Shun-Chang Zhong’s scientific contributions

... The effects of conscientiousness and agreeableness are two-fold. On one hand, they contribute positively to the performance of tasks in stable environments [37]. On the other hand, these traits may impede individuals and organizations from engaging in creative activities and negatively affect their responses to dynamic and evolving environments [38,39]. ...

Reference:

Individual quality, insecure organizational attachment, and formalistic task completion: Social cognitive perspective

... asserted that existing research on TMS overly emphasizes expertise-related factors while neglecting other individual attributes that also play a critical role in affecting TMS development and team collaboration. Among these attributes, individuals' personality traits stand out as crucial factors that significantly influence team performance and collaboration outcomes (Stadler et al., 2019;Zhong et al., 2019). Despite extensive research on personality traits across various contexts (see Huang et al., 2014), little attention has been given to investigating their impact on team performance through the operation of TMS development (Pearsall and Ellis, 2006). ...

Reference:

Who benefits from virtual collaboration? The interplay of team member expertness and Big Five personality traits

... However, these models are complex and difficult to train, posing significant challenges in the field of speech emotion recognition. With the rapid advancement of deep learning, convolutional neural networks (CNN) [7,8], recurrent neural networks (RNN) [9,10], Long Short-Term Memory networks (LSTM) [11,12], and Bidirectional LSTM networks (Bi-LSTM) [13,14] have been proposed successively, garnering more attention in the study of speech emotion recognition. Wang et al. [15] proposed the Dual Sequence LSTM (DS-LSTM) model, which utilizes two Mel-spectrograms with different time-frequency resolutions to predict emotions, achieving good results. ...

Reference:

TFC-SpeechFormer: Efficient Emotion Recognition Based on Deep Speech Analysis and Hierarchical Progressive Structures