Deep neural network (DNN) based text intelligence softwares have proliferated and have been widely used. However, they face challenges such as robustness. Testing is essential to DNNs in real-world applications. Researchers have proposed various testing techniques for generating test cases. With the black-box hard-label setup where only the output label of the DNN is accessible, related studies mainly concentrated on generating the new text test case by iteratively perturbing single initial test case, which limits the effectiveness. To address this issue, this paper proposes a new text test case generation method MVGText (Momentum and Variance Guided hard-label Text test case generation) for text-oriented DNN, which generates high-quality text test cases by searching based on multiple initial test cases. Firstly, multiple initial text test cases are generated randomly, in which global fixed word replacement is employed to enhance the success rate. Then, momentum and variance are applied to guide the search for the more imperceptible test case. Finally, greedy optimization is used to obtain a higher quality test case. Experimental evaluation shows that MVGText achieves an average post-test success rate of 1.4%. Furthermore, the test cases generated by MVGText exhibit the highest similarity to the original text compared to other comparative methods.

Inferring new facts from an existing knowledge graph with explainable reasoning processes is an important problem, known as knowledge graph (KG) reasoning. The problem is often formulated as finding the specific path that represents the query relation and connects the query entity and the correct answer. However, due to the limited expressiveness of individual paths, the majority of previous works failed to capture the complex subgraph structure in the graph. We propose CogKR that traverses the knowledge graph to conduct multi-hop reasoning. More specifically, motivated by the dual process theory from cognitive science, our framework is composed of an extension module and a reasoning module. By setting up a cognitive graph through iteratively coordinating the two modules, CogKR can cope with more complex reasoning scenarios in the form of subgraphs instead of individual paths. Experiments on three knowledge graph reasoning benchmarks demonstrate that CogKR achieves significant improvements in accuracy compared with previous methods while providing the explainable capacity. Moreover, we evaluate CogKR on the challenging one-shot link prediction task, exhibiting the superiority of the framework on accuracy and scalability compared to the state-of-the-art approaches.