![]() H.264 support has been widely adopted across hardware platforms, and has broad hardware offload support, for improved performance and video quality. The H.264 format is historically the preferred option in the RTC industry. We’re additionally laying the foundation for video interoperability within the Edge RTC stack by adding support for additional feedback messages as well as congestion control and robustness mechanisms. As a first step towards video interoperability with other platforms, we’re now working to add support for the H.264/AVC codec. Our initial ORTC implementation included audio support as well as support for the H.264UC video codec – an implementation of H.264/SVC based on Skype extensions. #REALTIMES UPDATE UPDATE#We’ve been hard at work on the next steps for RTC in Edge, and wanted to share an update on our roadmap, which we discussed in detail last week at Edge Web Summit. ![]() Late last year, we released our initial ORTC implementation to the web community as part of EdgeHTML 13. Originally published in the Journal of Medical Internet Research (). ©Uddhav Vaghela, Simon Rabinowicz, Paris Bratsos, Guy Martin, Epameinondas Fritzilas, Sheraz Markar, Sanjay Purkayastha, Karl Stringer, Harshdeep Singh, Charlie Llewellyn, Debabrata Dutta, Jonathan M Clarke, Matthew Howard, PanSurg REDASA Curators, Ovidiu Serban, James Kinross. The three benefits of REDASA's design are as follows: (1) it adopts a user-friendly, human-in-the-loop methodology by embedding an efficient, user-friendly curation platform into a natural language processing search engine (2) it provides a curated data set in the JavaScript Object Notation format for experienced academic reviewers' critical appraisal choices and decision-making methodologies and (3) due to the wide scope and depth of its web crawling method, REDASA has already captured one of the world's largest COVID-19-related data corpora for searches and curation.ĬOVID-19 critical analysis data data science data synthesis database decision making infodemic infrastructure literature methodology misinformation pipeline research structured data synthesis web crawl data. ![]() This data set can act as ground truth for the future implementation of a live, automated systematic review. These articles provide COVID-19-related information and represent around 10% of all papers about COVID-19. By capturing curators' critical appraisal methodologies through the discrete labeling and rating of information, REDASA rapidly developed a foundational, pooled, data science data set of over 1400 articles in under 2 weeks. REDASA (Realtime Data Synthesis and Analysis) is now one of the world's largest and most up-to-date sources of COVID-19-related evidence it consists of 104,000 documents. This data pipeline uses a novel curation methodology that adopts a human-in-the-loop approach for the characterization of quality, relevance, and key evidence across a range of scientific literature sources. To create an infrastructure that addresses our objectives, the PanSurg Collaborative at Imperial College London has developed a unique data pipeline based on a web crawler extraction methodology. ![]() Our secondary objective is to validate this platform and the curation methodology for COVID-19-related medical literature by expanding the COVID-19 Open Research Dataset via the addition of new, unstructured data. The main objective of this project is to build a generic, real-time, continuously updating curation platform that can support the data synthesis and analysis of a scientific literature framework. Solutions that aim to alleviate this data synthesis-related challenge are unable to capture heterogeneous web data in real time for the production of concomitant answers and are not based on the high-quality information in responses to a free-text query. However, the COVID-19 pandemic has also triggered an unprecedented "infodemic" the velocity and volume of data production have overwhelmed many key stakeholders such as clinicians and policy makers, as they have been unable to process structured and unstructured data for evidence-based decision making. The scale and quality of the global scientific response to the COVID-19 pandemic have unquestionably saved lives. ![]()
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