Motivated by cryptographic applications, we investigate two machine learning
approaches to modular multiplication: namely circular regression and a
sequence-to-sequence transformer model. The limited success of both methods
demonstrated in our results gives evidence for the hardness of tasks involving
modular multiplication upon which cryptosystems are based.

通过密码应用为动机，我们研究了两种机器学习方法用于模块乘法：即循环回归和序列到序列变换器模型。我们结果中两种方法都取得的有限成功为基于模块乘法的密码系统中任务的困难性提供了证据。

模块乘法的机器学习

Machine learning for modular multiplication

In this paper, a new perspective is suggested for unsupervised Ontology
Matching (OM) or Ontology Alignment (OA) by treating it as a translation task.
Ontologies are represented as graphs, and the translation is performed from a
node in the source ontology graph to a path in the target ontology graph. The
proposed framework, Truveta Mapper (TM), leverages a multi-task
sequence-to-sequence transformer model to perform alignment across multiple
ontologies in a zero-shot, unified and end-to-end manner. Multi-tasking enables
the model to implicitly learn the relationship between different ontologies via
transfer-learning without requiring any explicit cross-ontology manually
labeled data. This also enables the formulated framework to outperform existing
solutions for both runtime latency and alignment quality. The model is
pre-trained and fine-tuned only on publicly available text corpus and
inner-ontologies data. The proposed solution outperforms state-of-the-art
approaches, Edit-Similarity, LogMap, AML, BERTMap, and the recently presented
new OM frameworks in Ontology Alignment Evaluation Initiative (OAEI22), offers
log-linear complexity in contrast to quadratic in the existing end-to-end
methods, and overall makes the OM task efficient and more straightforward
without much post-processing involving mapping extension or mapping repair.

本文提出了一种新的视角，将无监督本体匹配（OM）或本体对齐（OA）视为翻译任务，利用多任务序列到序列 Transformer 模型在多个本体之间执行对齐，在不需要显式交叉本体手动标记数据的情况下，隐式学习不同本体之间的关系，提出的解决方案优于现有解决方案，使 OM 任务更加高效而简单。