The Measurement and Evaluation of Professional League of Legends Teams for Optimal Strategy

With ever improving streaming technologies and accessibility to video games, it comes asno surprise that competitive gaming or eSports have blown up in recent time. League ofLegends, former gaming startup Riot Games' sole intellectual property, has the title mostpopular eSport in the world with a thriving competitive scene and international competitionthat rivals traditional sports leagues such as the MLB, the NBA and the NFL [Sta, 2013].With the high stakes involved in the burgeoning eSports industry, it is imperative that theseorganizations develop methods that can dierentiate players based on their skill throughtheir in-game performance metrics and determine potential acquisitions. Additionally, wewant to leverage the data within Riot Games' databases on how the general playerbaseapproaches the game to determine what how in game performance metrics change as playerskill increases. The end goal of this analysis is to create a method to gauge team performanceand assess weak links in strategy

Acquisition and extinction across multiple virtual reality contexts: implications for specific phobias and current treatment methods

Victor Wong studied human acquisition learning over multiple contexts using virtual reality. He found that learning an association over multiple contexts can impact subsequent extinction training. This suggests that fears acquired over multiple contexts may be more difficult to treat using exposure-based therapies and will need to be augmented for effectiveness

Grimoires: Grid Registry with Metadata Oriented Interface: Robustness, Efficiency, Security --- Work-in-Progress

Grid registries allow users to discover resources made available by Grid resource providers. In this paper, we present our on-going work on a next-generation registry, initially designed as part of the myGrid project and to be part of the OMII Grid software release. Specifically, we discuss the support of semantic service descriptions and task/user-specific metadata, along with related performance and security considerations

Ethical problems of smart wearable devices

The stock market plays a major role in the entire financial market. How to obtain effective trading signals in the stock market is a topic that stock market has long been discussing. This paper first reviews the Deep Reinforcement Learning theory and model, validates the validity of the model through empirical data, and compares the benefits of the three classical Deep Reinforcement Learning models. From the perspective of the automated stock market investment transaction decision-making mechanism, Deep Reinforcement Learning model has made a useful reference for the construction of investor automation investment model, the construction of stock market investment strategy, the application of artificial intelligence in the field of financial investment and the improvement of investor strategy yield