• Publications
  • Influence
Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery
TLDR
We developed a strategy for the semi-automatic optimization of object-based classification, which involves an area-based accuracy assessment that analyzes the relationship between scale and the training set size. Expand
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Machine Learning Testing: Survey, Landscapes and Horizons
TLDR
This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research. Expand
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GC3-biased gene domains in mammalian genomes
TLDR
We used relative GC3 bias values to compare the strength of GC3 biases of genes in human and mouse. Expand
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QR Decomposition-Based Matrix Inversion for High Performance Embedded MIMO Receivers
TLDR
We present a matrix inversion approach based on modified squared Givens rotations (MSGR). Expand
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Combinatorial Testing for Deep Learning Systems
TLDR
In this paper, we perform an exploratory study of CT on DL systems and propose a set of coverage criteria for DL systems. Expand
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Characterising Deprecated Android APIs
TLDR
We present a research-based prototype tool called CDA and apply it to different revisions of the Android framework code for characterising deprecated APIs. Expand
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Multisource Coordination Energy Management Strategy Based on SOC Consensus for a PEMFC–Battery–Supercapacitor Hybrid Tramway
TLDR
A multisource coordination energy management strategy based on self-convergence droop control is proposed for a large-scale and high-power hybrid tramway. Expand
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Practical Fault Attack on Deep Neural Networks
TLDR
We introduce the first study of leveraging physical fault injection attacks on Deep Neural Networks (DNNs), by using laser injection technique on embedded systems. Expand
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Modeling and prediction of lung tumor motion for robotic assisted radiotherapy
TLDR
This paper is concerned with modeling and prediction of lung tumor motion for a new adaptive tumor tracking system in radiotherapy. Expand
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DeepCruiser: Automated Guided Testing for Stateful Deep Learning Systems
TLDR
Deep learning (DL) defines a data-driven programming paradigm that automatically composes the system decision logic from the training data. Expand
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