Semantic Concept Annotation of Consumer Videos at Frame-Level Using Audio

Author(s):  
Junwei Liang ◽  
Qin Jin ◽  
Xixi He ◽  
Gang Yang ◽  
Jieping Xu ◽  
...  
Keyword(s):  
2012 ◽  
Vol 38 (10) ◽  
pp. 1671
Author(s):  
Rui-Jie ZHANG ◽  
Zhi-Gang GUO ◽  
Bi-Cheng LI ◽  
Hao-Lin GAO

2020 ◽  
Vol 2 (25(52)) ◽  
pp. 27-47
Author(s):  
Olga Grigoryevna Puzyreva

The article describes the interpretation of the theme of love in the author's educational fiction text of the teacher for a foreign audience at the level of Russian language proficiency B1-C1 as a key value-semantic concept of the Russian mental-language picture of the world. The author presents, psychological, pedagogical, methodological and philosophical arguments for the need to include this topic in the proposed cycle of short stories. Special attention is paid to how the speech and "creative-motor " (S. V. Dmitriev) dialogue of the situation of love is interconnected with the surrounding socio-cultural space.


2020 ◽  
Vol 1 (2(71)) ◽  
pp. 45-66
Author(s):  
Olga Grigoryevna Puzyreva

The article describes the interpretation of the theme of love in the author's educational fiction text of the teacher for a foreign audience at the level of Russian language proficiency B1-C1 as a key value-semantic concept of the Russian mental-language picture of the world. The author presents psychological, pedagogical, methodological and philosophical arguments for the need to include this topic in the proposed cycle of short stories. Special attention is paid to how the speech and "creative-motor " (S. V. Dmitriev) dialogue of the situation of love is interconnected with the surrounding socio-cultural space.


Author(s):  
Jiaoyan Chen ◽  
Freddy Lecue ◽  
Jeff Z. Pan ◽  
Huajun Chen

Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic embeddings. The experiments show accurate prediction with data from Dublin and Beijing.


2008 ◽  
Vol 39 (2) ◽  
pp. 243-262 ◽  
Author(s):  
Man-Kwan Shan ◽  
Meng-Fen Chiang ◽  
Fang-Fei Kuo

2017 ◽  
pp. 31-49 ◽  
Author(s):  
Qiusha Zhu ◽  
Mei-Ling Shyu ◽  
Shu-Ching Chen

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