Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: IcoRating: A Deep-Learning System for Scam ICO Identification   Cryptocurrencies (or digital tokens, digital currencies, e.g., BTC, ETH, XRP,
NEO) have been rapidly gaining ground in use, value, and understanding among
the public, bringing astonishing profits to investors. Unlike other money and
banking systems, most digital tokens do not require central authorities. Being
decentralized poses significant challenges for credit rating. Most ICOs are
currently not subject to government regulations, which makes a reliable credit
rating system for ICO projects necessary and urgent.
  In this paper, we introduce IcoRating, the first learning--based
cryptocurrency rating system. We exploit natural-language processing techniques
to analyze various aspects of 2,251 digital currencies to date, such as white
paper content, founding teams, Github repositories, websites, etc. Supervised
learning models are used to correlate the life span and the price change of
cryptocurrencies with these features. For the best setting, the proposed system
is able to identify scam ICO projects with 0.83 precision.
  We hope this work will help investors identify scam ICOs and attract more
efforts in automatically evaluating and analyzing ICO projects.

19
The categories of this paper are: eess
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th