Subjects: Management Science >> Development and Management of Human Resources submitted time 2023-11-08
Abstract： With the spread of higher education and the global economic downturn, the overqualification phenomenon is increasingly becoming common and popular. Prior research has mainly focused on the negative effects of perceived overqualification. However, some scholars are currently urging a deeper exploration of the positive implications of perceived overqualification. Although most studies have focused on employee perceived overqualification and its impact on work attitudes, behaviours and personal well-being, information is limited on the phenomenon of leader perceived overqualification and its effects. For organisations, understanding the effects of leader perceived overqualification on teams is crucial for effective talent management. Therefore, our study draws on self-regulation theory and the process-based theory of team creative synthesis to propose and test a mediated moderation model that explores when and why leader perceived overqualification influences team creativity.
To test the proposed hypotheses, we conducted a multi-wave and multi-source field study. We collected data from five hospitals in North China, and the final sample consists of 106 head nurses and their 847 nurses. At time 1, head nurses were asked to report their demographics and perceived overqualification. At time 2 (two months later), head nurses were asked to report their perceptions of team capability and psychological entitlement. Additionally, nurses were asked to evaluate leader encouragement of creativity and abusive supervision. At time 3 (two months later), nurses rated their team creative process engagement. Lastly, head nurses were asked to assess team creativity.
Results provided support for our theoretical model and revealed the following findings. (1) The interaction between leader perceived overqualification and leader perceived capability significantly predicted leader encouragement of creativity, such that the positive relationship between leader perceived overqualification and leader encouragement of creativity was stronger when team capability was higher rather than lower. (2) Team creative process engagement mediated the relationship between leader encouragement of creativity and team creativity. (3) Leader encouragement of creativity and team creative process engagement mediated the interactive effect of leader perceived overqualification and team capability on team creativity, such that the indirect effect was stronger when team capability is higher.
The preceding results provide several important theoretical contributions. Firstly, this research enriches the outcomes of perceived overqualification by investigating the positive impact of leader perceived overqualification on team creativity. Secondly, this research identifies leader perceived team capability as an important boundary condition for the positive effects of leader perceived overqualification. Thirdly, by exploring the chain mediating roles of leader encouragement of creativity and team creative process engagement, this study opens the ‘black box’ of the effect of leader perceived overqualification on team creativity and expands the understanding of the positive implications of perceived overqualification. Lastly, by examining the relationship between leader perceived overqualification and team creativity, this study enriches the antecedents of team creativity from the leader characteristic perspective.
Subjects: Management Science >> Management Engineering submitted time 2023-08-10
Abstract： Abstract:[Objective] To comprehensively construct a new pattern for the root-out of migrant workers' wage arrears and multiply the capacity and effectiveness of smart governance for wage arrears. [Methods] The current situation of wage arrears governance and the "regulatory dilemma" in smart governance for wage arrears are analyzed. Under the framework of social co-governance, domain knowledge engineering model is used as the top-level design method. [Conclusions] An overall layout for the top-level design of smart governance for wage arrears for migrant workers is proposed, including two systems, one goal, two capabilities, three foundations, and one platform.
Peer Review Status:
Awaiting Review
Subjects: Engineering and technical science >> Optical Engineering submitted time 2021-10-21
Abstract： This paper proposes a simple self-calibration method for the internal time synchronization of MEMS(Micro-electromechanical systems) LiDAR during research and development. Firstly, we introduced the problem of internal time misalignment in MEMS lidar. Then, a robust Minimum Vertical Gradient(MVG) prior is proposed to calibrate the time difference between the laser and MEMS mirror, which can be calculated automatically without any artificial participation or specially designed cooperation target. Finally, actual experiments on MEMS LiDARs are implemented to demonstrate the effectiveness of the proposed method. It should be noted that the calibration can be implemented in a simple laboratory environment without any ranging equipment and artificial participation, which greatly accelerate the progress of research and development in practical applications."
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Computer Software submitted time 2021-03-01
Abstract： " This paper proposes a self-supervised low light image enhancement method based on deep learning, which can improve the image contrast and reduce noise at the same time to avoid the blur caused by pre-/post-denoising. The method contains two deep sub-networks, an Image Contrast Enhancement Network (ICE-Net) and a Re-Enhancement and Denoising Network (RED-Net). The ICE-Net takes the low light image as input and produces a contrast enhanced image. The RED-Net takes the result of ICE-Net and the low light image as input, and can re-enhance the low light image and denoise at the same time. Both of the networks can be trained with low light images only, which is achieved by a Maximum Entropy based Retinex (ME-Retinex) model and an assumption that noises are independently distributed. In the ME-Retinex model, a new constraint on the reflectance image is introduced that the maximum channel of the reflectance image conforms to the maximum channel of the low light image and its entropy should be the largest, which converts the decomposition of reflectance and illumination in Retinex model to a non-ill-conditioned problem and allows the ICE-Net to be trained with a self-supervised way. The loss functions of RED-Net are carefully formulated to separate the noises and details during training, and they are based on the idea that, if noises are independently distributed, after the processing of smoothing filters (\eg mean filter), the gradient of the noise part should be smaller than the gradient of the detail part. It can be proved qualitatively and quantitatively through experiments that the proposed method is efficient.
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Computer Application Technology submitted time 2020-08-26
Abstract： Low light images suffer from severe noise, low brightness, low contrast, etc. In previous researches, many image enhancement methods have been proposed, but few methods can deal with these problems simultaneously. In this paper, to solve these problems simultaneously, we propose a low light image enhancement method that can combined with supervised learning and previous HSV (Hue, Saturation, Value) or Retinex model based image enhancement methods. First, we analyse the relationship between the HSV color space and the Retinex theory, and show that the V channel (V channel in HSV color space, equals the maximum channel in RGB color space) of the enhanced image can well represent the contrast and brightness enhancement process. Then, a data-driven conditional re-enhancement network (denoted as CRENet) is proposed. The network takes low light images as input and the enhanced V channel as condition, then it can re-enhance the contrast and brightness of the low light image and at the same time reduce noise and color distortion. It should be noted that during the training process, any paired images with different exposure time can be used for training, and there is no need to carefully select the supervised images which will save a lot. In addition, it takes less than 20 ms to process a color image with the resolution 400*600 on a 2080Ti GPU. Finally, some comparative experiments are implemented to prove the effectiveness of the method. The results show that the method proposed in this paper can significantly improve the quality of the enhanced image, and by combining with other image contrast enhancement methods, the final enhancement result can even be better than the reference image in contrast and brightness. (Code will be available at https://github.com/hitzhangyu/image-enhancement-with-denoise) "
Peer Review Status:Awaiting Review
Subjects: Psychology >> Physiological Psychology Subjects: Linguistics and Applied Linguistics >> Linguistics and Applied Linguistics submitted time 2020-06-19
Abstract： This paper reviews the primary findings and breakthroughs of the study on predictive sentence processing by using event-related potentials (ERPs), published in international journals. Our review begins with introducing the rationale of sentence prediction in psycholinguistics. Then, the paper surveys the milestones that revealed the associations between two major predictive processes and their ERP correlates: N400 and frontal positivity effects. A model of predictive sentence processing is concluded based on the previous study. Finally, the paper proposes the limitations of the existing studies and possible directions for further research in the future. "
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Computer Application Technology submitted time 2020-04-14
Abstract： Low-light images suffer from severe noise and low illumination. Current deep learning models that are trained with real-world images have excellent noise reduction, but a ratio parameter must be chosen manually to complete the enhancement pipeline. In this work, we propose an adaptive low-light raw image enhancement network to avoid parameter-handcrafting and to improve image quality. The proposed method can be divided into two sub-models: Brightness Prediction (BP) and Exposure Shifting (ES). The former is designed to control the brightness of the resulting image by estimating a guideline exposure time t 1 . The latter learns to approximate an exposure-shifting operator ES, converting a low-light image with real exposure time t 0 to a noise-free image with guideline exposure time t 1 . Additionally, structural similarity (SSIM) loss and Image Enhancement Vector (IEV) are introduced to promote image quality, and a new Campus Image Dataset (CID) is proposed to overcome the limitations of the existing datasets and to supervise the training of the proposed model. In quantitative tests, it is shown that the proposed method has the lowest Noise Level Estimation (NLE) score compared with BM3D-based low-light algorithms, suggesting a superior denoising performance. Furthermore, those tests illustrate that the proposed method is able to adaptively control the global image brightness according to the content of the image scene. Lastly, the potential application in video processing is briefly discussed. " "
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Other Disciplines of Computer Science submitted time 2020-03-06
Abstract： This paper proposes a self-supervised low light image enhancement method based on deep learning. Inspired by information entropy theory and Retinex model, we proposed a maximum entropy based Retinex model. With this model, a very simple network can separate the illumination and reflectance, and the network can be trained with low light images only. We introduce a constraint that the maximum channel of the reflectance conforms to the maximum channel of the low light image and its entropy should be largest in our model to achieve self-supervised learning. Our model is very simple and does not rely on any well-designed data set (even one low light image can complete the training). The network only needs minute-level training to achieve image enhancement. It can be proved through experiments that the proposed method has reached the state-of-the-art in terms of processing speed and effect. "
Peer Review Status:Awaiting Review
Hits
YES
Hits
Downloads
Comment