Automatic machine learning is a subfield of machine learning that automates the common procedures faced in predictive tasks. The problem of one such procedure is automatic data augmentation, where one desires to enrich the existing data to increase model performance. In relational data repositories, the data is stored in normal form. This causes problems, since joining all tables and subsequently performing feature selection is highly inefficient. This paper provides AFAR, an approach to efficiently and effectively perform automated feature augmentation by ranking candidate joins in a data repository. Additionally, an experimental evaluation that validates the approach’s capabilities, is presented.

Blockchains like Bitcoin are known to be victim of scalability issues. The lack in high throughput and low latency form a great bottleneck to its network. A promis- ing solution are layer 2 protocols, more precisely payment channel networks (PCN). Payment success rates are a common metric in these networks. These rates can be in- creased by tweaking the routing of payments in the network. Local routing is a form of routing that allows payments in such networks to be split over multiple paths to reach its receiver. This significantly increases the rate of payment successes, however there is no trivial way to integrate fees in such protocol. This paper focuses on the integration of fees in local routing protocols by proposing a viable solution. Local Routing Fee Protocol (LRFP) is a protocol designed to extend an existing local routing protocol and is proven to be secure. It is a light addition but works as intended. Proofs on se- curity guarantees and a formal description on the protocol form the main contribution of this paper.