Search

International audience; The ISO rubber ball is used in measurement standards to assess heavy impacts on floors such as from footsteps in bare feet or children jumping. This paper investigates the prediction of the impact sound insulation using the Finite Element Method (FEM) and Transient Statistical Energy Analysis (TSEA). An assessment is made of modelling the rubber ball impact on a timber floor by simulating the rubber ball or by applying the contact force directly. The latter is advantageous in reducing the computation time and examples are shown of the differences in the approaches. An experimentally validated FEM model of a small timber floor is compared with Transient Statistical Energy Analysis (TSEA) in terms of the spatial-average maximum time-weighted vibration level on the surface of the timber floor using TSEA models of increasing complexity. Finally, laboratory measurements for a real case of rubber ball drop on a timber floor is used to assess FEM and TSEA models.

Community-based Question and Answering (CQA) platforms are nowadays enlightening over a billion people with crowdsourced knowledge. A key design issue in CQA platforms is how to find the potential answerers and to provide the askers timely and suitable answers, i.e., the so-called \textit{question routing} problem. State-of-art approaches often rely on extracting topics from the question texts. In this work, we analyze the question routing problem in a CQA system named Farm-Doctor that is exclusive for agricultural knowledge. The major challenge is that its questions contain limited textual information. To this end, we conduct an extensive measurement and obtain the whole knowledge repository of Farm-Doctor that consists of over 690 thousand questions and over 3 million answers. To remedy the text deficiency, we model Farm-Doctor as a heterogeneous information network that incorporates rich side information and based on network representation learning models we accurately recommend for each question the users that are highly likely to answer it. With an average income of fewer than 6 dollars a day, over 300 thousands farmers in China seek online in Farm-Doctor for agricultural advices. Our method helps these less eloquent farmers with their cultivation and hopefully provides a way to improve their lives.

Crowdsourced video systems like YouTube and Twitch.tv have been a major internet phenomenon and are nowadays entertaining over a billion users. In addition to video sharing and viewing, over the years they have developed new features to boost the community engagement and some managed to attract users to donate, to the community as well as to other users. User donation directly reflects and influences user engagement in the community, and has a great impact on the success of such systems. Nevertheless, user donations in crowdsourced video systems remain trade secrets for most companies and to date are still unexplored. In this work, we attempt to fill this gap, and we obtain and provide a publicly available dataset on user donations in one crowdsourced video system named BiliBili. Based on information on nearly 40 thousand donators, we examine the dynamics of user donations and their social relationships, we quantitively reveal the factors that potentially impact user donation, and we adopt machine-learned classifiers and network representation learning models to timely and accurately predict the destinations of the majority and the individual donations., 8 pages