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Abstract. In view of the problem of the subjectivity and some factors are hard to quantify in the process of selecting the agent for government investment projects. Draw on relevant research results,analysis of the factors affecting the comprehensive ability of the a gent and establish a comprehensive ability evaluation system of the agent,. AHP-fuzzy comprehensive evaluation method is used to quantify and analyze the influencing factors. The results show that: the AHP-fuzzy comprehensive evaluation method is suitable for the s election of the agent, which can greatly improve the efficiency and the scientific nature of the comprehensive ability of the construction unit.

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Adversarial attack on point clouds plays a vital role in evaluating and improving the adversarial robustness of 3D deep learning models. Current attack methods are mainly applied by point perturbation in a non-manifold manner. In this paper, we formulate a novel manifold attack, which deforms the underlying 2-manifold surfaces via parameter plane stretching to generate adversarial point clouds. First, we represent the mapping between the parameter plane and underlying surface using generative-based networks. Second, the stretching is learned in the 2D parameter domain such that the generated 3D point cloud fools a pretrained classifier with minimal geometric distortion. Extensive experiments show that adversarial point clouds generated by manifold attack are smooth, undefendable and transferable, and outperform those samples generated by the state-of-the-art non-manifold ones.