Digital intervention tools against problematic smartphone usage help users control their consumption on smartphones, for example, by setting a time limit an app. However, today's social media apps offer mix of quasiessential and addictive features in app (e.g., Instagram has following feeds, recommended stories, direct messaging features), which makes it hard to apply uniform logic all uses wit...

Large capacity deep learning models are often prone to a high generalization gap when trained with limited amount of labeled training data. A recent class methods address this problem uses various ways construct new sample by mixing pair (or more) samples. We propose PatchUp, hidden state block-level regularization technique for Convolutional Neural Networks (CNNs), that is applied on selected ...

Hyperspectral image classification methods based on deep learning have led to remarkable achievements in recent years. However, these with outstanding performance are also accompanied by problems such as excessive dependence the number of samples, poor model generalization, and time-consuming training. Additionally, previous patch-level feature extraction some limitations, for instance, non-loc...