Over the past decade, the application of deep learning in code clone detection has produced remarkable results. However, the current approaches have two limitations: (a) code representation approaches with low information utilization, such as vanilla Abstract Syntax Tree (AST), leading to information redundancy which results in performance degradation; (b) low efficiency of clone detection on evaluation, resulting in excessive time costs during practical use. In this paper, we propose a Multiple Representation Transformer with an Optimized Abstract Syntax Tree (MRT-OAST) to introduce an efficient code representation method while achieving competitive performance. Specifically, MRT-OAST strategically prunes and enhances the AST, utilizing both pre-order and post-order traversals to represent two different representations. To speed up the evaluation process, MRT-OAST utilizes a pure Siamese Network and employs cosine similarity to compare the similarity between codes. Our approach effectively reduces AST sequences to 40 % and 39 % of their original length in Java and C/C++ while preserving structural information. In code clone detection tasks, our model surpasses state-of-the-art approaches on OJClone and Google Code Jam. During the evaluation of BigCloneBench, our model has a 5x speed improvement compared to the state-of-the-art lightweight model and a 563x speed improvement compared to the BERT-based model, with only a 0.3 % and 0.9 % decrease in$F_{1}$-score.

Relation prediction in knowledge graphs is a classic task. However, predicting unseen facts under the open-world assumption remains challenging, as conventional closure reasoning methods tend to fail. To address this, we propose an uncertain knowledge graph embedding and Bayesian inferring model, UKGEBN, which examines the capabilities of approximate inference under the open-world assumption. This model employs a large language model to encode entities and relations by treating facts as natural language sentences. It then learns knowledge confidence through a recurrent neural network and subsequently constructs Bayesian networks for the uncertain knowledge graph based on the learned confidence. The constructed Bayesian network can reasonably infer unseen knowledge facts, even if the elements of these facts-such as new entities and relationships-have never been encountered in the knowledge base before. The experimental results show that the new model has improved the MSE metric by 24.6% in relation prediction on the benchmark dataset. Case studies indicate that the model significantly enhances the revelation of latent semantic information, facilitating the prediction of implicit relationships in uncertain knowledge graphs.

So far, many nonmonotonic logics are available to deal with the problem of reasoning about completeness, consistency and priorities of the knowledge. The two representative formalisms of them are default reasoning and belief revision, on which the frameworks of Brewka's default theories and Rodrigues' structured clusters are based, respectively. In this paper, we present a framework with the meta-formalism of open logic based on a partial-ordered partition model. Via this formalism, we give model-theoretic interpretations for the two frameworks above. Taking into account computational aspects, we define their corresponding epistemic processes in turn. In this sense, our framework can be viewed as a generalization of them. Our framework are particularly suitable for the reasoning of requirements evolution.