With many institutions forced online due to the pandemic, assessments became a challenge for many educators. Take-home exams provided the flexibility required for varied student needs (and time zones), but they were vulnerable to cheating. In response, many turned to tools that could present a different exam for every student. PrairieLearn is a feature-rich open-source package that allows educators to author randomized Question Generators; we have been using the tool extensively for the last two years, and it has a fast-growing educator user base. One of the first issues we noticed with the system was that the only way to quality assure (QA) a question was to click the new variant button, which would spin whatever internal random number generators were used again to produce a new question. Sometimes it was the same one you had just seen, and other times it would never seem to "hit'' on the variant you were looking to debug. This poster describes our team's work to solve this problem through the design of an API that would allow a question to declare how many total variants it had, and be asked to render variant i. The user interface could then be extended to list what variant the QA team was viewing out of the total (e.g., 7/50), and a next, previous and go to a particular variant buttons would allow for the team to easily QA all variants.

A database service is required to meet the consistency, performance, and availability goals of modern applications serving a global user-base. Configuring a database deployed across multiple regions such that it fulfils these goals requires significant expertise. In this paper, we describe how CockroachDB makes this easy for developers by providing a high-level declarative syntax that allows expressing data access locality and availability goals through SQL statements. These high-level goals are then mapped to database configuration, replica placement, and data partitioning decisions. We show how all layers of the database, from the SQL Optimizer to Replication, were enhanced to support multi-region workloads. We also describe a new Transaction Management protocol that enables local, strongly consistent reads from any database replica. Finally, the paper includes an extensive evaluation demonstrating that CockroachDB's new declarative SQL syntax for multi-region clusters is easy to use and supports a variety of configuration options with different performance tradeoffs to benefit a variety of workloads. We also show that throughput scales linearly with the number of regions, and the new Transaction Management protocol reduces tail latency by over 10x compared to prior approaches.

A database service is required to meet the consistency, performance, and availability goals of modern applications serving a global user-base. Configuring a database deployed across multiple regions such that it fulfills these goals requires significant expertise. In this paper, we describe how CockroachDB makes this easy for developers by providing a high-level declarative syntax that allows expressing data access locality and availability goals through SQL statements. CockroachDB also enables many types of queries on the multiregion database to perform as well as they would in a single-region deployment, due to enhancements to the SQL optimizer, transaction, and replication layers. This paper showcases these features with a comprehensive demonstration scenario tracking a ride-sharing company's journey as they expand their application globally.

The game industry is moving into an era where old-style game engines are being replaced by re-engineered systems with embedded machine learning technologies for the operation, analysis and understanding of game play. In this paper, we describe our machine learning course designed for graduate students interested in applying recent advances of deep learning and reinforcement learning towards gaming. This course serves as a bridge to foster interdisciplinary collaboration among graduate schools and does not require prior experience designing or building games. Graduate students enrolled in this course apply different fields of machine learning techniques such as computer vision, natural language processing, computer graphics, human computer interaction, robotics and data analysis to solve open challenges in gaming. Student projects cover use-cases such as training AI-bots in gaming benchmark environments and competitions, understanding human decision patterns in gaming, and creating intelligent non-playable characters or environments to foster engaging gameplay. Projects demos can help students open doors for an industry career, aim for publications, or lay the foundations of a future product. Our students gained hands-on experience in applying state of the art machine learning techniques to solve real-life problems in gaming.