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Every year, earthquakes result in severe economic losses and a significant number of casualties worldwide. In limiting the losses that occur after these extreme events, timely and accurate assessment of seismic damages and mobilizing proportionate post‐event relief efforts play crucial roles. Traditional on‐site investigation generally results in prolonged evaluation windows. Several computational alternatives exist that show promise in addressing the downsides of the traditional approach. Damage estimates based on pre‐computed fragility libraries can provide near‐real time seismic damage quantification, but at present, they are coarse and involve considerable uncertainties. Estimates based on nonlinear time‐history analyses simulate the seismic response in greater detail, yet due to the computation and data requirements, their use at the regional scale is challenging. Given this perspective, herein, a rapid regional post‐event seismic damage assessment procedure based on convolutional neural network (CNN) is proposed. In this approach, an inventory of buildings, anticipated ground motion datasets, and corresponding damage levels for a region are brought together into a scenario bank. The time‐frequency distribution graphs of the ground motions, which serve as detailed visual representations of their frequency‐domain as well as time‐domain features, are generated. These data are then used to train CNN models, which could predict the damage states. The proposed methodology is verified through two numerical studies—one for an individual building, and the other, regional case, involving the buildings in the Tsinghua University campus. The results confirm that the proposed method offers prediction results with sufficient accuracy in near real‐time.

Privacy is one of the most important issues in social social network data sharing. Structure anonymization is a effective method to protect user from being reidentfied through graph modifications. The data utility of the distorted graph structure after the anonymization is a really severe problem. Reducing the utility loss is a new measurement while k-anonymity as a criterion to guarantee privacy protection. The existing anonymization algorithms that use vertex's degree modification usually introduce a large amount of distortion to the original social network graph. In this paper, we present a k-degree anonymity with vertex and edge modification algorithm which includes two phase: first, finding the optimal target degree of each vertex; second, deciding the candidates to increase the vertex degree and adding the edges between vertices to satisfy the requirement. The community structure factors of the social network and the path length between vertices are used to evaluated the anonymization methods. Experimental results on real world datasets show that the average relative performance between anonymized data and original data is the best with our approach. B Tinghuai Ma