Ring-based topology is popular for optical network-on-chip. However, the network congestion is serious for ring topology, especially when optical circuit-switching is employed. In this paper, we proposed an algorithm to build a low congestion multi-ring architecture for optical network-on-chip without additional wavelength or scheduling overhead. A network congestion model is established with new network congestion factor defined. An algorithm is developed to optimize the low congestion multi-ring topology. Finally, a case study is shown and the simulation results by OPNET verify the superiority over the traditional ONoC architecture.

Nowadays there is no way to automatically obtain the function points when using function point analyze (FPA) method, especially for the requirement documents written in Chinese language. Considering the characteristics of Chinese grammar in words segmentation, it is necessary to divide words accurately Chinese words, so that the subsequent entity recognition and disambiguation can be carried out in a smaller range, which lays a solid foundation for the efficient automatic extraction of the function points. Therefore, this paper proposed a method of K-Means clustering based on TF-IDF, and conducts experiments with 24 software requirement documents written in Chinese language. The results show that the best clustering effect is achieved when the extracted information is retained by 55% to 75% and the number of clusters takes the middle value of the total number of clusters. Not only for Chinese, this method and conclusion of this paper, but provides an important reference for automatic extraction of function points from software requirements documents written in other Oriental languages, and also fills the gaps of data preprocessing in the early stage of automatic calculation function points.

During the recent several years, deep learning has achieved excellent results in image recognition, voice processing, and other research areas, which has set off a new upsurge of research and application. Internal defects and external malicious attacks may threaten the safe and reliable operation of a deep learning system and even cause unbearable consequences. The technology of testing deep learning systems is still in its infancy. Traditional software testing technology is not applicable to test deep learning systems. In addition, the characteristics of deep learning such as complex application scenarios, the high dimensionality of input data, and poor interpretability of operation logic bring new challenges to the testing work. This paper focuses on the problem of test case generation and points out that adversarial examples can be used as test cases. Then the paper proposes MTGAN which is a framework to generate test cases for deep learning image classifiers based on Generative Adversarial Network. Finally, this paper evaluates the effectiveness of MTGAN.