The relationship between family functioning and social media addiction among university students: a moderated mediation model of depressive symptoms and peer support.

BACKGROUND: Social media addiction (SMA) is an increasing problem, especially among young adults. Little is known about university students' SMA and family functioning. This study aimed to explore the mediating effect of depressive symptoms and the moderating effect of peer support in the relationship between family functioning and SMA among young adults. METHODS: A sample of 1862 Chinese university students completed an online survey including the Bergen Social Media Addiction Scale (BSMAS), Family APGAR, the Patient Health Questionnaire (PHQ-9), peer support, and demographic characteristics. Hierarchical regression and moderated mediation analysis were used to test the effects and pathways among them. RESULTS: Of the 1840 participants, 30.11% experienced SMA, 38.80% had family dysfunction and 15.98% had depressive symptoms. Hierarchical multiple regression showed better family functioning significantly predicted less SMA (ß = -0.26, p < 0.001) and lower depressive symptoms (ß = -0.58, p < 0.001), after adjusting for covariates. Mediation analysis verified that depressive symptoms mediated the effect of family functioning on SMA (indirect effect = -0.22, 95%CI[-0.28, -0.17]). Furthermore, the interaction of family functioning and peer support was negatively related to depressive symptoms (ß= -0.03, 95% CI[-0.05, -0.01]) and the interaction of depressive symptoms and peer support was positively related to SMA (ß = 0.01, 95%CI[0.004, 0.02]). Additional analysis further confirmed that peer support decreased depressive symptoms among young adults from dysfunctional families, and increased SMA behaviors in individuals with depressive symptoms. CONCLUSION: Better family functioning and lower depressive symptoms may contribute to less social media addiction among Chinese university students. Peer support could moderate the mediating role of depressive symptoms on social media addiction in individuals with family dysfunction.

Viewpoint-Controllable Telepresence: A Robotic-Arm-Based Mixed-Reality Telecollaboration System.

In mixed-reality (MR) telecollaboration, the local environment is remotely presented to a remote user wearing a virtual reality (VR) head-mounted display (HMD) via a video capture device. However, remote users frequently face challenges in naturally and actively manipulating their viewpoints. In this paper, we propose a telepresence system with viewpoint control, which involves a robotic arm equipped with a stereo camera in the local environment. This system enables remote users to actively and flexibly observe the local environment by moving their heads to manipulate the robotic arm. Additionally, to solve the problem of the limited field of view of the stereo camera and limited movement range of the robotic arm, we propose a 3D reconstruction method combined with a stereo video field-of-view enhancement technique to guide remote users to move within the movement range of the robotic arm and provide them with a larger range of local environment perception. Finally, a mixed-reality telecollaboration prototype was built, and two user studies were conducted to evaluate the overall system. User study A evaluated the interaction efficiency, system usability, workload, copresence, and user satisfaction of our system from the remote user's perspective, and the results showed that our system can effectively improve the interaction efficiency while achieving a better user experience than two traditional view-sharing techniques based on 360 video and based on the local user's first-person view. User study B evaluated our MR telecollaboration system prototype from both the remote-user side and the local-user side as a whole, providing directions and suggestions for the subsequent design and improvement of our mixed-reality telecollaboration system.

Research on face specular removal and intrinsic decomposition based on polarization characteristics.

It is well known that the specular component in the face image destroys the true informantion of the original image and is detrimental to the feature extraction and subsequent processing. However, in many face image processing tasks based on Deep Learning methods, the lack of effective datasets and methods has led researchers to routinely neglect the specular removal process. To solve this problem, we formed the first high-resolution Asian Face Specular-Diffuse-Image-Material (FaceSDIM) dataset based on polarization characterisitics, which consists of real human face specular images, diffuse images, and various corresponding material maps. Secondly, we proposed a joint specular removal and intrinsic decomposition multi-task GAN to generate a de-specular image, normal map, albedo map, residue map and visibility map from a single face image, and also further verified that the prediected de-specular images have a positive enhancement effect on face intrinsic decomposition. Compared with the SOTA algorithm, our method achieves optimal performance both in corrected linear images and in uncorrected wild images of faces.

Method for reconstructing a high dynamic range image based on a single-shot filtered low dynamic range image.

Traditional cameras are limited by sensors and cannot directly capture single-shot high dynamic range (HDR) images. We propose an improved single-shot HDR image reconstruction method that uses a single-exposure filtered low dynamic range (FLDR) image. First, by adding an optical filter in front of the camera lens, a FLDR image with different RGB channel exposure states and luminance ranges can be captured in a single-shot, unlike the traditional LDR image. Second, a deep inverse tone mapping network (DITMnet) with multibranch features extraction and multioutput images synthesis is designed to reconstruct an HDR image from a single FLDR image. Experimentally, under different exposure states and color spaces, our method outperforms similar algorithms.