Exploring evidence from relevant articles to confirm the veracity of claims is a trend towards explainable claim verification. However, most strategies capture the top-k check-worthy articles or salient words as evidence, but this evidence is difficult to focus on the questionable parts of unverified claims. Besides, they utilize relevant articles indiscriminately, ignoring the source credibility of these articles, which may cause quiet a few unreliable articles to interfere with the assessment results. In this paper, we propose Evidence-aware Hierarchical Interactive Attention Networks (EHIAN) by considering the capture of evidence fragments and the fusion of source credibility to explore more credible evidence semantics discussing the questionable parts of claims for explainable claim verification. EHIAN first designs internal interaction layer (IIL) to strengthen deep interaction and matching between claims and relevant articles for obtaining key evidence fragments, and then proposes global inference layer (GIL) that fuses source features of articles and interacts globally with the average semantics of all articles and finally earns the more credible evidence semantics discussing the questionable parts of claims. Experiments on two datasets demonstrate that EHIAN not only achieves the state-of-the-art performance but also secures effective evidence to explain the results.

Mind evolutionary computation (MEC) is a new approach of evolutionary computation (EC) proposed by Chengyi Sun in 1998. It has excellent performances on various aspects. Operations of similartaxis and dissimilation are employed in MEC rather than crossover and mutation operators in GAs, so the performance have improved a lot. The excellent performances are accomplished also due to three mechanisms of MEC, which are evolutionary directionality mechanism, memory mechanism and harmony mechanism between exploitation and exploration. This paper surveys the study and the main results during 1998-2001. The results demonstrate that a preliminary system has already been established for MEC. A detailed and complete description of MEC by pseudo codes is given first time. A modified result on convergence velocity of similartaxis process and some changes of glossaries are also given in the paper.