Topic modeling comprises a set of machine learning algorithms that allow topics to be extracted from a collection of documents. These algorithms have been widely used in many areas, such as the identification of dominant topics in scientific research. However, works addressing such problems often focus on identifying static topics, providing snapshots that are unable to show how those topics evolve over time. Aiming to close this gap, in this article, we describe an approach for dynamic article set analysis and classification. This is accomplished by querying open data of notable scientific databases via representational state transfers and subsequently enforcing data management practices with a dynamic topic modeling approach on the associated metadata available. As a result, we identify research trends for a given field at specific instants and the referred terminology trends evolution throughout the years. It was possible to detect the associated lexical variation over time in published content, ultimately determining the so-called "hot topics" in arbitrary instants and also how they are correlated to each other.
Quantum computing threatens classical cryptography, leading to the search for stronger alternatives. The cryptographic approach based on lattices is considered as a viable option. Schemes with that approach use Gaussian sampling, a design which brings along two concerns: efficiency and information leakage. This work adresses those concerns in the RLWE formulation, for digital signatures. Efficiency mitigation uses the central limit theorem, and the Walsh–Hadamard transform, whereas the information leakage risk is reduced via isochronous implementation. Up to 223 samples are queried, and the results are compared against those of a cumulative distribution table sampler. Statistical metrics show the suitability of the presented sampler in a number of contexts.
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