Hero drafting for multiplayer online arena (MOBA) games is crucial because drafting directly affects the outcome of a match. Both sides take turns to "ban"/"pick" a hero from a roster of approximately 100 heroes to assemble their drafting. In professional tournaments, the process becomes more complex as teams are not allowed to pick heroes used in the previous rounds with the "best-of-N" rule. Additionally, human factors including the team's familiarity with drafting and play styles are overlooked by previous studies. Meanwhile, the huge impact of patch iteration on drafting strengths in the professional tournament is of concern. To this end, we propose a visual analytics system, BPCoach, to facilitate hero drafting planning by comparing various drafting through recommendations and predictions and distilling relevant human and in-game factors. Two case studies, expert feedback, and a user study suggest that BPCoach helps determine hero drafting in a rounded and efficient manner.

In SC20, (Srivastava et al. 2020) proposed a Parallel Framework forBayesianLearning, or ramBLe, for short, which is a highly parallel and efficient framework for learning the structure of Bayesian Networks (BNs) from samples,There was a discrepancy in Bibliography in the PDF and the source file. We have followed the source file. ?> particularly large genome-scale networks. As part of our participation in the SC21 Student Cluster Competition, our task was to verify conclusions from the original work (Srivastava et al. 2020). Here we present the outcome of our experiments, which were performed on a four-node cluster from the Oracle Cloud HPC platform. We reproduce the numerical results from (Srivastava et al. 2020), namely the algorithm's performance and scaling behavior using MPI and different Python and Boost libraries on the Oracle cloud.

Promotions are commonly used by e-commerce merchants to boost sales. The efficacy of different promotion strategies can help sellers adapt their offering to customer demand in order to survive and thrive. Current approaches to designing promotion strategies are either based on econometrics, which may not scale to large amounts of sales data, or are spontaneous and provide little explanation of sales volume. Moreover, accurately measuring the effects of promotion designs and making bootstrappable adjustments accordingly remains a challenge due to the incompleteness and complexity of the information describing promotion strategies and their market environments. We present PromotionLens, a visual analytics system for exploring, comparing, and modeling the impact of various promotion strategies. Our approach combines representative multivariant time-series forecasting models and well-designed visualizations to demonstrate and explain the impact of sales and promotional factors, and to support "what-if" analysis of promotions. Two case studies, expert feedback, and a qualitative user study demonstrate the efficacy of PromotionLens.