Collaborative Virtual Environments: an introductory review

In Virtual Reality. Research Developments and Applications, Vol 3, 3-15, 1998
Collaborative Virtual Environments: an introductory review
Elizabeth Churchill, Dave Snowdon
Abstract

A Collaborative Virtual Environment or CVE is a distributed, virtual reality that is designed to support collaborative activities. As such, CVEs provide a potentially infinite, graphically realised digital landscape within which multiple users can interact with each other and with simple or complex data representations.

CVEs are increasingly being used to support collaborative work between geographically separated and between collocated collaborators. CVEs vary in the sophistication of the data and embodiment representations employed and in the level of interactivity supported.

It is clear that systems which are intended to support collaborative activities should be designed with explicit consideration of the tasks to be achieved and the intended users' social and cognitive characteristics.

In this paper, we detail a number of existing systems and applications, but first discuss the nature of collaborative and cooperative work activities and consider the place of virtual reality systems in supporting such collaborative work. Following this, we discuss some future research directions.

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Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.

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