Abstract

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Abstract

Abstract

Abstract
Data are growing fast in today's world and great portion of that is in the form of stream. In many situations, data streams are imbalanced making it difficult to use with classical data mining methods. However, mining these special kinds of streams is one of the most attractive research area. In this paper, we propose two algorithms for learning from imbalanced regression data streams. Both methods are based on Chebychev's inequality but in a different way. The first method, under-samples from the frequent target value examples while the second method over-samples the rare and extreme target value examples. This way, the learner will focus in the rare and more difficult cases. We applied our methods to train regression models using two benchmark datasets and two well-known regression algorithms: Perceptron and FIMT-DD. Our obtained results from the simulations indicate the usefulness of our proposed methods.

Abstract
This paper reports on the use of property-based testing for providing feedback to C programming exercises. Test cases are generated automatically from properties specified in a test script; this not only makes it possible to conduct many tests (thus potentially find more mistakes), but also allows simplifying failed tests cases automatically. We present some experimental validation gathered for an introductory C programming course during the fall semester of 2018 that show significant positive correlations between getting feedback during the semester and the student's results in the final exam. We also discuss some limitations regarding feedback for undefined behaviors in the C language. 2012 ACM Subject Classification Social and professional topics ! Student assessment; Software and its engineering ! Software testing and debugging; Software and its engineering ! Domain specific languages.