An ideal artificial intelligence (AI) system should have the capability to continually learn like humans. However, when learning new knowledge, AI systems often suffer from catastrophic forgetting of old knowledge. Although many continual learning methods have been proposed, they often ignore the issue of misclassifying similar classes and make insufficient use of textual priors of visual classes to improve continual learning performance. In this study, we propose a continual learning framework based on a pre-trained vision-language model (VLM) that does not require storing old class data. This framework utilizes parameter-efficient fine-tuning of the VLM's text encoder for constructing a shared and consistent semantic textual space throughout the continual learning process. The textual priors of visual classes are encoded by the adapted VLM's text encoder to generate discriminative semantic representations, which are then used to guide the learning of visual classes. Additionally, fake out-of-distribution (OOD) images constructed from each training image further assist in the learning of visual classes. Extensive empirical evaluations on three natural datasets and one medical dataset demonstrate the superiority of the proposed framework.

Fake news brings us panic and misunderstanding against the truth, especially under some unusual circumstances, such as the outbreak of COVID-19. It’s crucial to detect fake news on social media early to avoid further propagation. Previous methods manually label the stances implied in post-reply pairs to aid fake news detection, which costs much time and effort. To solve this problem, a novel Stance Extraction and Reasoning Network (SERN) is proposed to extract the stances implied in post-reply pairs implicitly and integrate the stance representations for fake news detection without manually labeling stances, which saves much time and effort. Besides, the adequate utilization of multimodal content in the news is beneficial for complementing information for unimodal representation and jointly improving decision confidence. Thus, a sentence-guided visual attention mechanism is proposed in the text-image fusion module that leverages text-image content for better fake news detection. Encouraging empirical results on Fakeddit and PHEME demonstrate that our method outperforms the state-of-the-art methods.

The herb recommendation system aiming for recommending a set of herb for patients is a significant task for Traditional Chinese Medicine (TCM). Recent works apply a graph convolutional network to model the relations among symptoms and herbs, showing promising performance. However, they typically suffer from two limitations: (1) The learning of the relations of symptoms and herbs from symptom-herb heterogeneous graphs would be disturbed by the semantic gap and the weak correlations between symptoms and herbs. (2) They ignore the complex diagnosis and systemic relations of a patient's multi-symptom, resulting in the lack of effectiveness and personalization in syndrome diagnosis. To overcome these limitations, we propose a novel Homogeneous Symptom Graph Attentive Reasoning Network (HSGARN). Firstly, to alleviate the noisy semantic gap and weak correlations of heterogeneous graphs, we propose a homogeneous graph embedding module to comprehensively model the semantic relations of symptoms and herbs. Secondly, we propose a symptom attentive reasoning module to generate syndrome representation for patients, which can sufficiently exploit the interrelation of a patient's symptoms and model the individual difference. Experimental results on two TCM datasets demonstrate the advantages of HSGARN over the state-of-the-arts.

After the sale of goods, more and more manufacturers provide product-supplementary services. This paper analyzes the structure of the service industry under duopoly market. The two periods Bertrand Nash game is used to discuss the competition among two manufacturers and one service provider. The two manufacturers can provide similar products and the service provider can only provide the similar product-supplementary service. It shows that the service provider can reduce the entry barriers to consumers and improve consumer welfare. In the short term, the manufacturers can gain more market share by increasing the switch cost. However, in the long run, the method of increasing the switch cost must reduce the motivation for consumers to choose services, which results in the overall market slump. Not only do the service provider lose much market share, but also the two manufacturers.