Quality assurance for Internet of Things and speech recognition systems

In this issue, we are pleased to present two papers: one for risk assessment for an industrial Internet of Things and the other for testing speech recognition systems. The first paper, ‘ HiRAM: A Hierarchical Risk Assessment Model and Its Implementation for an Industrial Internet of Things in the Cloud ’ by Wen-Lin Sun, Ying-Han Tang and Yu-Lun Huang, proposes Hierarchical Risk Assessment Model (HiRAM) for an IIoT cloud platform to enable self-evaluate its security status by leveraging analytic hierarchy processes (AHPs). The authors also realise HiRAM-RAS, a modular and responsive Risk Assessment System based on HiRAM, and evaluate it in a real-world IIoT cloud platform. The evaluation results show the changes in integrity and availability scores evaluated by HiRAM. (Recommended by Xiaoyin Wang). The second paper, ‘ Adversarial Example-based Test Case Generation for Black-box Speech Recognition Systems ’ by Hanbo Cai, Pengcheng Zhang, Hai Dong, Lars Grunske, Shunhui Ji and Tianhao Yuan, proposes methods for generating targeted adversarial examples for speech recognition systems, based on the firefly algorithm. These methods generate the targeted adversarial samples by continuously adding interference noise to the original speech samples. The evaluation results show that the proposed methods achieve satisfactory results on three speech datasets (Google Command, Common Voice and LibriSpeech), and compared with existing methods, these methods can effectively improve the success rate of the targeted adversarial example generation. (Recommended by Yves Le Traon). We hope that these papers will inspire further research in these directions of quality assurance.