese NER.

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