These technologies blend digital elements into the real world to varying extents, as shown in Figure 1. Most are envisioned to be primarily used in in - premise domestic entertainment and industrial applications. Currently in these settings, low data rate, non - guaranteed bandwidth, and high - latency transmission technologies such as WiFi are deployed. However, to support human operators with a comfortable quality of experience (QoE) during XR collaborations, high - quality XR video frames (8K, 16K) are required to be transmitted with end - to - end inter - frame latency ( 20 ms) and jitter ( 15 ms) requirements [2].

Granville V., Rasson J.P. Multivariate discriminate analysis and maximum penalized likelihood.... Journal of the Royal Statistical Society, Series B, 57 (1995), 501-517.

Granville V., Rasson J.P. Multivariate discriminate analysis and maximum penalized likelihood.... Journal of the Royal Statistical Society, Series B, 57 (1995), 501-517.

Second beauty contest, where humans are judged by Artificial Intelligence Beauty.AI 2.0 launches. Robot judgement day is set for 01.08.2016. People are encouraged to download the free Beauty.AI 2.0 mobile app in either the Google Play market or Apple App Store and algorithm developers should reach out to the organizers through a form on the http://beauty.ai/ Beauty.AI 1.0 was covered by the leading reporters and journalists at New Scientist, Techcrunch, Cosmopolitan, GQ, Yahoo! Beauty.AI 2.0 will provide valuable prizes for both human competitors and robot jury members.

Social media data mining and analytics has stimulated a wide array of computational research. Traditionally, individual researchers are responsible for acquiring and managing their own datasets. However, the temporal nature of social data, the challenges involved in correctly preparing a dataset, the sheer scale of many datasets, and the proprietary nature of many data sources can make extending and comparing computational methods difficult and often impossible. In light of this, because replicability is a fundamental pillar of the scientific process and because method comparison is essential to characterizing computational advancements, we require an alternative to the traditional model of researcher-owned datasets. In this paper we propose FREESR, a framework that gives researchers the ability to develop and test method performance without requiring direct access to “shared” datasets. As a case study and first community resource, we have implemented the FREESR paradigm around the task of Tweet geolocation. The implementation showcases the clear suitability of this framework for the social media research context. Beyond the implementation, we see the FREESR paradigm as being an important step towards making study reproducibility and method comparison more principled and ubiquitous in the social media research community.