A number of security and privacy challenges of cyber system are arising due to the rapidly evolving scale and complexity of modern system and networks. The cyber system is a fundamental ingredient for Internet of Things (IoT) and smart city which are driven by huge amount of data. These data carry a lot of information for mining and analysis, especially trajectory data. If unprotected trajectory data is released, it may disclose user’s personal privacy, such as home, religion, and behavior mode, which will endanger their personal security. Until now, many methods for protecting trajectory information have been proposed. However, these methods have the following deficiencies: (i) they cannot defend against speculative attacks if the attacker’s background knowledge is maximized; (ii) when studying the problem, they made some strong assumptions that did not match the reality; (iii) the implementation algorithm is complicated and the time complexity is high, which means that data cannot be executed quickly when the amount is large. So, in this paper, we propose a spatial partition based method to publish trajectory data via differential privacy. First, by exponential mechanism, we divide location set at the same time into different partitions fast and accurately. Then we propose another effective method to release trajectory in a differential private manner. We design experiment based on the real-life dataset and compare it with existing method. The results show that the trajectory dataset released by our algorithm has better usability while ensuring privacy.

The mesoscopic simulation of behaviours of cementitious materials under different conditions has become a hot topic in academic research, as it provides more details to the mechanism study and structural design. To conduct a mesoscopic simulation, the meso-scale model of cementitious materials must be built. To ensure the precision of the aggregate shape in the simulated meso-scale model, key shape parameters of real aggregates should be identified. In this paper, an image-processing based method is proposed to detect the aspect ratio of a polygonal aggregate. The procedure and used algorithms are demonstrated in detail. As an application, totally about 1000 coarse aggregates from the Xinjiang, China are selected to identify the aspect ratio. It is found that the aspect ratio of coarse aggregates is a random variable following the Generalized Extreme Value (GEV) distribution. The published data by using the X-ray technique is also adopted as a comparison, and the results are almost the same as each other, which indicates that the aggregate source does not have an obvious effect on the probabilistic characteristics of the aspect ratio.

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U-Net++ is one of the most prominent deep convolutional neural networks in the field of medical image segmentation after U-Net. However, the semantic gaps between the encoder and decoder subnets are still large, which will lead to fuzzy feature maps and even target regions of segmentation. To solve this problem, we propose an improved semantic segmentation model utilizing channel attention mechanism and Laplacian sharpening filter, SCU-Net++: dense skip connections are redesigned with sharpening filters to ease the semantic gaps, and channel attention modules are used to make the model pay more attention on the feature maps that are useful for our pixel-level classification task. Compared with U-Net++, the proposed model obtains a more competitive performance on the Pancreas Segmentation dataset and Liver Tumor Segmentation dataset, while increases a very small number of learnable parameters and thus almost does not make additional training and reasoning costs. The training of the proposed method is carried out in deep supervision mode, which alleviates the problem of gradient disappearance, and pruning mechanism can be activated to accelerate the reasoning speed.

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