Effectiveness of cloud-based rehabilitation in children with developmental language disorder during the COVID-19 pandemic: A prospective cohort study.

During the coronavirus disease 2019 pandemic, we considered the case of a child with developmental language disorder (DLD) who could not go to the hospital on time to receive timely rehabilitation treatment due to disrupted hospital operations. The application of cloud-based rehabilitation platforms has provided significant advantages and convenience for children with DLD in-home remote rehabilitation. Among them, the JingYun Rehab Cloud Platform is the most widely used in mainland China. It is an interactive telerehabilitation system developed by Weixin Huang that delivers personalized home rehabilitation for special education children. In this study, we used the JingYun Rehab Cloud Platform to investigate the extent to which cloud-based rehabilitation is effective for children with DLD in terms of language and cognitive outcomes. This was a prospective cohort study including all children who were evaluated and diagnosed with DLD through Sign-Significant Relations and were followed up at the rehabilitation clinic of our institute. We followed 162 children with DLD for 3 months, including 84 children with DLD who participated in remote cloud-based rehabilitation on the JingYun Rehab Cloud Platform and 78 children with DLD as the control group who underwent home-based rehabilitation. Language abilities of both groups were assessed using the Chinese version of the Peabody Picture Vocabulary Test-Revised. Several measures of training performance (language, memory, and cognition tasks) were assessed before and after cloud-based rehabilitation in the remote cloud-based rehabilitation group. Children with DLD in the cloud-based rehabilitation group performed significantly better in language abilities, as assessed by the Peabody Picture Vocabulary Test-Revised, than children with DLD in the control group. Furthermore, for children who participated in remote cloud-based rehabilitation, the frequency of training sessions was proportional to their performance on language, memory, and cognition tasks. This study demonstrated the effectiveness of cloud-based rehabilitation on the JingYun Rehab Cloud Platform in treating children with DLD.

A Privacy-Preserved Variational-Autoencoder for DGA Identification in the Education Industry and Distance Learning.

One of the most insidious methods of bypassing security mechanisms in a modern information system is the domain generation algorithms (DGAs), which are used to disguise the identity of malware by periodically switching the domain name assigned to a command and control (C&C) server. Combating advanced techniques, such as DGAs, is an ongoing challenge that security organizations often need to work with and possibly share private data to train better and more up-to-date machine learning models. This logic raises serious concerns about data integrity, trade-related issues, and strict privacy protocols that must be adhered to. To address the concerns regarding the privacy and security of private data, we propose in this work a privacy-preserved variational-autoencoder to DGA combined with case studies from the education industry and distance learning, specifically because the recent pandemic has brought an explosive increase to remote learning. This is a system that, using the secured multi-party computation (SMPC) methodology, can successfully apply machine learning techniques, specifically the Siamese variational-autoencoder algorithm, on encrypted data and metadata. The method proposed for the first time in the literature facilitates learning specialized extraction functions of useful intermediate representations in complex deep learning architectures, producing improved training stability, high generalization performance, and remarkable categorization accuracy.