Microsoft is introducing a new support offering for customers with Software Assurance on December 8. The company is rolling out in early 2017 Windows Server Premium Assurance and SQL Server Premium Assurance, which add six more years of support beyond the current 10. During the six years of Premium Assurance, customers will get Security updates and bulletins that are rated "Critical or "Important." Microsoft officials said in a blog post that the new extended support will help those who need to "continue to meet compliance requirements and ensure security on systems you aren't ready to update." They also are positioning the extended support option as offering "flexibility as you move to the cloud."
The testing blueprint for states provides details missing from the administration's guidelines for them to return to normal operations, which were released more than a week ago. It includes a focus on surveillance testing as well as "rapid response" programs to isolate those who test positive and identify those with whom they had come in contact. The administration aims to have the market "flooded" with tests for the fall, when COVID-19 is expected to recur alongside the seasonal flu.
The Semantic Web has the potential to change the Web as we know it. However, the community faces a significant challenge in managing, aggregating, and curating the massive amount of data and knowledge. Human computation is only beginning to serve an essential role in the curation of these Web-based data. Ontologies, which facilitate data integration and search, serve as a central component of the Semantic Web, but they are large, complex, and typically require extensive expert curation. Furthermore, ontology-engineering tasks require more knowledge than is required in a typical crowdsourcing-task. We have developed ontology-engineering methods that leverage the crowd. In this work, we describe our general crowdsourcing workflow. We then highlight our work on applying this workflow to ontology verification and quality assurance. In a pilot study, this method approaches expert ability, finding the same errors that experts identified with 86% accuracy in a faster and more scalable fashion. The work provides a general framework with which to develop crowdsourcing methods for the Semantic Web. In addition, it highlights opportunities for future research in human computation and crowdsourcing.