Evaluating Expert Estimators Based on Elicited Competences

Utilization of expert effort estimation approach shows promising results when it is applied to software development process. It is based on judgment and decision making process and due to comparative advantages extensively used especially in situations when classic models cannot be accounted for. This becomes even more accentuated in today’s highly dynamical project environment. Confronted with these facts companies are placing ever greater focus on their employees, specifically on their competences. Competences are defined as knowledge, skills and abilities required to perform job assignments. During effort estimation process different underlying expert competences influence the outcome i.e. judgments they express. Special problem here is the elicitation, from an input collection, of those competences that are responsible for accurate estimates. Based on these findings different measures can be taken to enhance estimation process. The approach used in study presented in this paper was targeted at elicitation of expert estimator competences responsible for production of accurate estimates. Based on individual competences scores resulting from performed modeling experts were ranked using weighted scoring method and their performance evaluated. Results confirm that experts with higher scores in competences identified by applied models in general exhibit higher accuracy during estimation process. For the purpose of modeling data mining methods were used, specifically the multilayer perceptron neural network and the classification and regression decision tree algorithms. Among other, applied methods are suitable for the purpose of elicitation as in a sense they mimic the ways human brains operate. Data used in the study was collected from real projects in the company specialized for development of IT solutions in telecom domain. The proposed model, applied methodology for elicitation of expert competences and obtained results give evidence that in future such a model can be used in practice to reduce estimation error and enhance expert effort estimation

Mining Competences of Expert Estimators

This paper reports on a study conducted with intention to identify competences of employees engaged on software development projects that are responsible for reliable effort estimation. Execution of assigned project tasks engages different human characteristics and effort estimation is integral part of development process. Competences are defined as knowledge , skills and abilities required to perform job assignments. As input data we used company internal classification and collection of employee competences together with data sets of task effort estimates from ten projects executed in a department of the company specialized for development of IT solutions in telecom domain. Techniques used for modeling are proven data mining methods, the neural network and decision tree algorithms. Results provided mapping of competences to effort estimates and represent valuable knowledge discovery that can be used in practice for selection and evaluation of expert effort estimators

Complete Model for Automatic Object Detection and Localisation on Aerial Images using Convolutional Neural Networks

In this paper, a novel approach for an automatic object detection and localisation on aerial images is proposed. Proposed model does not use ground control points (GCPs) and consists of three major phases. In the first phase, optimal flight route is planned in order to capture the area of interest and aerial images are acquired using unmanned aerial vehicle (UAV), followed by creating a mosaic of collected images to obtained larger field-of-view panoramic image of the area of interest and using the obtained image mosaic to create georeferenced map. The image mosaic is then also used to detect objects of interest using the approach based on convolutional neural networks

The Effect of Latent Space Dimension on the Quality of Synthesized Human Face Images

In recent years Generative Adversarial Networks (GANs) have achieved remarkable results in the task of realistic image synthesis. Despite their continued success and advances, there still lacks a thorough understanding of how precisely GANs map random latent vectors to realistic-looking images and how the priors set on the latent space affect the learned mapping. In this work, we analyze the effect of the chosen latent dimension on the final quality of synthesized images of human faces and learned data representations. We show that GANs can generate images plausibly even with latent dimensions significantly smaller than the standard dimensions like 100 or 512. Although one might expect that larger latent dimensions encourage the generation of more diverse and enhanced quality images, we show that an increase of latent dimension after some point does not lead to visible improvements in perceptual image quality nor in quantitative estimates of its generalization abilities