A Framework towards Quantifying Human Restorativeness in Virtual Built Environments
Abstract: The impact of built environment on the human restorativeness has long been argued; however, the interrelations between neuroscience and the built environment, and the degree to which the built environment contributes to increased human restorativeness has not been completely understood yet. Understanding the interrelations between neuroscience and the built environment is critical as 90% […]
The agency effect: The impact of student agency on learning, emotions, and problem-solving behaviors in a game-based learning environment
Abstract: Game-based learning environments are designed to foster high levels of student engagement and motivation during learning of complex topics. Game-based learning environments allow students freedom to navigate a space to interact with game elements that foster learning, i.e., agency. Agency has been studied in learning, and it has been demonstrated that increased student agency […]
Modeling emotion in complex stories: the Stanford Emotional Narratives Dataset
Abstract: Human emotions unfold over time, and more affective computing research has to prioritize capturing this crucial component of real-world affect. Modeling dynamic emotional stimuli requires solving the twin challenges of time-series modeling and of collecting high-quality time-series datasets. We begin by assessing the state-of-the-art in time-series emotion recognition, and we review contemporary time-series approaches […]
Between text and platforms: A case study on the real-time emotions and psychophysiological indicators of video gaming and academic engagement
Abstract: Literature indicates that both academic tasks (Deater-Deckard et al. 2013; Skinner et al. Educational and Psychological Measurement, 69(3): 493–525, 2009) and video gaming (Irwin The Journal of Psychology, 133, 157–164, 1999; Laffan et al. Computers in Human Behavior, 65, 544–549, 2016; Whitton Simulation & Gaming, 42(5), 596–609, 2011) have qualities that engage and captivate […]
The Effectiveness of Online Cause-Related Marketing Message Framing on Hotel Brand Evaluation
Abstract: The purpose of this study is to examine the interaction effect between online Cause-Related Marketing (CRM) advertisements and brand reputation on consumers’ brand evaluation in the hotel industry. Two experimental studies were conducted: (1) a survey-based experiment that demonstrates the moderating role of brand reputation, and (2) a laboratory experiment using psychophysiological measurements of emotional […]
Investigating the detection of emotion concealment using the Gazepoint GP3 eye-tracker
Abstract: In the investigative field, the use of interviews as a method of gathering legally admissible information is in serious decline. Due to a greater understanding of human stress response and biometric measurements, previously objective tools such as the polygraph have been removed from the investigators toolkit. Newer methods of biometric monitoring can potentially help […]
Factorized Multimodal Transformer For Multimodal Sequential Learning
Abstract: The complex world around us is inherently multimodal and sequential (continuous). Information is scattered across different modalities and requires multiple continuous sensors to be captured. As machine learning leaps towards better generalization to real world, multimodal sequential learning becomes a fundamental research area. Arguably, modeling arbitrarily distributed spatio-temporal dynamics within and across modalities is […]
Rhetorical strategies and emotions in political marketing management
Abstract: Purpose The purpose of this paper is to analyze how political marketing management in terms of communication practices influence the voters’ emotional responses as they observe and listen to the discourse of a political leader. Design/methodology/approach An experiment was conducted, in which participants watched the last debate of the campaign leading up the Peruvian […]
Modality to Modality Translation: An Adversarial Representation Learning and Graph Fusion Network for Multimodal Fusion
Abstract: Learning joint embedding space for various modalities is of vital importance for multimodal fusion. Mainstream modality fusion approaches fail to achieve this goal, leaving a modality gap which heavily affects cross-modal fusion. In this paper, we propose a novel adversarial encoder-decoderclassifier framework to learn a modality-invariant embedding space. Since the distributions of various modalities […]
Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis
Abstract: Multimodal language analysis often considers relationships between features based on text and those based on acoustical and visual properties. Text features typically outperform non-text features in sentiment analysis or emotion recognition tasks in part because the text features are derived from advanced language models or word embeddings trained on massive data sources while audio […]
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