Biblio

Recent years, more and more testing criteria for deep learning systems has been proposed to ensure system robustness and reliability. These criteria were defined based on different perspectives of diversity. However, there lacks comprehensive investigation on what are the most essential diversities that should be considered by a testing criteria for deep learning systems. Therefore, in this paper, we conduct an empirical study to investigate the relation between test diversities and erroneous behaviors of deep learning models. We define five metrics to reflect diversities in neuron activities, and leverage metamorphic testing to detect erroneous behaviors. We investigate the correlation between metrics and erroneous behaviors. We also go further step to measure the quality of test suites under the guidance of defined metrics. Our results provided comprehensive insights on the essential diversities for testing criteria to exhibit good fault detection ability.

Industrial cluster is an important organization form and carrier of development of small and medium-sized enterprises, and information service platform is an important facility of industrial cluster. Improving the credibility of the network platform is conducive to eliminate the adverse effects of distrust and information asymmetry on industrial clusters. The decentralization, transparency, openness, and intangibility of block chain technology make it an inevitable choice for trustworthiness optimization of industrial cluster network platform. This paper first studied on trusted standard of industry cluster network platform and construct a new trusted framework of industry cluster network platform. Then the paper focus on trustworthiness optimization of data layer and application layer of the platform. The purpose of this paper is to build an industrial cluster network platform with data access, information trustworthiness, function availability, high-speed and low consumption, and promote the sustainable and efficient development of industrial cluster.