A New Framework for Software Vulnerability Detection Based on an Advanced Computing

The detection of software vulnerabilities written in C and C++languages takes a lot of attention and interest today.This paper proposes a new framework called DrCSE to improve software vulnerability detection.It uses an intelligent computation technique based on the combination of two methods:Rebalancing data and representation learning to analyze and evaluate the code property graph(CPG)of the source code for detecting abnormal behavior of software vulnerabilities.To do that,DrCSE performs a combination of 3 main processing techniques:(i)building the source code feature profiles,(ii)rebalancing data,and(iii)contrastive learning.In which,the method(i)extracts the source code’s features based on the vertices and edges of the CPG.The method of rebalancing data has the function of supporting the training process by balancing the experimental dataset.Finally,contrastive learning techniques learn the important features of the source code by finding and pulling similar ones together while pushing the outliers away.The experiment part of this paper demonstrates the superiority of the DrCSE Framework for detecting source code security vulnerabilities using the Verum dataset.As a result,the method proposed in the article has brought a pretty good performance in all metrics,especially the Precision and Recall scores of 39.35%and 69.07%,respectively,proving the efficiency of the DrCSE Framework.It performs better than other approaches,with a 5%boost in Precision and a 5%boost in Recall.Overall,this is considered the best research result for the software vulnerability detection problem using the Verum dataset according to our survey to date.

Development of a Broadly Tunable Mid-Infrared Laser Source for Highly Sensitive Multiple Trace Gas Detection

A room-temperature broadly tunable mid-infrared difference frequency laser source for highly sensitive trace gas detection has been developed recently in our laboratory. The mid-infrared laser system is based on quasi-phase-matched （QPM） difference frequency generation （DFG） in a multigrating, temperature-controlled periodically poled LiNbO3 （PPLN） crystal and employs two near-infrared diode lasers as pump sources. The mid-infrared coherent radiation generated is tunable from 3.2 μm to 3.7μm with an output power of about 100 μW. By changing one of the pump laser head with another wavelength range, we can readily obtain other needed mid-infrared wavelength range cover. The performance of the mid-infrared laser system and its application to highly sensitive spectroscopic detection of CH4, HCl, CH2O, and NO2 has been carried out. A multi-reflection White cell was used in the experiment gaining ppb-level sensitivity. The DFG laser system has the features of compact, room-temperature operation, narrow line-width, and broadly continuous tunable range for potential applications in industry and environmental monitoring.

Technology of fiber-optic temperature sensing and its application in ＆ temperature measuring of gob area

Based on advantages of technology of distributive fiber-optic temperature sensing and specific to its applications in monitoring mine conflagration, the corresponding Processes such as connection arrangement, signal transmission and monitoring were illustrated. As applied in Sitai Coal Mine of Datong Coal Mine Group Co., this method is effective and accurate and could provide reliable gist for monitoring spontaneous combustion in gob area of mines.

COMPUTATIONALLY EFFICIENT ESPRIT METHOD FOR DIRECTION FINDING

In this paper,a low complexity ESPRIT algorithm based on power method and Orthogo- nal-triangular (QR) decomposition is presented for direction finding,which does not require a priori knowledge of source number and the predetermined threshold (separates the signal and noise ei- gen-values).Firstly,according to the estimation of noise subspace obtained by the power method,a novel source number detection method without eigen-decomposition is proposed based on QR de- composition.Furthermore,the eigenvectors of signal subspace can be determined according to Q matrix and then the directions of signals could be computed by the ESPRIT algorithm.To determine the source number and subspace,the computation complexity of the proposed algorithm is approximated as (2log_2 n+2.67)M^3,where n is the power of covariance matrix and M is the number of array ele- ments.Compared with the Single Vector Decomposition (SVD) based algorithm,it has a substantial computational saving with the approximation performance.The simulation results demonstrate its effectiveness and robustness.

Feature analysis of acoustic emission sources for rail crack detection by the finite element method

The safety of rail is very important for the development of high speed railway, and it is necessary to investigate the features of inner cracks in rail. In order to obtain the features of Acoustic Emission （AE） sources of inner cracks in rail, AE sources with different types, depths and propagation distances are examined for crack in rail. The finite element method is utilized to model the rail with cracks and the results of experiment demonstrate the effectiveness of this model. Wavelet transform and Rayleigh-Lamb equations are utilized to extract the features of crack AE sources. The results illustrate that the intensity ratio among AE modes can identify the AE source types and the AE sources with different frequencies in rail. There are uniform AE mode features existing in the AE signals from AE sources in rail web, however AE signals from AE sources in rail head and rail base have the complex and unstable AE modes. Different AE source types have the different propagation features in rail. It is helpful to understand the rail cracks and detect the rail cracks based on the AE technique.

Application of underdetermined blind source separation in ultra-wideband communication signals

Aiming to the estimation of source numbers, mixing matrix and separation of mixing signals under underdetermined case, the article puts forward a method of underdetermined blind source separation （UBSS） with an application in ultra-wideband （UWB） communication signals. The method is based on the sparse characteristic of UWB communication signals in the time domain. Firstly, finding the single source area by calculating the ratio of observed sampling points. Then an algorithm called hough-windowed method was introduced to estimate the number of sources and mixing matrix. Finally the separation of mixing signals using a method based on amended subspace projection. The simulation results indicate that the proposed method can separate UWB communication signals successfully, estimate the mixing matrix with higher accuracy and separate the mixing signals with higher gain compared with other conventional algorithms. At the same time, the method reflects the higher stability and the better noise immunity.

Study on Information Diffusion Analysis in Social Networks and Its Applications

Due to the prevalence of social network services, more and more attentions are paid to explore how information diffuses and users affect each other in these networks, which has a wide range of applications, such as viral marketing, reposting prediction and social recommendation. Therefore, in this paper, we review the recent advances on information diffusion analysis in social networks and its applications. Specifically, we first shed light on several popular models to describe the information diffusion process in social networks, which enables three practical applications, i.e., influence evaluation, influence maximization and information source detection. Then, we discuss how to evaluate the authority and influence based on network structures. After that, current solutions to influence maximiza- tion and information source detection are discussed in detail, respectively. Finally, some possible research directions of information diffu- sion analysis are listed for further study.

Air quality short-term control in an industrial region under adverse weather conditions

A new short-term optimal control of air quality in an industrial region during atmospheric inversions is proposed.lts goal is to prevent violation of health standard of air quality in a few monitored zones.The control establishes restrictions on the emission rates of industrial sources and includes the identification of the industrial sources violating(exceeding)the emission rates set by the control.Both control and identification are based on using solutions to an adjoint dispersion model.Conditions that show the convergence of the emission rates,prescribed by the control,to the original emission rates of the industrial sources are given(Theorems 4 and 5).These results ensure that the new emission rates of industrial sources(established by the control)will be as close as possible to the original emission rates throughout the entire period of application of the control.This creates the minimum possible restrictions on the functioning of industrial enterprises.The highlight of the new control is the possibility of selecting special weights for each pollution source in the goal function that is minimized.These weights are mainly aimed at reducing the intensity of emissions of the main sources of pollution.An example demonstrates the ability of the new method.A similar approach can also be used to develop methods for cleaning water zones polluted by oil(the problem of bioremediation),and to prevent excessive pollution of urban areas with automobile emissions.