V
Vincent G. Duffy
Researcher at Purdue University
Publications - 56
Citations - 737
Vincent G. Duffy is an academic researcher from Purdue University. The author has contributed to research in topics: Computer science & Publish or perish. The author has an hindex of 9, co-authored 56 publications receiving 340 citations.
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Journal ArticleDOI
Seven HCI Grand Challenges
Constantine Stephanidis,Gavriel Salvendy,Margherita Antona,Jessie Y. C. Chen,Jianming Dong,Vincent G. Duffy,Xiaowen Fang,Cali M. Fidopiastis,Gino Fragomeni,Limin Paul Fu,Yinni Guo,Don Harris,Andri Ioannou,Kyeong-Ah Jeong,Shin'ichi Konomi,Heidi Krömker,Masaaki Kurosu,James R. Lewis,Aaron Marcus,Gabriele Meiselwitz,Abbas Moallem,Hirohiko Mori,Fiona Fui-Hoon Nah,Stavroula Ntoa,Pei-Luen Patrick Rau,Dylan Schmorrow,Keng Siau,Norbert A. Streitz,Wentao Wang,Sakae Yamamoto,Panayiotis Zaphiris,Jia Zhou +31 more
TL;DR: The Grand Challenges which arise in the current and emerging landscape of rapid technological evolution towards more intelligent interactive technologies, coupled with increased and widened societal needs, as well as individual and collective expectations that HCI, as a discipline, is called upon to address are investigated.
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Product Placement in Mass Media: A Review and Bibliometric Analysis
TL;DR: The authors provide a comprehensive and systematic review of the product placement literature in order to map the thematic development of product placement research, help researchers understand the research, and provide an overview of the most popular product placement topics.
Human centred computing : cognitive, social and ergonomic aspects
TL;DR: Universal Access and Design for All Assistive Technologies Mobile and Ubiquitious Interaction Adaptation and Personalization Access to Information User Diversity Accessibility and Usability Applications and Services Non-Visual Interaction Interaction Devices, Techniques and Modalities Design and Evaluation Tools.
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Bibliometric Analysis of Affective Computing Researches during 1999~2018
TL;DR: This paper can enable researchers to gain wider and deeper insight into affective computing researches in the last decades through bibliometric analysis, thereby facilitating relevant researchers having a general understanding of aggregate performance in the affective Computing field and finding research directions in the future.
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Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning
TL;DR: The classification of mental workload using combined indices as inputs showed that classification models combining physiological signals and task performance can reach satisfying accuracy, and showed that ECG and EDA signals have good discriminating power for mental workload detection.