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pro vyhledávání: '"Tanveer, Md. Iftekhar"'
\emph{Topological data analysis} (TDA) has recently emerged as a new technique to extract meaningful discriminitve features from high dimensional data. In this paper, we investigate the possibility of applying TDA to improve the classification accura
Externí odkaz:
http://arxiv.org/abs/2103.14131
The role of verbal and non-verbal cues towards great public speaking has been a topic of exploration for many decades. We identify a commonality across present theories, the element of "variety or heterogeneity" in channels or modes of communication
Externí odkaz:
http://arxiv.org/abs/2012.06157
With the recent trend of applying machine learning in every aspect of human life, it is important to incorporate fairness into the core of the predictive algorithms. We address the problem of predicting the quality of public speeches while being fair
Externí odkaz:
http://arxiv.org/abs/1911.11558
We use the largest open repository of public speaking---TED Talks---to predict the ratings of the online viewers. Our dataset contains over 2200 TED Talk transcripts (includes over 200 thousand sentences), audio features and the associated meta infor
Externí odkaz:
http://arxiv.org/abs/1906.03940
Automated prediction of public speaking performance enables novel systems for tutoring public speaking skills. We use the largest open repository---TED Talks---to predict the ratings provided by the online viewers. The dataset contains over 2200 talk
Externí odkaz:
http://arxiv.org/abs/1905.08392
Autor:
Hasan, Md Kamrul, Rahman, Wasifur, Zadeh, Amir, Zhong, Jianyuan, Tanveer, Md Iftekhar, Morency, Louis-Philippe, Mohammed, Hoque
Publikováno v:
EMNLP-IJCNLP, 2019, 2046-2056
Humor is a unique and creative communicative behavior displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (vision) and prosodic cues (acoustic). Understanding humor from these three
Externí odkaz:
http://arxiv.org/abs/1904.06618
We present a framework to identify whether a public speaker's body movements are meaningful or non-meaningful ("Mannerisms") in the context of their speeches. In a dataset of 84 public speaking videos from 28 individuals, we extract 314 unique body m
Externí odkaz:
http://arxiv.org/abs/1707.04790
Akademický článek
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Publikováno v:
Affective Computing & Intelligent Interaction: 4th International Conference, ACII 2011, Memphis, TN, USA, October 9-12, 2011, Proceedings, Part II; 2011, p598-607, 10p
Autor:
Tanveer, Md. Iftekhar, Anam, A.S.M. Iftekhar, Rahman, A.K.M Mahbubur, Ghosh, Sreya, Yeasin, Mohammed
Publikováno v:
ACM SIGACCESS Conference on Computers & Accessibility; Jan2012, p207-208, 2p