The real-time monitoring data generated by high-speed trains during running are usually processed by variable-length coding compression technology, which is convenient for transmission and storage. However, this method will complicate the internal structure of the compressed data, so that the corresponding data decompression process must follow the composition order of the compressed data, which is inefficient. In order to improve the decompression efficiency of high-speed train monitoring data, a parallel decompression algorithm for high-speed train monitoring data was proposed with the help of the speculation technology. Firstly, the structural characteristics of high-speed train monitoring data were studied, and the internal dependence that affects data division was analyzed. Secondly, the speculation technology was used to clean up internal dependence, and then, the data were divided into different parts tentatively. Thirdly, the division results were decompressed in a distributed computing environment in parallel. Finally, the parallel decompression results were combined together. Through this way, the decompression efficiency of high-speed train monitoring data was improved. Experimental results showed that on the computing cluster composed of 7 computing nodes, compared with the serial algorithm, the speedup of the proposed speculative parallel algorithm was about 3, showing a good performance of this algorithm. It can be seen that this algorithm can improve the monitoring data decompression efficiency significantly.

Web applications are under the threat from malicious host problem just as native applications. How to ensure the core algorithm or main business process's security of Web applications in browser-side has become a serious problem needed to be solved. For the problem of low effectiveness to resist dynamic analysis and cumulative attack in present JavaScript code protection methods, a JavaScript code Protection based on Temporal Diversity (TDJSP) method was proposed. In order to resist cumulative attack, the method firstly made the JavaScript program obtain the diverse ability in runtime by building program's diversity set and obfuscating its branch space. And then, it detected features of abnormal execution environments such as debuggers and emulations to improve the difficulty of dynamic analysis. The theoretical analyses and experimental results show that the method improves the ability of JavaScript program against the converse analysis. And the space growth rate is 3.1 (superior to JScrambler3) while the delay time is in millisecond level. Hence, the proposed method can protect Web applications effectively without much overhead.