In this ubiquitous age of mobile phones, Android malware is a growing threat to mobile users. The most common malware detection technique is analyzing different features that any Android app encompasses. However, malware developers have become increasingly sophisticated; hence, these Android app features can’t be revealed easily. One way to address this problem is to apply reverse engineering on the Android app executable file, which will eventually decompose the app into low-level source codes. After that, these source codes can be analyzed for subsequent identification of features, which will ultimately help to segregate malware from the benignware. In this article, a step-by-step procedure has been demonstrated to extract four important features (declared permission, intent, component, and hardware/software) from the Android app using reverse engineering. This will open a path for new researchers in this domain.
RNA is a molecule composed of ribonucleotides and plays a crucial role in biological activities. The computational prediction of RNA secondary structures has been a long-standing issue in computational biology. Traditional methods for this problem are based on free energy minimization, but the performance of these methods has reached an upper limit. In recent years, various deep learning-based methods have been proposed, but these models are still primitive and prone to overfitting, resulting in poor performance across RNA families. In this paper, we propose two methods, AttnUFold and TransUFold, which utilize the attention mechanism to enhance the model’s learning ability for the global features of RNA sequences. Additionally, we modify the loss function to cope with sample distribution imbalances and attempt to introduce relevant constraints for RNA folding. Compared with the baseline, the two models have brought improvements in both within- and cross-family tasks. AttnUFold achieved a high F1 score of 0.852 on the ArchiveII dataset, surpassing all traditional and most deep learning methods.
Benefiting from precise perception, real-time prediction and reliable planning, autonomous driving systems have exhibited exceptional performance in research. However, the high complexity and opacity prevent its application in practice. To introduce a user-friendly autonomous driving system, we propose a driving captioner to generate real time description and explanation of self-driving systems in natural language. Specifically, we unify the end-to-end autonomous driving and video captioning tasks into a single yet effective framework by introducing an additional captioning head to describe the action of the vehicle and explain the reasons. Besides, we exploit an effective accelerating method to accelerate the inference process, which decreases the average inference time from 0.670 s to 0.298 s. Through extensive experiments on both simulation datasets and real-world datasets, we show the superior generalization ability and robustness of the proposed framework.
So far, the number of individuals with Tic disorder worldwide has reached 59 million, and the prevalence of the disorder is rapidly increasing globally. In this work, we focus on weakly supervised learning methods for recognizing childhood tic disorders. In situations with limited data availability, we design a relative probability metric based on the characteristics of the data and a multi-phase learning algorithm is proposed based on relative probability in order to efficiently utilize coarse-labeled data in a “from easy to difficult” manner. Furthermore, the effectiveness of our method is validated through ablation experiments. Through extensive experiments on the test dataset, we demonstrate that our method behaves extraordinarily compared to baseline approaches, improving AUC by 3.0%, and facilitating expedited diagnostic assessment for medical practitioners.
Neural network pruning is a widely used approach for reducing the inference cost of deep models in order to deploy on resource-limited settings. However, current pruning works lack attention to information migration from pruned to the remaining part of the Deep Neural Network, and on balancing model performance and compression rate. On these two issues, in this paper, we propose a novel E xplicit I nformation M igration network P runing (EIMP) algorithm. Specifically (1) the constrained gradient update method transfers valid information from redundant networks to the preserved, and (2) the newly designed λ -decay regularization method learns the trade-off between the performance and penalty item. Experiments show that our EIMP algorithm achieves state-of-the-art performance on several datasets with various benchmark network architectures. Notably, EIMP achieves +1.54% better than SOTA on ImageNet.
