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Kevin T. Maher

dblp:267/7477 · DBLP profile ↗
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3ranked-venue papers
1as first author
2since 2021 · last 2024
0000-0002-6486-4866ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author · 2 since 2021Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
2 papers
Visualization and visual analytics · 77% Multimedia analysis and retrieval · 23%
Human-computer interaction and pervasive computing
2 papers
Learning and educational technologies · 82% Human-robot interaction · 18%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › visual analytics
visual analytics system
1.322024
SpeechMirror: A Multimodal Visual Analytics System for Personalized Reflection of Online Public Speaking Effectiveness · IEEE Trans. Vis. Comput. Graph. 2024
E-ffective: A Visual Analytic System for Exploring the Emotion and Effectiveness of Inspirational Speeches · IEEE Trans. Vis. Comput. Graph. 2022
Learning and educational technologies › skill training
public speaking training
0.812024
SpeechMirror: A Multimodal Visual Analytics System for Personalized Reflection of Online Public Speaking Effectiveness · IEEE Trans. Vis. Comput. Graph. 2024
Visualization and visual analytics › information visualization
affective visualization
0.612022
E-ffective: A Visual Analytic System for Exploring the Emotion and Effectiveness of Inspirational Speeches · IEEE Trans. Vis. Comput. Graph. 2022
Visualization and visual analytics
visualization design
0.612022
E-ffective: A Visual Analytic System for Exploring the Emotion and Effectiveness of Inspirational Speeches · IEEE Trans. Vis. Comput. Graph. 2022
Human-robot interaction
multimodal data analysis
0.212022
E-ffective: A Visual Analytic System for Exploring the Emotion and Effectiveness of Inspirational Speeches · IEEE Trans. Vis. Comput. Graph. 2022

Methods — techniques the papers use, named apart from their topics

visual summarization · 1.5similarity recommendation · 1.5statistical analysis · 1.1multimodal feature extraction · 1.1
YearPublicationVenuePosition
2024 SpeechMirror: A Multimodal Visual Analytics System for Personalized Reflection of Online Public Speaking Effectiveness
abstract
As communications are increasingly taking place virtually, the ability to present well online is becoming an indispensable skill. Online speakers are facing unique challenges in engaging with remote audiences. However, there has been a lack of evidence-based analytical systems for people to comprehensively evaluate online speeches and further discover possibilities for improvement. This paper introduces SpeechMirror, a visual analytics system facilitating reflection on a speech based on insights from a collection of online speeches. The system estimates the impact of different speech techniques on effectiveness and applies them to a speech to give users awareness of the performance of speech techniques. A similarity recommendation approach based on speech factors or script content supports guided exploration to expand knowledge of presentation evidence and accelerate the discovery of speech delivery possibilities. SpeechMirror provides intuitive visualizations and interactions for users to understand speech factors. Among them, SpeechTwin, a novel multimodal visual summary of speech, supports rapid understanding of critical speech factors and comparison of different speech samples, and SpeechPlayer augments the speech video by integrating visualization of the speaker's body language with interaction, for focused analysis. The system utilizes visualizations suited to the distinct nature of different speech factors for user comprehension. The proposed system and visualization techniques were evaluated with domain experts and amateurs, demonstrating usability for users with low visualization literacy and its efficacy in assisting users to develop insights for potential improvement.
Kevin T. Maher, Xiaoming Deng 0001, Yukun Lai, CuiXia Ma, Sheng Feng Qin, Yong-Jin Liu 0001, Hongan Wang
IEEE Trans. Vis. Comput. Graph.3
2022 E-ffective: A Visual Analytic System for Exploring the Emotion and Effectiveness of Inspirational Speeches
abstract
What makes speeches effective has long been a subject for debate, and until today there is broad controversy among public speaking experts about what factors make a speech effective as well as the roles of these factors in speeches. Moreover, there is a lack of quantitative analysis methods to help understand effective speaking strategies. In this paper, we propose E-ffective, a visual analytic system allowing speaking experts and novices to analyze both the role of speech factors and their contribution in effective speeches. From interviews with domain experts and investigating existing literature, we identified important factors to consider in inspirational speeches. We obtained the generated factors from multi-modal data that were then related to effectiveness data. Our system supports rapid understanding of critical factors in inspirational speeches, including the influence of emotions by means of novel visualization methods and interaction. Two novel visualizations include E-spiral (that shows the emotional shifts in speeches in a visually compact way) and E-script (that connects speech content with key speech delivery information). In our evaluation we studied the influence of our system on experts' domain knowledge about speech factors. We further studied the usability of the system by speaking novices and experts on assisting analysis of inspirational speech effectiveness.
Kevin T. Maher, Jian-Cheng Song, Xiaoming Deng 0001, Yukun Lai, CuiXia Ma, Hao Wang 0005, Yong-Jin Liu 0001, Hongan Wang
IEEE Trans. Vis. Comput. Graph.1
2020 EmotionMap: Visual Analysis of Video Emotional Content on a Map
CuiXia Ma, Jian-Cheng Song, Qian Zhu 0010, Kevin T. Maher, Hongan Wang
J. Comput. Sci. Technol.4