4,641 research outputs found
Automating Requirements Traceability: Two Decades of Learning from KDD
This paper summarizes our experience with using Knowledge Discovery in Data
(KDD) methodology for automated requirements tracing, and discusses our
insights.Comment: The work of the second author has been supported in part by NSF
grants CCF-1511117 and CICI 1642134; 4 pages; in Proceedings of IEEE
Requirements Engineering 201
Numerical Key Performance Indicators for the Validation of PHM Health Indicators with Application to a Hydraulic Actuation System
In order to perform Prognostic and Health Management (PHM) of a given system, it is necessary to define some relevant variables sensitive to the different degradation modes of the system. Those variables are named Health Indicators (HI) and they are the keystone of PHM. However, they are subject to a lot of uncertainties when computed in real time and the stochastic nature of PHM makes it hard to evaluate the efficiency of a HI set before the extraction algorithm is implemented. This document introduces Numerical Key Performance Indicators (NKPI) for the validation of HI computed only from data provided by numerical models in the upstream stages of a PHM system development process. In order to match as good as possible the reality, the multiple sources of uncertainties are quantified and propagated into the model. After having introduced the issue of uncertain systems modeling, the different NKPI are defined and eventually an application is performed on a hydraulic actuation system of an aircraft engine
An open dataset for research on audio field recording archives: freefield1010
We introduce a free and open dataset of 7690 audio clips sampled from the
field-recording tag in the Freesound audio archive. The dataset is designed for
use in research related to data mining in audio archives of field recordings /
soundscapes. Audio is standardised, and audio and metadata are Creative Commons
licensed. We describe the data preparation process, characterise the dataset
descriptively, and illustrate its use through an auto-tagging experiment
PCA 4 DCA: The Application Of Principal Component Analysis To The Dendritic Cell Algorithm
As one of the newest members in the field of artificial immune systems (AIS),
the Dendritic Cell Algorithm (DCA) is based on behavioural models of natural
dendritic cells (DCs). Unlike other AIS, the DCA does not rely on training
data, instead domain or expert knowledge is required to predetermine the
mapping between input signals from a particular instance to the three
categories used by the DCA. This data preprocessing phase has received the
criticism of having manually over-?tted the data to the algorithm, which is
undesirable. Therefore, in this paper we have attempted to ascertain if it is
possible to use principal component analysis (PCA) techniques to automatically
categorise input data while still generating useful and accurate classication
results. The integrated system is tested with a biometrics dataset for the
stress recognition of automobile drivers. The experimental results have shown
the application of PCA to the DCA for the purpose of automated data
preprocessing is successful.Comment: 6 pages, 4 figures, 3 tables, (UKCI 2009
Tversky loss function for image segmentation using 3D fully convolutional deep networks
Fully convolutional deep neural networks carry out excellent potential for
fast and accurate image segmentation. One of the main challenges in training
these networks is data imbalance, which is particularly problematic in medical
imaging applications such as lesion segmentation where the number of lesion
voxels is often much lower than the number of non-lesion voxels. Training with
unbalanced data can lead to predictions that are severely biased towards high
precision but low recall (sensitivity), which is undesired especially in
medical applications where false negatives are much less tolerable than false
positives. Several methods have been proposed to deal with this problem
including balanced sampling, two step training, sample re-weighting, and
similarity loss functions. In this paper, we propose a generalized loss
function based on the Tversky index to address the issue of data imbalance and
achieve much better trade-off between precision and recall in training 3D fully
convolutional deep neural networks. Experimental results in multiple sclerosis
lesion segmentation on magnetic resonance images show improved F2 score, Dice
coefficient, and the area under the precision-recall curve in test data. Based
on these results we suggest Tversky loss function as a generalized framework to
effectively train deep neural networks
Dynamic Mutant Subsumption Analysis using LittleDarwin
Many academic studies in the field of software testing rely on mutation
testing to use as their comparison criteria. However, recent studies have shown
that redundant mutants have a significant effect on the accuracy of their
results. One solution to this problem is to use mutant subsumption to detect
redundant mutants. Therefore, in order to facilitate research in this field, a
mutation testing tool that is capable of detecting redundant mutants is needed.
In this paper, we describe how we improved our tool, LittleDarwin, to fulfill
this requirement
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