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This study aims to reduce data bias during human activity and increase the accuracy of activity recognition.

A convolutional neural network and a bidirectional long short-term memory model are used to automatically capture feature information of time series from raw sensor data and use a self-attention mechanism to learn select potential relationships of essential time points. The proposed model has been evaluated on six publicly available data sets and verified that the performance is significantly improved by combining the self-attentive mechanism with deep convolutional networks and recursive layers.

The proposed method significantly improves accuracy over the state-of-the-art method between different data sets, demonstrating the superiority of the proposed method in intelligent sensor systems.

Using deep learning frameworks, especially activity recognition using self-attention mechanisms, greatly improves recognition accuracy.

Recent attention to the developments focusing on the educational services has been noteworthy, with the educational environment specifically the smart campus emerging both as a domain and as an opportunity. As a domain worthy of exploration, a number of research efforts are being conceptualized around smart campus initiatives. The existing bouquet of research publications on smart campus provides a testimony of the enthusiasm and also exposes the heterogeneous attempts the domain has witnessed to date. The available evidence is still inadequate to provide clarity on the thrust areas of research around smart campus.

Given the understanding, this study intends to decode the domain to get an early impression of the focus of the research concentration around smart campus. Thereby the study resorts to an automated text-mining approach using Python on contents shortlisted systematically, and published between the period 2010 and May 2022, from select databases.

Based on the analysis it was possible to identify eight themes (i.e. smart campus characteristics, smart campus stakeholders, smart campus frameworks, smart campus technologies, smart campus infrastructure, smart campus evaluation, smart learning environment and smart campus applications) characterizing research efforts within the smart campus literature.

The themes around the smart campus showcase the thrust areas receiving attention. These characterize extant research endeavours in the smart campus domain and can offer useful pointers to researchers going forward. This awareness can also be beneficial to institutional leadership and technology providers intending to implement smart campus initiatives, contributing to the development of the educational environment.

This study mainly explores how ESG performance (ESG stands for Environment, Social, and Governance) affects corporate downside risk through innovation input and innovation output, thereby promoting sustainable development of enterprises.

Using Chinese A-share listed companies from 2014 to 2022 as research samples, a stepwise regression method is used to empirically test the impact of ESG performance on corporate innovation and downside risk by constructing multiple multivariate primary regression models.

ESG performance is beneficial for obtaining external resources and alleviating principal-agent problems. It can promote enterprises to increase innovation input and improve innovation output, thereby enhancing their core competitiveness, and suppressing their downside risk. This inhibitory effect is more significant in non-state-owned enterprises, non-high-tech enterprises, and enterprises where the chairman and the general manager are not combined in one. Further additional analysis has found that equity concentration weakens the inhibitory effect of ESG performance on corporate downside risk, equity balance strengthens the inhibitory effect of ESG performance on corporate downside risk, indicating that a mutually restrictive equity structure is conducive to promoting enterprises to actively fulfill ESG responsibility, thereby improving corporate innovation level and resolving their downside risk.

Enterprise managers, policy makers, and other practitioners can clearly see the benefits of implementing ESG measures, further strengthen their confidence in sustainable development, actively apply ESG concepts to the entire production and operation process of enterprises, increase attention and implementation of ESG elements, and promote the healthy and vigorous development of enterprises and macroeconomics.

The research conclusions reveal the inherent mechanism by which ESG performance empowers enterprises to improve their innovation level and reverse their performance decline, effectively expanding the theoretical achievements of ESG performance in enterprise innovation and risk management.