Although few-shot learning (FSL) has achieved great progress, it is still an enormous challenge especially when the source and target sets are from different domains, which is also known as cross-domain few-shot learning (CD-FSL). Utilizing more source domain data is an effective way to improve the performance of CD-FSL. However, knowledge from different source domains may entangle and confuse with each other, which hurts the performance on the target domain.Although few-shot learning (FSL) has achieved great progress, it is still an enormous challenge especially when the source and target sets are from different domains, which is also known as cross-domain few-shot learning (CD-FSL). Utilizing more source domain data is an effective way to improve the performance of CD-FSL. However, knowledge from different source domains may entangle and confuse with each other, which hurts the performance on the target domain.[#item_full_content]

Finding tenuous groups, those with few social interactions and weak relationships among members, has been a hot topic in community search for reviewer selection and psycho-educational group formation. The existing metrics (e.g., k-triangle, k-line, and k-tenuity) used to measure the tenuity require a suitable k value to be specified, which is difficult for users without background knowledge.Finding tenuous groups, those with few social interactions and weak relationships among members, has been a hot topic in community search for reviewer selection and psycho-educational group formation. The existing metrics (e.g., k-triangle, k-line, and k-tenuity) used to measure the tenuity require a suitable k value to be specified, which is difficult for users without background knowledge.[#item_full_content]