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Mining software repositories to extract meaningful information from them has become an important topic in software engineering. This paper presents our study to mine a very large dataset consisting of over three million software repositories across many version control systems and create derived data for future studies. Through this study, we propose a method for detecting forks and duplicates in repositories. We also preliminarily investigate the possible correlations between forking patterns, software health and risks, and success indicators.

Kinship verification and kinship retrieval are emerging tasks in computer vision. Kinship verification aims at determining whether two facial images are from related people or not, while kinship retrieval is the task of retrieving possible related facial images to a person from a gallery of images. They introduce unique challenges because of the hidden relations and features that carry inherent characteristics between the facial images. We employ 3 methods, FaceNet, Siamese VGG-Face, and a combination of FaceNet and VGG-Face models as feature extractors, to achieve the 9th standing for kinship verification and the 5th standing for kinship retrieval in the Recognizing Family in The Wild 2020 competition. We then further experimented using StyleGAN2 as another encoder, with no improvement in the result., Will appear as part of RFIW2020 in the Proceedings of 2020 International Conference on Automatic Face and Gesture Recognition (IEEE AMFG)

Recent WiFi-based fall detection systems have drawn much attention due to their advantages over other sensory systems. Various implementations have achieved impressive progress in performance, thanks to machine learning and deep learning techniques. However, many of such high accuracy systems have low reliability as they fail to achieve robustness in unseen environments. To address that, this paper investigates a method of generalization through adversarial data augmentation. Our results show a slight improvement in deep learning-systems in unseen domains, though the performance is not significant., Comment: Will appear in Proceedings of the 54th Annual Conference on Information Sciences and Systems (CISS2020)