As a type of data with both temporal and spatial dimensions， spatio?temporal data is widely used in supply chain management， e?commerce and other fields， which integrity and security are of great importance in practical applications. Aiming at the problems of lack of transparency and easily being tampered of data in the current centralized storage of spatial?temporal datasets， a blockchain construction and query method for spatio?temporal data was proposed by combining the decentralized， tamper?proof and traceable characteristics of blockchain technology with spatio?temporal data management. Firstly， an improved Directed Asycline Graph Blockchain （Block?DAG） based blockchain architecture for spatio?temporal data， namely ST_Block?DAG （Spatio?Temporal Block?DAG）， was proposed. Secondly， to improve the efficiency of spatio?temporal data storage and query， a storage structure based on quadtree and single linked list was adopted to store spatio?temporal data in the ST_Block?DAG blockchain. Finally， a variety of spatio?temporal data query algorithms were implemented on the basis of the storage structure of ST_Block?DAG， such as single?value query and range query. Experimental results show that compared with STBitcoin （Spatio?Temporal Bitcoin）， Block?DAG and STEth （Spatio?Temporal Ethereum）， ST_Block?DAG has the spatio?temporal data processing efficiency improved by more than 70% and the comprehensive query performance of spatio?temporal data improved by more than 60%. The proposed method can realize fast storage and query of spatio?temporal data， and can effectively support the management of spatio?temporal data.

During the distributing and downloading of Android application packages, it is always be vulnerable to download hijacking attacks. Traffic analysis could be used by sites to detect if they are under this kind of regular download hijacking attacks. But the stealth download hijacking attacks cannot be discovered by using such a method. Based on the discovery and analysis of an actual case, a vulnerability of Android application package stealth download hijacking was proposed. Attackers exploited this vulnerability to implement a stealth download hijacking through deploying bypass devices between the downloaders and the publishers. And the victim sites can hardly notice it by using current methods. The cause, influence and mechanism of the vulnerability were discussed, and a solution was tried to put forward in respects of distributed detection, centralized analysis and active prevention.

In previous patch-based texture synthesis algorithm, the size of patch requires artificial choice which leads to an uncertainty quality of texture synthesis, so an automatic selection algorithm of patch size for texture synthesis was presented. When the patch was slid on the examplar in scan-line order until getting through all the plane, the histograms of the patch and the examplar were both pretreated by normalization and mean filtering, then the intersection of the two histograms was calculated. Among all the calculation results for different position, the maximum value was selected as the measurement of the color similarity between patches and examplar. Due to approximate monotone increasing relation between color similarity and patchs size, bisection method was adopted to calculate the abscissa for the color similarity threshold point, and the abscissa was used as the patchs size for texture synthesis. The experimental results of various types of textures show that the size of patch automatically selected by this method coincides with the range of best empirical value. Compared with the other automatic selection methods of patchs size, this method not only applies to structured texture synthesis, but also applies to stochastic texture synthesis, and obtains suitable results of texture synthesis.