Predicting Virtual World User Population Fluctuations with Deep Learning

<div><p>This paper proposes a system for predicting increases in virtual world user actions. The virtual world user population is a very important aspect of these worlds; however, methods for predicting fluctuations in these populations have not been well documented. Therefore, we attempt to predict changes in virtual world user populations with deep learning, using easily accessible online data, including formal datasets from Google Trends, Wikipedia, and online communities, as well as informal datasets collected from online forums. We use the proposed system to analyze the user population of EVE Online, one of the largest virtual worlds.</p></div

EVE Online Forum Opinion Analysis Example

<p>EVE Online Forum Opinion Analysis Example</p

Z-scores of fluctuations in population and results of opinion analysis.

<p>Some opinions show a trend similar to that of fluctuations in the population.</p

System overview.

<p>System overview.</p

Pearson Correlation Coefficient Result

<p>Pearson Correlation Coefficient Result</p

Example of a deep learning dataset.

<p>The z-score for data from the previous 20 days was used as the values A–J, which indicate the value of the sum of forum opinion on a given date. V–Z denote formal data values (number of topics, sum of replies, sum of views, Google Trends value, and Wikipedia page views) on a given date.</p

Experimental results of predicted fluctuations in the EVE Online user population

<p>Experimental results of predicted fluctuations in the EVE Online user population</p

Summary of crawled data

<p>Summary of crawled data</p

Staurosporine-producing <i>Streptomyces</i> sp. strain 11 × 1 cell-free culture filtrates control diseases caused by the oomycete plant pathogens <i>Pythium myriotylum</i> and <i>Phytophthora sojae</i>

Oomycetes are devastating soil-borne plant pathogens, and cause numerous losses on diverse crops worldwide. In this study, our aim was to identify actinomycetes with antimicrobial activity towards oomycetes from the rhizosphere of soybean. Strong growth inhibition of P. myriotylum was found in dual culture with a strain called 11 × 1, and also from using the 11 × 1 cell-free culture filtrate and EtOAc extract. The P. myriotylum growth-inhibiting activity of the 11 × 1 culture filtrates was stable at high temperatures, and acidic and alkaline conditions. The 11 × 1 strain was identified as a Streptomyces strain with the highest 16S rDNA sequence identity to S. scabiei, and phylogenomic analysis also showed clustering with S. scabiei strains. Applying the 11 × 1 culture filtrates to ginger and soybean leaves inhibited the necrosis caused by P. myriotylum and P. sojae, respectively. Pot assay experiments showed a strong control effect of the 11 × 1 culture filtrate against P. myriotylum-caused soft-rot disease of ginger, and against P. sojae caused root-rot of soybean. Analysis of the biosynthetic gene clusters from the genome of 11 × 1 predicted the production of staurosporine, and staurosporine was subsequently identified from LC-MS analysis of the 11 × 1 extract. The concentrations of staurosporine quantified in the 11 × 1 extracts were in the range of the staurosporine minimum inhibitory concentrations for P. myriotylum, supporting the contribution of staurosporine to the biocontrol effect of 11 × 1. The use of 11 × 1 culture filtrate to control both ginger and soybean diseases could be beneficial in an intercropping system of ginger and the leguminous nitrogen-fixing soybean.</p