Research on user participation behavior of mobile short video APPs: Taking Xiaohongshu as an example

Short video apps for mobile devices are rising in popularity. Using Xiaohongshu as an example, this work carefully studies the user participation behavior of mobile short video Apps and contributes to the body of knowledge in the field of pertinent theoretical research. This study equips creators of short videos with the knowledge they require to improve user experience and content marketing on a more objective basis, as well as to enable app upgrading and optimization. The UTAUT theoretical model is used in this paper to develop hypotheses, which are then tested using survey data. Finally, the theoretical model and hypothesis are validated using multiple regression analysis and hierarchical regression analysis. The significant study results are as follows: Users\u27 behavior is significantly influenced by social value, perceived entertainment value, individual innovation, facilitating conditions, and privacy security when using communities; by social value, individual innovation, facilitating conditions, and privacy security when participating in communities; and by social value, facilitating conditions, and privacy security when contributing to communities. Finally, it makes some suggestions for the long-term expansion of mobile short video apps based on the testing results

Generative Face Completion

In this paper, we propose an effective face completion algorithm using a deep generative model. Different from well-studied background completion, the face completion task is more challenging as it often requires to generate semantically new pixels for the missing key components (e.g., eyes and mouths) that contain large appearance variations. Unlike existing nonparametric algorithms that search for patches to synthesize, our algorithm directly generates contents for missing regions based on a neural network. The model is trained with a combination of a reconstruction loss, two adversarial losses and a semantic parsing loss, which ensures pixel faithfulness and local-global contents consistency. With extensive experimental results, we demonstrate qualitatively and quantitatively that our model is able to deal with a large area of missing pixels in arbitrary shapes and generate realistic face completion results.Comment: Accepted by CVPR 201

Are Smell-Based Metrics Actually Useful in Effort-Aware Structural Change-Proneness Prediction? An Empirical Study

Bad code smells (also named as code smells) are symptoms of poor design choices in implementation. Existing studies empirically confirmed that the presence of code smells increases the likelihood of subsequent changes (i.e., change-proness). However, to the best of our knowledge, no prior studies have leveraged smell-based metrics to predict particular change type (i.e., structural changes). Moreover, when evaluating the effectiveness of smell-based metrics in structural change-proneness prediction, none of existing studies take into account of the effort inspecting those change-prone source code. In this paper, we consider five smell-based metrics for effort-aware structural change-proneness prediction and compare these metrics with a baseline of well-known CK metrics in predicting particular categories of change types. Specifically, we first employ univariate logistic regression to analyze the correlation between each smellbased metric and structural change-proneness. Then, we build multivariate prediction models to examine the effectiveness of smell-based metrics in effort-aware structural change-proneness prediction when used alone and used together with the baseline metrics, respectively. Our experiments are conducted on six Java open-source projects with up to 60 versions and results indicate that: (1) all smell-based metrics are significantly related to structural change-proneness, except metric ANS in hive and SCM in camel after removing confounding effect of file size; (2) in most cases, smell-based metrics outperform the baseline metrics in predicting structural change-proneness; and (3) when used together with the baseline metrics, the smell-based metrics are more effective to predict change-prone files with being aware of inspection effort