There is an increasing awareness of the added value that efficient design of experiments can provide to simulation studies. However, many practitioners lack the proper training required to design, execute and analyze a well-designed simulation experiment. In this study, we present the initial stages of a modeldriven engineering based tool for managing simulation experiments. Underlying our approach are an experiment ontology and a feature model that capture the statistical design of experiments expert knowledge which users might be lacking. In its current state, the tool provides support for the design and execution of simple experiments. Using a web-based interface, the user is guided through an experiment design wizard that produces an experiment model that can be exported as an XML file containing the experiment's description. This XML can be used to synthesize scripts that can be run by a simulator and shared across different platforms.

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The human eye contains valuable information about an individual’s identity and health. Therefore, segmenting the eye into distinct regions is an essential step towards gathering this useful information precisely. The main challenges in segmenting the human eye include low light conditions, reflections on the eye, variations in the eyelid, and head positions that make an eye image hard to segment. For this reason, there is a need for deep neural networks, which are preferred due to their success in segmentation problems. However, deep neural networks need a large amount of manually annotated data to be trained. Manual annotation is a labor-intensive task, and to tackle this problem, we used data augmentation methods to improve synthetic data. In this paper, we detail the exploration of the scenario, which, with limited data, whether performance can be enhanced using similar context data with image augmentation methods. Our training and test set consists of 3D synthetic eye images generated from the UnityEyes application and manually annotated real-life eye images, respectively. We examined the effect of using synthetic eye images with the Deeplabv3+ network in different conditions using image augmentation methods on the synthetic data. According to our experiments, the network trained with processed synthetic images beside real-life images produced better mIoU results than the network, which only trained with real-life images in the Base dataset. We also observed mIoU increase in the test set we created from MICHE II competition images.

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