imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-of-the- art methods can be categorized into 4 groups: (i) under- sampling, (ii) over-sampling, (iii) combination of over- and under-sampling, and (iv) ensemble learning methods. The proposed toolbox depends only on numpy, scipy, and scikit-learn and is distributed under MIT license. Furthermore, it is fully compatible with scikit-learn and is part of the scikit-learn-contrib supported project. Documentation, unit tests as well as integration tests are provided to ease usage and contribution. Source code, binaries, and documentation can be downloaded from github.com/scikit-learn-contrib/imbalanced-learn.

This article reviews the current state of automatic classification methodologies to identify Diabetic Macular Edema (DME) versus normal subjects based on Spectral Domain OCT (SD-OCT) data. Addressing this classification problem has valuable interest since early detection and treatment of DME play a major role to prevent eye adverse effects such as blindness. The main contribution of this article is to cover the lack of a public dataset and benchmark suited for classifying DME and normal SD-OCT volumes, providing our own implementation of the most relevant methodologies in the literature. Subsequently, 6 different methods were implemented and evaluated using this common benchmark and dataset to produce reliable comparison.

This paper reports on emotions felt by users manipulating a computationally and acoustically augmented artifact. Prior studies have highlighted systematic relationships between acoustic features and emotions felt when individuals are passively listening to sounds. However, during interaction with real or computationally augmented artifacts, acoustic feedback results from users' active manipulation of the artifact. In such a setting, both sound and manipulation can contribute to the emotions that are elicited. We report on a set of experimental studies that examined the respective roles of sound and manipulation in eliciting emotions from users. The results show that, while the difficulty of the manipulation task predominated, the acoustical qualities of the sounds also influenced the feelings reported by participants. When the sounds were embedded in an interface, their pleasantness primarily influenced the valence of the users' feelings. However, the results also suggested that pleasant sounds made the task slightly easier, and left the users feeling more in control. The results of these studies provide guidelines for the measurement and design of affective aspects of sound in computationally augmented artifacts and interfaces.