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Cybersickness is one of the greatest barriers to the adoption of virtual reality. A growing body of research has focused on identifying the characteristics of cybersickness and finding ways to mitigate it through the utilization of data from VR content, physiological signals, and body movement, along with artificial intelligence techniques. In this work, we extend prior research on cybersickness prediction by considering the role of different data modalities. We propose a novel deep learning model named multimodal, attention-based cybersickness (MAC), which learns temporal sequences and characteristics of video flows, eye movement, head movement, and electrodermal activity. Based on data collected from 27 participants, we demonstrate the effectiveness of MAC, showing an F1-score of 0.87. Our experimental results further show not only the influences of gender and prior VR experience but also the effectiveness of the attention mechanism on model performance, emphasizing the importance of considering the characteristics of data types and users in cybersickness modeling. [ABSTRACT FROM AUTHOR]

"Beaming" systems aim to physically transfer a person to a remote place and whilst many researchers have attempted to facilitate a beaming system based on technological advances in teleoperation, robotics, and virtual reality; however, telecommunication experiences with full-body ownership have not yet been rigorously investigated. The present study aims to develop a beaming system that provide a full-body ownership through a humanoid robot, and investigate users' telecommunication experiences as roles of visitor and local with different levels of controllability. Forty participants were assigned to both visitor and local roles, and their copresence, usability, eye-contact, emotion, verbal, and gesture communications were investigated. The results suggested that the subjective reports on the telecommunication experience of the visitors were generally controlled in a stepwise manner depending on the level of embodiment controllability, whereas that of the locals were not. However, participants' emotion, verbal, and gesture communications showed synchronizing tendencies between the visitor and the local. The implications of these results for future physical beaming systems are discussed. [ABSTRACT FROM AUTHOR]

Along with the rapidly increasing influence and importance of advertisements and publicity in social networking services (SNS), considerable efforts are being made to provide user-customized services through an understanding of SNS content. Studies on online purchasing patterns based on user attributes have also been conducted; however, these studies used either only experimental methods (e.g., surveys or ethnographic accounts) or simple user attributes (e.g., age, biological sex, and location) for computational user modeling. This paper, through interviews with professional marketers, identifies their needs to understand multifactorial SNS user (potential customers) attributes—gender (i.e., masculine, feminine, androgynous) and biological sex (i.e., male and female) characteristics—for marketing purposes. Based on 33,752 Instagram posts, we develop a deep learning-based, classification model merged with three modalities—image (i.e., VGG16 feature and gesture), text (i.e., linguistic, tag, sentence, and category), and activity (i.e., reply and day). Our model achieves a better performance in classifying three gender types in the male, female, and male + female cases than the traditional machine learning models. Our study results reveal the applicability of identifying gender characteristics from posts in the marketing field. [ABSTRACT FROM AUTHOR]

The wide use of recommendation systems includes more users and items in system operations, leading to a significant increase in the size of related datasets. However, recommendation algorithms on existing single-machine-based graph engines have been developed without considering the important characteristics of recommendation datasets, i.e., huge size and power-law degree distribution. In this paper, we address how to realize efficient graph- and matrix-factorization-based recommendation algorithms, handling recommendation datasets on RealGraph, a state-of-the-art single-machine-based graph engine. Through extensive experiments, we demonstrate that our recommendation algorithms on RealGraph universally and consistently outperform the algorithms on other graph engines over all datasets up to 34 times. [ABSTRACT FROM AUTHOR]

Social scientists have identified a crisis of community in modern society, for example, Putnam's (Bowling alone: the collapse and revival of American community. Simon and Schuster, New York, ) study of a 40-year decline of community in American society. We argue that the design palette of Web 2.0 offers critical affordances for community information and interaction that enable hyperlocality and suprathresholding. We describe a community-based participatory design research project through which we investigated the design characteristics of hyperlocality and suprathresholding. We speculate on the possibility of a new design model for community networks. [ABSTRACT FROM AUTHOR]