476 research outputs found
Network-clustered multi-modal bug localization
Developers often spend much effort and resources to debug a program. To help
the developers debug, numerous information retrieval (IR)-based and
spectrum-based bug localization techniques have been devised. IR-based
techniques process textual information in bug reports, while spectrum-based
techniques process program spectra (i.e., a record of which program elements
are executed for each test case). While both techniques ultimately generate a
ranked list of program elements that likely contain a bug, they only consider
one source of information--either bug reports or program spectra--which is not
optimal. In light of this deficiency, this paper presents a new approach dubbed
Network-clustered Multi-modal Bug Localization (NetML), which utilizes
multi-modal information from both bug reports and program spectra to localize
bugs. NetML facilitates an effective bug localization by carrying out a joint
optimization of bug localization error and clustering of both bug reports and
program elements (i.e., methods). The clustering is achieved through the
incorporation of network Lasso regularization, which incentivizes the model
parameters of similar bug reports and similar program elements to be close
together. To estimate the model parameters of both bug reports and methods,
NetML employs an adaptive learning procedure based on Newton method that
updates the parameters on a per-feature basis. Extensive experiments on 355
real bugs from seven software systems have been conducted to benchmark NetML
against various state-of-the-art localization methods. The results show that
NetML surpasses the best-performing baseline by 31.82%, 22.35%, 19.72%, and
19.24%, in terms of the number of bugs successfully localized when a developer
inspects the top 1, 5, and 10 methods and Mean Average Precision (MAP),
respectively.Comment: IEEE Transactions on Software Engineerin
Role of the particle size polydispersity in the electrical conductivity of carbon nanotube-epoxy composites
Carbon nanotubes (CTNs) with large aspect-ratios are extensively used to
establish electrical connectedness in polymer melts at very low CNT loadings.
However, the CNT size polydispersity and the quality of the dispersion are
still not fully understood factors that can substantially alter the desired
characteristics of CNT nanocomposites. Here we demonstrate that the electrical
conductivity of polydisperse CNT-epoxy composites with purposely-tailored
distributions of the nanotube length L is a quasiuniversal function of the
first moment of L. This finding challenges the current understanding that the
conductivity depends upon higher moments of the CNT length. We explain the
observed quasiuniversality by a combined effect between the particle size
polydispersity and clustering. This mechanism can be exploited to achieve
controlled tuning of the electrical transport in general CNT nanocomposites.Comment: 9 pages, 5 figure
JITO: A tool for just-in-time defect identification and localization
Australian Research Counci
Minet Magnetic Indoor Localization
Indoor localization is a modern problem of computer science that has no unified solution, as there are significant trade-offs involved with every technique. Magnetic localization, though less popular than WiFi signal based localization, is a sub-field that is rooted in infrastructure-free design, which can allow universal setup. Magnetic localization is also often paired with probabilistic programming, which provides a powerful method of estimation, given a limited understanding of the environment. This thesis presents Minet, which is a particle filter based localization system using the Earth\u27s geomagnetic field. It explores the novel idea of state space limitation as a method of optimizing a particle filter, by limiting the scope of possibilities the filter has to predict. Minet is also built as a distributed model, which can be easily modified to integrate new technologies. Minet showed promising results, but ultimately fell short of its accuracy goal. Minet had some inconsistencies that led to these accuracy issues, but these issues have been diagnosed and can be fixed in future updates. Finally, potential improvements of Minet\u27s base components are discussed, along with how different technologies such as a Deep Learning model can be implemented to improve performance
A Comprehensive Review on Autonomous Navigation
The field of autonomous mobile robots has undergone dramatic advancements
over the past decades. Despite achieving important milestones, several
challenges are yet to be addressed. Aggregating the achievements of the robotic
community as survey papers is vital to keep the track of current
state-of-the-art and the challenges that must be tackled in the future. This
paper tries to provide a comprehensive review of autonomous mobile robots
covering topics such as sensor types, mobile robot platforms, simulation tools,
path planning and following, sensor fusion methods, obstacle avoidance, and
SLAM. The urge to present a survey paper is twofold. First, autonomous
navigation field evolves fast so writing survey papers regularly is crucial to
keep the research community well-aware of the current status of this field.
Second, deep learning methods have revolutionized many fields including
autonomous navigation. Therefore, it is necessary to give an appropriate
treatment of the role of deep learning in autonomous navigation as well which
is covered in this paper. Future works and research gaps will also be
discussed
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis
Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model's performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field
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