I analyzed the Hextech-Crafting in a Stochastic Simulation with 10 Million Players and found that you're going to make more Riot Points than you spend (in value) and thats without counting Champs. I've made some assumptions, which can be found down in the paper itself for those interested. For everyone else: If you're trying to maximize your RP-Net-Worth stack up on Hextech Chests.
Human life has developed in many aspects since the evolution of computer started. The main function of creating new technologies is to make human life easier. New technologies are being invented everyday which creates limitless opportunities for developers to make use of these technologies to serve a specific purpose or task. The Advertising used to be print paper or TV ads. Today mobile and online video creates new ways to contact with customers. Today Ads are kind of the most popular online advertising market in the world and trading ad spaces over the Internet. Currently the social online ads like Facebook and YouTube have some preferred set of users they wish to reach by showing their ads but some cases force the users to watch their Advertising. Ethan Zuckerman wad created the first pop-up advertising on the web was 15-Aug-2014. The pop-up advertising most using in the pc application and browser, and APPs for smartphone. The small internet windows that pop up on your screen can be useful, annoying or dangerous often used by advertisers to get your attention or by viruses to trick you into clicking on them. This guide gives them basic information about your identity. Some dangerous pop-up can leak your private information like your name, number, credit card member, and etc. In this paper we sum up, all these facts contribute to show how danger the pop-up advertising with developing new pop-up advertising to leak the user identity.
This project focuses on a modification of a greedy transition based dependency parser. Typically a Part-Of-Speech (POS) tagger models a probability distribution over all the possible tags for each word in the given sentence and chooses one as its best guess. This is then pass on to the parser which uses this information to build a parse tree. The current state of the art for POS tagging is about 97% word accuracy, which seems high but results in a around 56% sentence accuracy. Small errors at the POS tagging phase can lead to large errors down the NLP pipeline and transition based parsers are particularity sensitive to these types of mistakes. A maximum entropy Markov model was trained as a POS multi-tagger passing more than its 1-best guess to the parser which was thought could make a better decision when committing to a parse for the sentence. This has been shown to give improved accuracy in other parsing approaches. We shown there is a correlation between tagging ambiguity and parsers accuracy and in fact the higher the average tags per word the higher the accuracy.