152 research outputs found
Sample Mixed-Based Data Augmentation for Domestic Audio Tagging
Audio tagging has attracted increasing attention since last decade and has
various potential applications in many fields. The objective of audio tagging
is to predict the labels of an audio clip. Recently deep learning methods have
been applied to audio tagging and have achieved state-of-the-art performance,
which provides a poor generalization ability on new data. However due to the
limited size of audio tagging data such as DCASE data, the trained models tend
to result in overfitting of the network. Previous data augmentation methods
such as pitch shifting, time stretching and adding background noise do not show
much improvement in audio tagging. In this paper, we explore the sample mixed
data augmentation for the domestic audio tagging task, including mixup,
SamplePairing and extrapolation. We apply a convolutional recurrent neural
network (CRNN) with attention module with log-scaled mel spectrum as a baseline
system. In our experiments, we achieve an state-of-the-art of equal error rate
(EER) of 0.10 on DCASE 2016 task4 dataset with mixup approach, outperforming
the baseline system without data augmentation.Comment: submitted to the workshop of Detection and Classification of Acoustic
Scenes and Events 2018 (DCASE 2018), 19-20 November 2018, Surrey, U
Pull request latency explained:an empirical overview
Pull request latency evaluation is an essential application of effort evaluation in the pull-based development scenario. It can help the reviewers sort the pull request queue, remind developers about the review processing time, speed up the review process and accelerate software development. There is a lack of work that systematically organizes the factors that affect pull request latency. Also, there is no related work discussing the differences and variations in characteristics in different scenarios and contexts. In this paper, we collected relevant factors through a literature review approach. Then we assessed their relative importance in five scenarios and six different contexts using the mixed-effects linear regression model. The most important factors differ in different scenarios. The length of the description is most important when pull requests are submitted. The existence of comments is most important when closing pull requests, using CI tools, and when the contributor and the integrator are different. When there exist comments, the latency of the first comment is the most important. Meanwhile, the influence of factors may change in different contexts. For example, the number of commits in a pull request has a more significant impact on pull request latency when closing than submitting due to changes in contributions brought about by the review process. Both human and bot comments are positively correlated with pull request latency. In contrast, the bot’s first comments are more strongly correlated with latency, but the number of comments is less correlated. Future research and tool implementation needs to consider the impact of different contexts. Researchers can conduct related studies based on our publicly available datasets and replication scripts
Trusted Multi-Scale Classification Framework for Whole Slide Image
Despite remarkable efforts been made, the classification of gigapixels
whole-slide image (WSI) is severely restrained from either the constrained
computing resources for the whole slides, or limited utilizing of the knowledge
from different scales. Moreover, most of the previous attempts lacked of the
ability of uncertainty estimation. Generally, the pathologists often jointly
analyze WSI from the different magnifications. If the pathologists are
uncertain by using single magnification, then they will change the
magnification repeatedly to discover various features of the tissues. Motivated
by the diagnose process of the pathologists, in this paper, we propose a
trusted multi-scale classification framework for the WSI. Leveraging the Vision
Transformer as the backbone for multi branches, our framework can jointly
classification modeling, estimating the uncertainty of each magnification of a
microscope and integrate the evidence from different magnification. Moreover,
to exploit discriminative patches from WSIs and reduce the requirement for
computation resources, we propose a novel patch selection schema using
attention rollout and non-maximum suppression. To empirically investigate the
effectiveness of our approach, empirical experiments are conducted on our WSI
classification tasks, using two benchmark databases. The obtained results
suggest that the trusted framework can significantly improve the WSI
classification performance compared with the state-of-the-art methods
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