When seismic waves propagate through horizontal transversely isotropic (HTI) media, the variation of acoustic impedance with azimuth can effectively reflect the fracture density and direction information. The key to high-precision fracture parameter prediction lies in improving the accuracy of azimuthal impedance inversion and elliptical analysis. This study proposes an integrated solution: First, the total-variation multiplicative regularization multichannel impedance inversion technique is employed to obtain azimuthal impedance information. By incorporating formation dip constraints and an improved Polak-Ribière-Polyak-Hestenes-Stiefel (PRP-HS) hybrid conjugate gradient algorithm, the inversion accuracy and lateral resolution are significantly enhanced. Second, to address the small-sample problem in azimuthal impedance elliptical analysis, we developed a hybrid elliptical analysis method incorporating probability distribution and uncertainty estimation. The methodology involves: (1) applying the adaptive random sample consensus (RANSAC) algorithm to screen high-quality data points, (2) utilizing Bayesian regression for probabilistic modeling of the data, and (3) implementing Bayesian inference through Markov chain Monte Carlo (MCMC) methods. This combined strategy of random sampling and iterative optimization ensures the stability and reliability of fitting results. Furthermore, a planar transformation matrix is introduced to correct the rotation angle, thereby optimizing the prediction accuracy of fracture direction. Application results from actual well logging and seismic data demonstrate that this method exhibits superior performance in predicting both fracture direction and density, providing a reliable technical approach for fractured reservoir characterization.

Code authorship attribution has been an interesting research problem for decades. Recent studies have revealed that existing methods for code authorship attribution suffer from weak robustness. Under the influence of small perturbations added by the attacker, the accuracy of the method will be greatly reduced. As of now, there is no code authorship attribution method capable of effectively handling such attacks. In this paper, we attribute the weak robustness of code authorship attribution methods to dataset bias and argue that this bias can be mitigated through adjustments to the feature learning strategy. We first propose a robust code authorship attribution feature combination framework, which is composed of only simple shallow neural network structures, and introduces controllability for the framework in the feature extraction by incorporating expert knowledge. Experiments show that the framework has significantly improved robustness over mainstream code authorship attribution methods, with an average drop of 23.4% (from 37.8% to 14.3%) in the success rate of targeted attacks and 25.9% (from 46.7% to 20.8%) in the success rate of untargeted attacks. At the same time, it can also achieve results comparable to mainstream code authorship attribution methods in terms of accuracy.

Software products have brought convenience to modern society but also pose significant security risks due to various types of vulnerabilities. Identifying vulnerability names is vital for program repair and software maintenance, but the lack of training data presents a challenge. Big data analytics and machine learning can help overcome this challenge by processing large amounts of data and improving the accuracy of vulnerability name prediction. Considering that the data is often from datasets composed of multiple sources, a feature-based or attention-based multi-source domain adaptation (MSDA) approach is required. In this paper, we propose an MSDA method based on both feature and attention to accomplish the task of predicting vulnerability names, called Multi-Source Domain Adaptation for Vulnerability Name Prediction (MSDA-VNP). First, MSDA-VNP reduces domain divergence by adversarial training and then uses domain-invariant features to obtain feature correlations between individual source and target domains. In combination with the obtained domain correlations, Weighted multi-kernel Maximum Mean Discrepancy (WMK-MMD) is proposed as the attention mechanism. Second, a data augmentation strategy is employed to enhance MSDA-VNP to identify privacy-related vulnerabilities. To evaluate our approach, we conducted experiments on eight Java real-world projects in the Software Assurance Reference Dataset (SARD). The experimental results show that the proposed method MSDA-VNP performed efficiently and stably for the 44 types of vulnerabilities involved. The data augmentation strategy has also been proved to be effective as an enhancement for the proposed method MSDA-VNP.

In this article, the exponential stabilization (ES) of delayed switched linear systems with asynchronous switching is settled via discrete dynamic event-triggered (DDET) control. First, an observer-based DDET scheme with a prescribed constant upper bound is devised. The DDET scheme can avoid the Zeno behavior directly and reduce the communication burden immensely. And the prescribed constant upper bound for the trigger interval can prevent the system performance from deteriorating. Under the framework of the DDET scheme, the situation of no system switching or multiple system switching over a trigger interval is considered, and a unified closed-looped system (CLS) which incorporates the above two situations is established. Then, by constructing a series of multiple Lyapunov functionals, the exponential stability of the unified CLS is studied based on the average dwell-time (ADT) method. Moreover, a design procedure for the feedback gain is provided. Finally, a simulation example is given to illustrate the effectiveness and superiority of the DDET scheme and the obtained main results.