3 research outputs found
Non-Negative Paratuck2 Tensor Decomposition Combined to LSTM Network for Smart Contracts Profiling
Background: Past few months have seen the rise of blockchain and cryptocurrencies. In this context, the Ethereum platform, an open-source blockchain-based platform using Ether cryptocurrency, has been designed to use smart contracts programs. These are self-executing blockchain contracts. Due to their high volume of transactions, analyzing their behavior is very challenging. We address this challenge in our paper.
Methods: We develop for this purpose an innovative approach based on the non-negative tensor
decomposition Paratuck2 combined with long short-term memory. The objective is to assess if predictive analysis can forecast smart contracts activities over time. Three statistical tests are performed on the predictive analytics, the mean absolute percentage error, the mean directional accuracy and the Jaccard distance.
Results: Among dozens of GB of transactions, the Paratuck2 tensor decomposition allows asymmetric modeling of the smart contracts. Furthermore, it highlights time dependent latent groups. The latent activities are modeled by the long short term memory network for predictive analytics. The highly accurate predictions underline the accuracy of the method and show that blockchain activities are not pure randomness.
Conclusion: Herein, we are able to detect the most active contracts, and predict their behavior. In the context of future regulations, our approach opens new perspective for monitoring blockchain activities
Overview of Constrained PARAFAC Models
In this paper, we present an overview of constrained PARAFAC models where the
constraints model linear dependencies among columns of the factor matrices of
the tensor decomposition, or alternatively, the pattern of interactions between
different modes of the tensor which are captured by the equivalent core tensor.
Some tensor prerequisites with a particular emphasis on mode combination using
Kronecker products of canonical vectors that makes easier matricization
operations, are first introduced. This Kronecker product based approach is also
formulated in terms of the index notation, which provides an original and
concise formalism for both matricizing tensors and writing tensor models. Then,
after a brief reminder of PARAFAC and Tucker models, two families of
constrained tensor models, the co-called PARALIND/CONFAC and PARATUCK models,
are described in a unified framework, for order tensors. New tensor
models, called nested Tucker models and block PARALIND/CONFAC models, are also
introduced. A link between PARATUCK models and constrained PARAFAC models is
then established. Finally, new uniqueness properties of PARATUCK models are
deduced from sufficient conditions for essential uniqueness of their associated
constrained PARAFAC models