Hierarchical Text Classification poses the difficult challenge of classifying documents into multiple labels organized in a hierarchy.The vast majority of works aimed to address this problem relies on supervised methods which are difficult to implement due to the scarcity of labeled data in many real world applications.This paper focuses on strict Zero-Shot Classification, the setting in which the system lacks both labeled instances and training data.We propose a novel approach that uses a Large Language Model to augment the deepest layer of the labels hierarchy in order to enhance its specificity.We achieve this by generating semantically relevant labels as children connected to the existing branches, creating a deeper taxonomy that better overlaps with the input texts.We leverage the enriched hierarchy to perform Zero-Shot Hierarchical Classification by using the Upward score Propagation technique.We test our method on four public datasets, obtaining new state-of-the art results on three of them.We introduce two cosine similarity-based metrics to quantify the density and granularity of a label taxonomy and we show a strong correlation between the metric values and the classification performance of our method on the datasets.

In this paper, we explore the usage of hierarchical priors to improve learning in contexts where the number of available examples is extremely low.Specifically, we consider a Prototype Learning setting where deep neural networks are used to embed data in hyperspherical geometries.In this scenario, we propose an innovative way to learn the prototypes by combining class separation and hierarchical information.In addition, we introduce a contrastive loss function capable of balancing the exploitation of prototypes through a prototype pruning mechanism.We compare the proposed method with state-of-the-art approaches on two public datasets.This work has been partially supported by the Spoke 1 "FutureHPC & BigData" of ICSC -Centro Nazionale di Ricerca in High-Performance-Computing, Big Data and Quantum Computing, funded by European Union -NextGenerationEU.

Blockchain, the underlying technology of Bitcoin and several other cryptocurrencies, like Ethereum, produces a massive amount of open-access data that can be analyzed, providing important information about the network's activity and its respective token. The on-chain data have extensively been used as input to Machine Learning algorithms for predicting cryptocurrencies' future prices; however, there is a lack of study in predicting the future behaviour of on-chain data. This study aims to show how on-chain data can be used to detect cryptocurrency market regimes, like minimum and maximum, bear and bull market phases, and how forecasting these data can provide an optimal asset allocation for long-term investors.