Search results

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.

Does the history of the bureaucratic system, along with the establishment of the Great Wall during the Ming and Qing dynasties (1368–1911), affect firm behavior across the borderlands of the Great Wall?

The Ming and Qing dynasties built a centralized administrative system in the borderlands on the south side of the Great Wall, in contrast to the “feudal lordship” system on the north side. Employing a regression discontinuity analysis framework with the Great Wall as a geographical discontinuity, we examine the long-run effects of the Great Wall on firms’ earnings management.

Using a large sample of nonlisted firms in the central core frontier region, we show that the earnings management of firms in the region south of the Great Wall is significantly curtailed compared with firms in the north of it, and this effect is more pronounced for non-SOEs. Our findings are robust to a battery of tests to account for alternative explanations.

Overall, by emphasizing the role of institutions, like legal system, shaped in history on firms’ earnings management, this study sheds new light on institutional determinants of firms’ behaviors in earnings information disclosure.

First, we enrich our understanding of the institutional determinants of firms’ financial reporting outcomes. Second, our findings shed new light on the long-term effects of historical ruling styles on modern corporate behavior.