Cross-project defect prediction (CPDP) is a hot research topic in recent years. The inconsistent data distribution between source and target projects and lack of labels for most of target instances bring a challenge for defect prediction. Researchers have developed several CPDP methods. However, the prediction performance still needs to be improved. In this paper, we propose a novel approach called Joint Domain Adaption and Pseudo-Labeling (JDAPL). The network architecture consists of a feature mapping sub-network to map source and target instances into a common subspace, followed by a classification sub-network and an auxiliary classification sub-network. The classification sub-network makes use of the label information of labeled instances to generate pseudo-labels. The auxiliary classification sub-network learns to reduce the distribution difference and improve the accuracy of pseudo-labels for unlabeled instances through loss maximization. Network training is guided by the adversarial scheme. Extensive experiments are conducted on 10 projects of the AEEEM and NASA datasets, and the results indicate that our approach achieves better performance compared with the baselines.

This letter considers the problem of modeling a nonstationary Gaussian ARMA process. The corresponding approximation problem is formulated on the basis of the theory of canonical representation of the Gaussian process. Further, it will be shown that the solution can be obtained by solving a set of linear equations, as an extension of the Padé approximation established in the stationary case.

The phenomena of iced line galloping in overhead transmission lines, caused by wind or asymmetric icing, can directly result in structural damage, windage yaw discharge of conductor, and metal damage, posing significant risks to the operation of power systems. However, the existing prediction methods for iced line galloping are difficult to achieve accurate predictions due to the lack of a large amount of iced line galloping data that matches real-world conditions. To address these issues, this paper studies the overhead iced transmission line galloping response prediction. First, the models of finite element, aerodynamic coefficient, and aerodynamic excitation for the iced conductor are constructed. The dynamic response of the conductor is simulated using finite element software to obtain a dataset of conductor galloping under different parameters. Secondly, a particle swarm optimization-conditional generative adversarial network (PSO-CGAN) based iced transmission line galloping prediction model is proposed, where the weight parameters of loss function in CGAN are optimized by PSO. The model takes initial wind attack angle, wind speed, and span as inputs to output prediction results of iced transmission line galloping. Then, based on the dynamics and galloping features of the conductor, the effects of different initial wind attack angles, wind speeds, and icing thickness on galloping are analyzed. Finally, the superior performance of the proposed model is verified through simulations.

We propose a new method of differential fault attack, which is based on the nibble-group differential diffusion property of the lightweight block cipher MIBS. On the basis of the statistical regularity of differential distribution of the S-box, we establish a statistical model and then analyze the relationship between the number of faults injections, the probability of attack success, and key recovering bits. Theoretically, time complexity of recovering the main key reduces to 22 when injecting 3 groups of faults (12 nibbles in total) in 30,31 and 32 rounds, which is the optimal condition. Furthermore, we calculate the expectation of the number of fault injection groups needed to recover 62 bits in main key, which is 3.87. Finally, experimental data verifies the correctness of the theoretical model.