Type inference in flexible model-driven engineering using classification algorithms

Flexible or bottom-up model-driven engineering (MDE) is an emerging approach to domain and systems modelling. Domain experts, who have detailed domain knowledge, typically lack the technical expertise to transfer this knowledge using traditional MDE tools. Flexible MDE approaches tackle this challenge by promoting the use of simple drawing tools to increase the involvement of domain experts in the language definition process. In such approaches, no metamodel is created upfront, but instead the process starts with the definition of example models that will be used to infer the metamodel. Pre-defined metamodels created by MDE experts may miss important concepts of the domain and thus restrict their expressiveness. However, the lack of a metamodel, that encodes the semantics of conforming models has some drawbacks, among others that of having models with elements that are unintentionally left untyped. In this paper, we propose the use of classification algorithms to help with the inference of such untyped elements. We evaluate the proposed approach in a number of random generated example models from various domains. The correct type prediction varies from 23 to 100% depending on the domain, the proportion of elements that were left untyped and the prediction algorithm used

Editorial for topical collections on emerging trends in artificial intelligence and machine learning

It gives us a great pleasure to introduce this special issue focused on the recent advances in various areas of pattern recognition, machine learning and artificial intelligence. We congratulate the authors who contributed successful submissions and thank the reviewers who worked hard on a tight timeframe. As a result of the open call for papers, which was widely disseminated, we received 34 submissions which were judged to be within scope of the special issue. We encouraged contributions on any topic under the broad umbrella of NCAA. In addition, a selection of high-quality manuscripts presented at MedPRAI 2020 has been invited to submit an extended version of their work. Each submission was assigned to one of the guest editors, making sure that any potential conflict of interest is avoided. We then solicited reviews from experts in the field following the standard practices of the journal. Following a rigorous reviewing process, which extended to two or three rounds in some cases, we ultimately accepted 13 papers for publication in this special issue. These reflect both the range of the research in the field today and also the depth of the problems that are being studied. We believe the research presented in this special issue will provide a valuable resource for those working in the field over the coming years. Once again, we thank everyone who contributed to the success of this special issue, both authors and reviewers. We also wish to thank journal staff members for their ongoing support and assistance

A Deep HMM model for multiple keywords spotting in handwritten documents

International audienceIn this paper, we propose a query by string word spotting system able to extract arbitrary key-words in handwritten documents, taking both segmen-tation and recognition decisions at the line level. The system relies on the combination of a HMM line model made of keyword and non-keyword (filler) models, with a deep neural network (DNN) that estimates the state-dependent observation probabilities. Experiments are carried out on RIMES database, an unconstrained hand-written document database that is used for benchmark-ing different handwriting recognition tasks. The ob-tained results show the superiority of the proposed frame-work over the classical GMM-HMM and standard HMM hybrid architectures