international conference

We Are Launching our Own NFT! Characterizing Fashion NFT Transactions-Preliminary Results

Paper presented @BRAINS2023, conference held in Paris (October 2023).

Abstract. Blockchain technology and Non Fungible Tokens (NFTs) have been a hot topic for several years now, as proven by the multitude of brands launching their own NFT projects. In this paper, we will consider some popular fashion NFT collections, namely: adidas Originals Into the Metaverse, AMBUSH OFFICIAL POW! REBOOT, Azuki x AMBUSH IKZ, CULT & RAIN – The Genesis Collection, Dolce& Gabbana: DGFamily, Dolce& Gabbana: DGFamily Glass Box, Chito x Givenchy NFT, MUGLER – We Are All Angel, RTFKT x Nike Dunk Genesis CRYPTOKICK, Prada Timecapsule. First, we will analyze and examine if we can find salient characteristics of transactions pertaining to these collections. Second, we will attempt to propose a first taxonomy of fashion NFT transactions. From the results, we can state that most transactions occur at the NFT launch and that they belong to the Memberships category. Secondly, the results show that we can propose a taxonomy of four transaction groups or clusters. The findings can have practical implications for both researchers and practitioners, indeed the results: (i) can be a stepping stone for future research on (fashion) NFTs, (ii) can help practitioners analyze transactions using our preliminary taxonomy.

To Fork or Not To Fork? Bitcoin Forks’ Success Analysis Using Twitter Data: Preliminary Results

Short paper presented @BRAINS2022, conference held in Paris.

Abstract. Bitcoin is a decentralized cryptocurrency. It is open-source; its design is public, nobody owns or controls it and everyone can take part. And Bitcoin, just like any other open-source projects, has been subject to forks.

In this paper, we will consider some popular Bitcoin forks and we will examine if we can find a link between the value of the fork (i.e. its price) and the overall sentiment of the fork based on Twitter data. The forks we consider here are: Litecoin (LTC), Bitcoin Cash (BCH), Bitcoin Gold (BTG), Bitcoin Diamond (BCD), Bitcoin Private (BTCP), Bitcoin Atom (BCA), and Bitcoin SV (BSV). From the results, we can state that, for most cryptocurrencies studied here, the number of tweets follows the price trend more closely than the sentiment evolution does.

The findings can have practical implications for both researchers and practitioners, indeed the results: (i) can be a stepping stone for future research on hard forks, (ii) can help practitioners identify the relevant indicators for hard forks price evolution.

On the Popularity of Non-Fungible Tokens: Preliminary Results

Abstract. Blockchain technology supports digital assets, which can take the form of cryptocurrencies and tokens. Tokens are usually created on top of the blockchain platform, using smart contracts. Two main categories of tokens exist: Fungible Tokens and Non-Fungible Tokens (NFTs). Here, we focus on NFTs and propose a correlation analysis between various NFTs’ characteristics and the popularity of the NFTs. The results can have practical implications for both designers and users.

BRAINS 2020

The 2nd Conference on Blockchain Research & Applications for Innovative Networks and Services took place in September 2020 (BRAINS2020) was supposed to be held in Paris, but unfortunately due to the COVID-2019 pandemic, the conference had to be held virtually.

In my very first poster session, I’ve presented the poster you can see above which focuses on the prediction of gas for transactions on Ethereum. Obviously, the conditions were not ideal for exchanges with the other attendees, but the organizers did a great job in enabling interactions despite the virtual setting.

Abstract. The author uses data about transactions onEthereum as sources for studying the relationship between thehistoric of transactions for a given address and the amountof gas consumed for a transaction. The author combines dataabout transactions, and blocks to predict the gas usage for atransaction. Specifically, how much gas will be consumed for the next transaction, given the initiator’s transaction history. The results demonstrate the value of considering the transactionhistory for gas usage predictions.

Feel free to leave any comment or question you might have regarding this poster in itself or the topic of the poster.