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In probabilistic failure risk assessment, the accuracy and efficiency of the stress intensity factor calculation are important. The universal weight function method has been widely adopted for efficiency, but this method still has some debatable parts. For accurate and efficient stress intensity factor prediction, two approaches for machine learning techniques are specially designed. Three tests are conducted for the first approach where Gaussian process regression, tree-structure models, and artificial neural network are evaluated and compared for the ability of interpolation and extrapolation. Results show that the artificial neural network and extremely randomized trees perform better.Hybrid models in the second approach are also proposed, and results show that the accuracy of SIF calculation can be improved by 5-35% compared with the weight function. Real aeroengine disks are adopted, and the errors of machine learning methods are less than 20% in the disk life calculation.

Keyphrase Generation compresses a document into some highly-summative phrases, which is an important task in natural language processing. Most state-of-the-art adopt greedy search or beam search decoding methods. These two decoding methods generate a large number of duplicated keyphrases and are time-consuming. Moreover, beam search only predicts a fixed number of keyphrases for different documents. In this paper, we propose an adaptive generation model-AdaGM, which is mainly inspired by the importance of the first words in keyphrase generation. In AdaGM, a novel reset state training mechanism is proposed to maximize the difference in the predicted first words. To ensure the discreteness and get an appropriate number of keyphrases according to the content of the document adaptively, we equip beam search with a highly effective filter mechanism. Experiments on five public datasets demonstrate the proposed model can generate marginally less duplicated and more accurate keyphrases. The codes of AdaGM are available at: https://github.com/huangxiaolist/adaGM.

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Netflow-based network traffic analysis is one of today's mainstream network traffic monitoring and analysis techniques. However, port-based traffic identification that employed in Netflow-based protocol analysis is inaccurate. Netflow cannot provide enough details for analysis of site access behaviour. In order to overcome the above limitation, we propose an enhanced Netflow data collection method combining packet capturing and flow technique for accurate application identification. Then we design and implement an enhanced Netflow data collection system, which lays a data foundation for traffic analysis. Finally, the feasibility and effectiveness of the system are verified.

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