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PurposeIn this paper, we define the concept of user spectrum and adopt it to classify Ethereum users based on their behavior.Design/methodology/approachGiven a time period, our approach associates each user with a spectrum showing the trend of some behavioral features obtained from a social network-based representation of Ethereum. Each class of users has its own spectrum, obtained by averaging the spectra of its users. In order to evaluate the similarity between the spectrum of a class and the one of a user, we propose a tailored similarity measure obtained by adapting to this context some general measures provided in the past. Finally, we test our approach on a dataset of Ethereum transactions.FindingsWe define a social network-based model to represent Ethereum. We also define a spectrum for a user and a class of users (i.e., token contract, exchange, bancor and uniswap), consisting of suitable multivariate time series. Furthermore, we propose an approach to classify new users. The core of this approach is a metric capable of measuring the similarity degree between the spectrum of a user and the one of a class of users. This metric is obtained by adapting the Eros distance (i.e., Extended Frobenius Norm) to this scenario.Originality/valueThis paper introduces the concept of spectrum of a user and a class of users, which is new for blockchains. Differently from past models, which represented user behavior by means of univariate time series, the user spectrum here proposed exploits multivariate time series. Moreover, this paper shows that the original Eros distance does not return satisfactory results when applied to user and class spectra, and proposes a modified version of it, tailored to the reference scenario, which reaches a very high accuracy. Finally, it adopts spectra and the modified Eros distance to classify Ethereum users based on their past behavior. Currently, no multi-class automatic classification approach tailored to Ethereum exists yet, albeit some single-class ones have been recently proposed. Therefore, the only way to classify users in Ethereum are online services (e.g., Etherscan), where users are classified after a request from them. However, the fraction of users thus classified is low. To address this issue, we present an automatic approach for a multi-class classification of Ethereum users based on their past behavior.

After trundling along listlessly for months in 2020, battered from the effects of a Covid-19 circuit breaker, a faltering economy, and the recent loss of a number of institutions and spaces, the Year of the Ox started off with a bang for Singapore's otherwise enervated visual arts community. The ninth edition of the annual Singapore Art Week (1/22-30), coinciding with the initiation of a nation-wide vaccination program, featured a deluge of exhibitions and events. Highlights included \"Singapore Ceramics Now\" (1/20-2/13), curated by artist Jason Lim, a show that inflected the titular medium with sound, installation, and performativity; \"ON/OFF/ SCREEN\" (1/21-2/18) by the Moving Picture Experiment Group (MPEG) at DECK, which expanded the possibilities of the moving image; and \"Baroque Archipelago\" (1/22-3/7) at Mizuma Gallery, where curator Tan Siuli juxtaposed the syntaxes of fashion and fine art, an increasingly visible trajectory.