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Fuzzing is a technique widely used in vulnerability detection. The process usually involves writing effective fuzz driver programs, which, when done manually, can be extremely labor intensive. Previous attempts at automation leave much to be desired, in either degree of automation or quality of output. In this paper, we propose IntelliGen, a framework that constructs valid fuzz drivers automatically. First, IntelliGen determines a set of entry functions and evaluates their respective chance of exhibiting a vulnerability. Then, IntelliGen generates fuzz drivers for the entry functions through hierarchical parameter replacement and type inference. We implemented IntelliGen and evaluated its effectiveness on real-world programs selected from the Android Open-Source Project, Google's fuzzer-testsuite and industrial collaborators. IntelliGen covered on average 1.08X-2.03X more basic blocks and 1.36X-2.06X more paths over state-of-the-art fuzz driver synthesizers FUDGE and FuzzGen. IntelliGen performed on par with manually written drivers and found 10 more bugs.

We developed a deep learning prototype for the purpose of rainfall prediction for the city of Beijing China. It predicts rainfall dynamics in the next two hours with spatial reso-lution of 1km and temporal resolution of 6 minutes. That is a magnitude better than traditional weather forecasting. The computation time is less than 10 seconds, several magnitudes of less computation time than traditional forecasting. This deep learning network combines the strengths of known structures such as U-Net, ResNet, Sqeeze-and-Excitation, and the spatial Attention mod-ule. We rely solely on full convolutional layers instead of RNN layers as used in other weather prediction ef-forts. Meteorologically assessment metrics suggest better performance by the FCN approach. The prototype is now up and running in anticipation for the summer rain season of 2019. This might be the first such service employed by a capital weather service.

Improving I/O performance of large-scale computing systems has become increasingly dependent on the efficiency of parallel file systems. A common way to deploy parallel file systems is using the user space file system framework (e.g. FUSE), which introduces an extra I/O interposition layer that may cause considerable overhead due to excessive utilization of kernel crossings and system function calls. These slowdowns have been exacerbated due to the growing isolation of user and kernel space, increasing kernel crossing overhead significantly. In this paper, we present our findings on the evaluation of how FUSE affects the efficiency of a popular parallel file system, the Parallel Log-structured File System (PLFS). We then suggest a means to mitigate this issue by removing FUSE from the equation and demonstrate its viability with a proof of concept library.

Along to the durative increase of the battlefield situation information, the graphically visualize of which becomes a more and more complicated problem. How to plot the operational symbols on the basic geographical map suitably, clearly and completely deserves more consideration. This article bases on the presentation of huge battlefield information, make a specific research on fusing the operational symbols automatically in different echelon, different task, and different scale, which includes the deployment, aggregation, splitting and the transformation from point symbol to line or area, analyses the basic concepts of symbol fusion and the main problems when using symbols to present the situation. At last, we suggest specific methods of symbol fusion.

The credibility validation mode of the artificial urban rail transportation system is studied in this paper. Based on the analysis of credibility validation in the artificial urban rail transportation system, the validation mode which includes the validation interval, validation object and validation method of artificial urban rail transportation are established. Finally, the credibility of train agent is validated to show the validation process of agents which are used to “emerging” the parallel urban rail transportation system.

This study developed a new model for the low NOxignition for the direct flow of pulverized coal, which enabled the influence of the primary air and internally recirculated flue gas (RFG) on NOx formation to be investigated. A model of the preignition zone was discussed in detail. Three new parameters, the Local Stoichiometric Ratio of Volatiles(LSRV), the Virtual Temperature of Ignition (VTI) and a Gxnumber to indicate NOx reduction potential were introduced to analyze the influence of the primary air/RFG parameters on low NOx ignition. It was found that coal volatiles and size had a major impact on establishing the primary air parameters and ignition position which are favorable for low NOxcombustion. The model can be used to determine the necessary mixing parameters for the design or modification of boilers for low NOx coal combustion.

Disposable printed sensor tapes are being developed to record the magnitude of explosive blasts in the battlefield. The goal is to detect and mitigate the possible occurrence of traumatic brain injury in soldiers. This paper presents results on the pressure sensors to measure the blast pressure and the blast noise. The sensors are based on piezoelectric polymers and their fabrication is compatible with roll-to-roll fabrication methods to enable low cost.

In three-phase four-wire distribution power system, the loads are not balanced because of optional running, uncertain capacity and unpredictable kinds. Unbalanced power distribution system leads great transmission lines loss. Electromagnetic balancing technology can improve unbalanced phenomena in three-phase four-wire system dynamically, and reduces transmission lines loss to get the goal of saving energy. Electromagnetic balancing technology is partly composed of Efficiency Power Plant (EPP). If electromagnetic balancing technology is widely used, EPP can be constructed efficiently. Electromagnetic balancing technology theory is discussed in paper, some data is used to test the theory. An actual instrument is produced to apply in distribution system, the measured electrical data in field shows that electromagnetic balancing technology is effective. At last, results simulated by SimPowerSystems/MATLAB and actual data make the following conclusions: a. improving unbalanced status of three-phase four-wire distribution power system; b. reducing neutral line current and restraining harmonic components. c. decreasing the loss of distribution transformer, 10kV transmission lines and 380V transmission lines. Experimental data and simulation results show that electromagnetic balancing technology is efficient and practical in distribution network, as well as the distribution network based on distributed energy-efficient power plant construction is feasible.

Microarray gene expression data has high dimension and small samples, the gene selection is very important to the classification accuracy. In this paper, we present a scheme of recursive feature addition for microarray gene expression classification based on supervised learning and the similarity measure between chosen genes and candidates. In comparison with the well-known gene selection methods of T-TEST and SVM-RFE using different classifiers, our method, on the average, performs the best regarding the classification accuracy under different feature dimensions, the mean test accuracy and the highest test accuracy under the highest train accuracy, and the highest test accuracy in the experiments.