Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Filters

2022 - 20232

Settings

Subject: search.filters.subject.cryptocurrency

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Unconfirmed Transactions in Cryptocurrency: Reasons, Statistics, and Mitigation
    (Institute of Electrical and Electronics Engineers Inc., 2022) Kallurkar, H.S.; Chandavarkar, B.R.
    Blockchain has emerged to be a pioneer fundamental technology for distributed applications. Not only it is limited to financial sector, but it also has extended in the fields of health & medicare, managing logistics of goods through effective supply chain management etc. Although there are numerous applications of blockchain, cryptocurrencies remains at the top, in terms of popularity and cryptographic security it provides in maintenance of digital assets. Miner(s) in a cryptocurrency is/are an individual/group of individuals who benefit after per-forming Proof-of-Work for validating a transaction. The top two cryptocurrencies according to market cap value are Bitcoin and Ether. Millions of transactions happen on their blockchain on a daily basis, but not all of them result in success. Some are also marked as failed/unconfirmed, even if they are less compared to the confirmed transactions. Some of the reasons for this behavior could be too many transactions present in mempool of miners or insufficient fees is provided as the incentive to the miners of the network. Though the number of transactions that go unconfirmed per day is very small compared to the ones getting confirmed, still the area of failed cryptocurrency transactions remain unexplored. This paper focuses on statistics of failed cryptocurrency transactions, some primary reasons of failure in a cryptocurrency transaction. Furthermore, it also presents existing approaches to minimize the failure of transactions. © 2022 IEEE.
  • No Thumbnail Available
    Item
    Profiling Cryptocurrency Influencers using Few-shot Learning
    (CEUR-WS, 2023) Muslihuddeen, H.; Sathvika, P.; Sankar, S.; Ostwal, S.; Anand Kumar, M.
    This research provides a novel method for identifying cryptocurrency influencers on social media in a low-resource environment. The analysis focuses on English-language Twitter messages and divides influencers into impact categories ranging from minimal to massive. With a maximum of 10 English tweets per user, the dataset consists of 32 people per category. By comparing the suggested approach to two baseline models—Usercharacter Logistic Regression and t5-large (bi-encoders) using zero-shot and label tuning few-shot methods—the proposed system is evaluated using the Macro F1 measure. The findings show that the suggested approach operates effectively in low-resource environments and has the potential to be used to further in-depth studies of influencer profiling. © 2023 Copyright for this paper by its authors.