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To automate analog-to-digital conversion of chemical structure diagrams in scientific research articles, several software systems have been developed.
For RAs, architectural decisions are even more important than in a single software system since, owing to systematic reuse, an inadequate design decision could be propagated to several software systems (Montagud et al. [2011]).
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For example, in most publishing houses, editors feel the necessity to learn how to use several different software systems to process either text or pictures.
In order to remotely operate these devices safely, a supervisory system composed of several software sub-systems is needed.
Similar to a configuration model with a few configurations produce several variants of software systems in [2, 3], a feature model that consists of few features can produce several numbers of configurations.
The prototype implementation of the robustness testing environment based on the proposed approach is experimentally discussed and illustrated by industrial case studies based on several avionics-embedded software systems.
Surprisingly, we find convergent evolution in several key features of software systems, indicating that strong constraints are at work.
Rather, this paper will focus on the paradigms involved in the production of RAMP software systems through several case studies.
This paper will focus on the paradigms involved in the production of RAMP software systems through several case studies.
This paper demonstrates the correctness and scalability of our approach on several, large-scale third-party software systems.
Compared with existing software programs such as OSRA, Kekule, and CLiDE, our results indicate that ChemReader outperforms other software systems on several sets of sample images from diverse sources in terms of the rate of correct outputs and the accuracy on extracting molecular substructure patterns.
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