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Accurate modeling of facial hair is crucial not only for reconstructing a clean-shaven face but also for enhancing realism when creating digital human avatars. Previous methods have primarily focused on reconstructing sparse facial hairs from static scans, and more recently, efforts have been made to track facial performances. However, there is still room for improvement in reconstructing thick hairs. In this paper, we address the challenges of facial hair reconstruction and tracking, enhancing the realism and detail of facial hair in digital human avatars. For facial hair reconstruction, we propose a method that combines line-based multi-view stereo with line segment matching to recover a dense hair point cloud. From this point cloud, hair strands are extracted using the forward Euler method. For tracking, we introduce a space-time optimization method that registers the reference facial hair strands to subsequent frames, taking into account both the global shape and the motion between frames. After capturing the facial hair structures, we refine the underlying skin meshes by replacing the noisy hair region points with strand roots. We conducted experiments on various examples captured under different systems, demonstrating that our facial hair capture method outperforms previous methods in terms of accuracy and completeness. Our method provides a comprehensive and accurate solution for capturing and modeling facial hair in various facial performance scenarios. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) has become a powerful model for community discovery in complex networks. Existing NMF-based methods for community discovery often factorize the corresponding adjacent matrix of complex networks to obtain its community indicator matrix. However, the adjacent matrix cannot represent the global structure feature of complex networks very well, and this leads to the performance degradation of community discovery. Besides, most of existing methods are not robust and scalable enough, so they are not effective to deal with complex networks with noises and large scales. Aiming at these problems above, in this paper we propose a method for community discovery using distributed robust NMF with SimRank similarity measure. This method selects SimRank measure to construct the feature matrix, which can more accurately represent the global structure feature of complex networks. To improve the robustness, we select ℓ2,1 norm instead of the widely used Frobenius norm to construct its NMF-based community discovery model. In addition, to improve the scalability, we implement its key components by using MapReduce distributed computing framework, including computing SimRank feature matrix and iteratively solving the NMF-based model for community discovery. We conduct extensive experiments on several typical complex networks. The results show that our method has better performance and robustness than other representative NMF-based methods for community discovery. Moreover, our method presents good scalability and hence can be used to discover communities in the large-scale complex networks. [ABSTRACT FROM AUTHOR]

Developer recommendation is an essential task for resolving incoming issues in the evolution of software. Many developer recommendation techniques have been developed in the literature; among these studies, most techniques usually combined historical commits as supplementary information with bug repositories and/or source-code repositories to recommend developers. However, the question of whether the messages in historical commits are always useful has not yet been answered. This article aims at solving this problem by conducting an empirical study on four open-source projects. The results show that: (1) the number of meaningful words of the commit description has an impact on the quality of the commit, and a larger number of meaningful words in the description means that it can generally better reflect developers’ expertise; (2) using commit description to recommend the relevant developers is better than that using relevant files that are recorded in historical commits; (3) developers tend to change the relevant files that they have changed many times before; (4) developers generally tend to change the files that they have changed recently. [ABSTRACT FROM AUTHOR]

Flow experience is often considered as an important standard of ideal user experience (UX). Till now, flow is mainly measured via self-report questionnaires, which cannot evaluate flow immediately and objectively. In this paper, we constructed a physiological evaluation model to evaluate flow in virtual reality (VR) game. The evaluation model consists of five first-level indicators and their respective second-level indicators. Then, we conducted an empirical experiment to test the effectiveness of partial indicators to predict flow experience. Most results supported the model and revealed that heart rate, interbeat interval, heart rate variability (HRV), low-frequency HRV (LF-HRV), high-frequency HRV (HF-HRV), and respiratory rate are all effective indicators in predicting flow experience. Further research should be conducted to improve the evaluation model and conclude practical implications in UX and VR game design. [ABSTRACT FROM AUTHOR]

In studies of lake-atmosphere interactions, the fluxes of momentum, water vapour and sensible heat are often parametrized as being proportional to the differences in wind, humidity and air temperature between the water surface and a reference height above the surface. Here, the proportionality via transfer coefficients in these relationships was investigated with the eddy-covariance method at three sites within an eddy-covariance mesonet across Lake Taihu, China. The results indicate that the transfer coefficients decreased with increasing wind speed for weak winds and approached constant values for strong winds. The presence of submerged macrophytes reduced the momentum transfer (drag) coefficient significantly. At the two sites free of submerged macrophytes, the 10-m drag coefficients under neutral stability were 1.8 $$(\pm \,0.4) \times \,10^{-3}$$ and $$1.7\,(\pm \,0.3) \times \,10^{-3 }$$ at the wind speed of $$9\,\text{ m } \text{ s }^{-1}$$ , which are 38 and 34 % greater than the prediction by the Garratt model for the marine environment. [ABSTRACT FROM AUTHOR]