Yuan Tian

ShuQi Ye, Yuan Tian

Acoustic scene classification (ASC) is highly important in the real world. Recently, deep learning-based methods have been widely employed for acoustic scene classification. However, these methods are currently not lightweight enough as well as their performance is not satisfactory. To solve these problems, we propose a deep space separable distillation network. Firstly, the network performs high-low frequency decomposition on the log-mel spectrogram, significantly reducing computational complexity while maintaining model performance. Secondly, we specially design three lightweight operators for ASC, including Separable Convolution (SC), Orthonormal Separable Convolution (OSC), and Separable Partial Convolution (SPC). These operators exhibit highly efficient feature extraction capabilities in acoustic scene classification tasks. The experimental results demonstrate that the proposed method achieves a performance gain of 9.8% compared to the currently popular deep learning methods, while also having smaller parameter count and computational complexity.

Thong Hoang, Julia Lawall, Yuan Tian et al.

Linux kernel stable versions serve the needs of users who value stability of the kernel over new features. The quality of such stable versions depends on the initiative of kernel developers and maintainers to propagate bug fixing patches to the stable versions. Thus, it is desirable to consider to what extent this process can be automated. A previous approach relies on words from commit messages and a small set of manually constructed code features. This approach, however, shows only moderate accuracy. In this paper, we investigate whether deep learning can provide a more accurate solution. We propose PatchNet, a hierarchical deep learning-based approach capable of automatically extracting features from commit messages and commit code and using them to identify stable patches. PatchNet contains a deep hierarchical structure that mirrors the hierarchical and sequential structure of commit code, making it distinctive from the existing deep learning models on source code. Experiments on 82,403 recent Linux patches confirm the superiority of PatchNet against various state-of-the-art baselines, including the one recently-adopted by Linux kernel maintainers.

Anees Ara, Mznah Al-Rodhaan, Yuan Tian et al.

Cyber physical systems (CPS) are mission critical systems engineered by combination of cyber and physical systems respectively. These systems are tightly coupled, resource constrained systems and have dynamic real time applications. Due to the limitation of resources, and in order to improve the efficiency of the CPS systems, they are combined with cloud computing architecture, and are called as Cyber Physical Cloud Computing Systems (CPCCS). These CPCCS have critical care applications where security of the systems is a major concern. Therefore, we propose a Secure Service provisioning architecture for Cyber Physical Cloud Computing Systems (CPCCS), which includes the combination of technologies such as CPS, Cloud Computing and Wireless Sensor Networks. In addition to this, we also highlight various threats/attacks; security requirements and mechanisms that are applicable to CPCCS at different layers and propose two security models that can be adapted in a layered architectural format.