Understanding event and event-centered commonsense reasoning are crucial for natural language processing (NLP). Given an observed event, it is trivial for human to infer its intents and effects, while this type of If-Then reasoning still remains challenging for NLP systems. To facilitate this, a If-Then commonsense reasoning dataset Atomic is proposed, together with an RNN-based Seq2Seq model to conduct such reasoning. However, two fundamental problems still need to be addressed: first, the intents of an event may be multiple, while the generations of RNN-based Seq2Seq models are always semantically close; second, external knowledge of the event background may be necessary for understanding events and conducting the If-Then reasoning. To address these issues, we propose a novel context-aware variational autoencoder effectively learning event background information to guide the If-Then reasoning. Experimental results show that our approach improves the accuracy and diversity of inferences compared with state-of-the-art baseline methods.

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Component assignment problem is a common challenge of reliability optimization, which is a non-deterministic polynomial hard problem widely used in the linear consecutive k-out-of-n systems. In consideration of the advantages of quantum computing and importance measure, this article proposed a novel algorithm, which is Birnbaum importance-based quantum genetic algorithm, to improve the efficiency and accuracy for solving component assignment problem. First, the model of reliability optimization for linear consecutive k-out-of-n systems is established. Second, the detailed procedure of Birnbaum importance-based quantum genetic algorithm is introduced to solve the component assignment problem. Moreover, the effectiveness and the convergence of the quantum genetic algorithm, Birnbaum importance-based genetic local search, and Birnbaum importance-based quantum genetic algorithm is discussed through two comparative experiments. Finally, the case of production monitor systems is introduced to illustrate the effectiveness of Birnbaum importance-based quantum genetic algorithm comparing with the Birnbaum importance-based two-stage approach.

An outcome of upward social comparisons that has been largely overlooked is its effect on non-transactional behaviours (i.e., word of mouth). Previous research has identified three different emotional reactions to upward social comparisons: admiration, benign envy and malicious envy. Despite the fact that their effect on consumption has been previously analysed, it remains unclear how these reactions affect word of mouth intention. This study carries out an experimental design that demonstrates that admiration and benign envy positively influence word of mouth behaviour. However, there is no effect of malicious envy on such disposition. The results are sustained under different cultural contexts. The findings shed light on the drivers of word of mouth. They offer guidance to companies for developing more effective strategies to encourage both brand message sharing and consumer-to-consumer sharing of consumption experiences.

Prior work infers the causation between events mainly based on the knowledge induced from the annotated causal event pairs. However, additional evidence information intermediate to the cause and effect remains unexploited. By incorporating such information, the logical law behind the causality can be unveiled, and the interpretability and stability of the causal reasoning system can be improved. To facilitate this, we present an Event graph knowledge enhanced explainable CAusal Reasoning framework (ExCAR). ExCAR first acquires additional evidence information from a large-scale causal event graph as logical rules for causal reasoning. To learn the conditional probabilistic of logical rules, we propose the Conditional Markov Neural Logic Network (CMNLN) that combines the representation learning and structure learning of logical rules in an end-to-end differentiable manner. Experimental results demonstrate that ExCAR outperforms previous state-of-the-art methods. Adversarial evaluation shows the improved stability of Ex-CAR over baseline systems. Human evaluation shows that ExCAR can achieve a promising explainable performance.

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