Immersive technologies, such as virtual and augmented reality, initially failed to live up to expectations, but have improved greatly, with many new head-worn displays and associated applications being released over the past few years. Unfortunately, 'cybersickness' remains as a common user problem that must be overcome if mass adoption is to be realized. This article evaluates the state of research on this problem, identifies challenges that must be addressed, and formulates an updated cybersickness research and development (R&D) agenda. The new agenda recommends prioritizing creation of powerful, lightweight, and untethered head-worn displays, reduction of visual latencies, standardization of symptom and aftereffect measurement, development of improved countermeasures, and improved understanding of the magnitude of the problem and its implications for job performance. Some of these priorities are unresolved problems from the original agenda which should get increased attention now that immersive technologies are proliferating widely. If the resulting R&D agenda is carefully executed, it should render cybersickness a challenge of the past and accelerate mass adoption of immersive technologies to enhance training, performance, and recreation.
This report represents a committee summary of the current state of knowledge regarding aftereffects and sense of presence in virtual environments (VEs). The work presented in this article, and the proposed research agenda, are the result of a special session that was set up in the framework of the Seventh International Conference on Human Computer Interaction. Recommendations were made by the committee regarding research needs in aftereffects and sense of presence, and, where possible, priorities were suggested. The research needs were structured in terms of the short, medium, and long term and, if followed, should lead toward the effective use of VE technology. The 2 most critical research issues identified were (a) standardization and use of measurement approaches for aftereffects and (b) identification and prioritization of sensorimotor discordances that drive aftereffects. Identification of aftereffects countermeasures (i.e., techniques to assist users in readily transitioning between the real and virtual worlds), reduction of system response latencies, and improvements in tracking technology were also thought to be of critical importance.
We present diagnostic criteria for motion sickness, visually induced motion sickness (VIMS), motion sickness disorder (MSD), and VIMS disorder (VIMSD) to be included in the International Classification of Vestibular Disorders. Motion sickness and VIMS are normal physiological responses that can be elicited in almost all people, but susceptibility and severity can be high enough for the response to be considered a disorder in some cases. This report provides guidelines for evaluating signs and symptoms caused by physical motion or visual motion and for diagnosing an individual as having a response that is severe enough to constitute a disorder. The diagnostic criteria for motion sickness and VIMS include adverse reactions elicited during exposure to physical motion or visual motion leading to observable signs or symptoms of greater than minimal severity in the following domains: nausea and/or gastrointestinal disturbance, thermoregulatory disruption, alterations in arousal, dizziness and/or vertigo, headache and/or ocular strain. These signs/symptoms occur during the motion exposure, build as the exposure is prolonged, and eventually stop after the motion ends. Motion sickness disorder and VIMSD are diagnosed when recurrent episodes of motion sickness or VIMS are reliably triggered by the same or similar stimuli, severity does not significantly decrease after repeated exposure, and signs/symptoms lead to activity modification, avoidance behavior, or aversive emotional responses. Motion sickness/MSD and VIMS/VIMSD can occur separately or together. Severity of symptoms in reaction to physical motion or visual motion stimuli varies widely and can change within an individual due to aging, adaptation, and comorbid disorders. We discuss the main methods for measuring motion sickness symptoms, the situations conducive to motion sickness and VIMS, and the individual traits associated with increased susceptibility. These additional considerations will improve diagnosis by fostering accurate measurement and understanding of the situational and personal factors associated with MSD and VIMSD.
Social networks involve both positive and negative relationships, which can be captured in signed graphs. The edge sign prediction problem aims to predict whether an interaction between a pair of nodes will be positive or negative. We provide theoretical results for this problem that motivate natural improvements to recent heuristics.The edge sign prediction problem is related to correlation clustering; a positive relationship means being in the same cluster. We consider the following model for two clusters: we are allowed to query any pair of nodes whether they belong to the same cluster or not, but the answer to the query is corrupted with some probability 0 < q < 1 2 . Let δ = 1 − 2q be the bias. We provide an algorithm that recovers all signs correctly with high probability in the presence of noise with O( n log n δ 2 + log 2 n δ 6 ) queries. This is the best known result for this problem for all but tiny δ, improving on the recent work of Mazumdar and Saha [27]. We also provide an algorithm that performs O( n log n δ 4 ) queries, and uses breadth first search as its main algorithmic primitive. While both the running time and the number of queries for this algorithm are sub-optimal, our result relies on novel theoretical techniques, and naturally suggests the use of edge-disjoint paths as a feature for predicting signs in online social networks. Correspondingly, we experiment with using edge disjoint s − t paths of short length as a feature for predicting the sign of edge (s, t) in real-world signed networks. Empirical findings suggest that the use of such paths improves the classification accuracy, especially for pairs of nodes with no common neighbors.
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