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Shen, R.; Wang, J.; Fox, E.A.: ¬A Lightweight Protocol between Digital Libraries and Visualization Systems (2002)
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- Date
- 22. 2.2003 17:25:39
22. 2.2003 18:15:14
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Lu, C.; Bu, Y.; Wang, J.; Ding, Y.; Torvik, V.; Schnaars, M.; Zhang, C.: Examining scientific writing styles from the perspective of linguistic complexity : a cross-level moderation model (2019)
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- Abstract
- Publishing articles in high-impact English journals is difficult for scholars around the world, especially for non-native English-speaking scholars (NNESs), most of whom struggle with proficiency in English. To uncover the differences in English scientific writing between native English-speaking scholars (NESs) and NNESs, we collected a large-scale data set containing more than 150,000 full-text articles published in PLoS between 2006 and 2015. We divided these articles into three groups according to the ethnic backgrounds of the first and corresponding authors, obtained by Ethnea, and examined the scientific writing styles in English from a two-fold perspective of linguistic complexity: (a) syntactic complexity, including measurements of sentence length and sentence complexity; and (b) lexical complexity, including measurements of lexical diversity, lexical density, and lexical sophistication. The observations suggest marginal differences between groups in syntactical and lexical complexity.
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Hicks, D.; Wang, J.: Coverage and overlap of the new social sciences and humanities journal lists (2011)
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- Date
- 22. 1.2011 13:21:28
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Mao, J.; Xu, W.; Yang, Y.; Wang, J.; Yuille, A.L.: Explain images with multimodal recurrent neural networks (2014)
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- Abstract
- In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel sentence descriptions to explain the content of images. It directly models the probability distribution of generating a word given previous words and the image. Image descriptions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on three benchmark datasets (IAPR TC-12 [8], Flickr 8K [28], and Flickr 30K [13]). Our model outperforms the state-of-the-art generative method. In addition, the m-RNN model can be applied to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval.
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He, R.; Wang, J.; Tian, J.; Chu, C.-T.; Mauney, B.; Perisic, I.: Session analysis of people search within a professional social network (2013)
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- Date
- 19. 4.2013 20:31:22
-
Jiang, Z.; Gu, Q.; Yin, Y.; Wang, J.; Chen, D.: GRAW+ : a two-view graph propagation method with word coupling for readability assessment (2019)
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- Date
- 15. 4.2019 13:46:22
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Wang, J.; Halffman, W.; Zhang, Y.H.: Sorting out journals : the proliferation of journal lists in China (2023)
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- Date
- 22. 9.2023 16:39:23
-
Wang, J.; Clements, M.; Yang, J.; Vries, A.P. de; Reinders, M.J.T.: Personalization of tagging systems (2010)
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Thomas, I.S.; Wang, J.; GPT-3: Was euch zu Menschen macht : Antworten einer künstlichen Intelligenz auf die großen Fragen des Lebens (2022)
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- m
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Qiu, J.; Zuo, M.; Wang, J.; Cai, C.: Knowledge order in an online knowledge community : group heterogeneity and two paths mediated by group interaction (2021)
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