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  • × author_ss:"Zhou, F."
  • × year_i:[2010 TO 2020}
  1. Ménard, E.; Khashman, N.; Kochkina, S.; Torres-Moreno, J.-M.; Velazquez-Morales, P.; Zhou, F.; Jourlin, P.; Rawat, P.; Peinl, P.; Linhares Pontes, E.; Brunetti., I.: ¬A second life for TIIARA : from bilingual to multilingual! (2016) 0.01 
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    Abstract
    Multilingual controlled vocabularies are rare and often very limited in the choice of languages offered. TIIARA (Taxonomy for Image Indexing and RetrievAl) is a bilingual taxonomy developed for image indexing and retrieval. This controlled vocabulary offers indexers and image searchers innovative and coherent access points for ordinary images. The preliminary steps of the elaboration of the bilingual structure are presented. For its initial development, TIIARA included only two languages, French and English. As a logical follow-up, TIIARA was translated into eight languages-Arabic, Spanish, Brazilian Portuguese, Mandarin Chinese, Italian, German, Hindi and Russian-in order to increase its international scope. This paper briefly describes the different stages of the development of the bilingual structure. The processes used in the translations are subsequently presented, as well as the main difficulties encountered by the translators. Adding more languages in TIIARA constitutes an added value for a controlled vocabulary meant to be used by image searchers, who are often limited by their lack of knowledge of multiple languages.
    Source
    Knowledge organization. 43(2016) no.1, S.22-34
    Type
    a
  2. Xiao, C.; Zhou, F.; Wu, Y.: Predicting audience gender in online content-sharing social networks (2013) 0.00 
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    Abstract
    Understanding the behavior and characteristics of web users is valuable when improving information dissemination, designing recommendation systems, and so on. In this work, we explore various methods of predicting the ratio of male viewers to female viewers on YouTube. First, we propose and examine two hypotheses relating to audience consistency and topic consistency. The former means that videos made by the same authors tend to have similar male-to-female audience ratios, whereas the latter means that videos with similar topics tend to have similar audience gender ratios. To predict the audience gender ratio before video publication, two features based on these two hypotheses and other features are used in multiple linear regression (MLR) and support vector regression (SVR). We find that these two features are the key indicators of audience gender, whereas other features, such as gender of the user and duration of the video, have limited relationships. Second, another method is explored to predict the audience gender ratio. Specifically, we use the early comments collected after video publication to predict the ratio via simple linear regression (SLR). The experiments indicate that this model can achieve better performance by using a few early comments. We also observe that the correlation between the number of early comments (cost) and the predictive accuracy (gain) follows the law of diminishing marginal utility. We build the functions of these elements via curve fitting to find the appropriate number of early comments (approximately 250) that can achieve maximum gain at minimum cost.
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.6, S.1284-1297
    Type
    a

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