186,342 research outputs found
Semantics-based Automated Web Testing
We present TAO, a software testing tool performing automated test and oracle
generation based on a semantic approach. TAO entangles grammar-based test
generation with automated semantics evaluation using a denotational semantics
framework. We show how TAO can be incorporated with the Selenium automation
tool for automated web testing, and how TAO can be further extended to support
automated delta debugging, where a failing web test script can be
systematically reduced based on grammar-directed strategies. A real-life
parking website is adopted throughout the paper to demonstrate the effectivity
of our semantics-based web testing approach.Comment: In Proceedings WWV 2015, arXiv:1508.0338
Simplification and cost reduction of visual corona tests
Peer ReviewedPostprint (author's final draft
Differentially Testing Soundness and Precision of Program Analyzers
In the last decades, numerous program analyzers have been developed both by
academia and industry. Despite their abundance however, there is currently no
systematic way of comparing the effectiveness of different analyzers on
arbitrary code. In this paper, we present the first automated technique for
differentially testing soundness and precision of program analyzers. We used
our technique to compare six mature, state-of-the art analyzers on tens of
thousands of automatically generated benchmarks. Our technique detected
soundness and precision issues in most analyzers, and we evaluated the
implications of these issues to both designers and users of program analyzers
A Winnow-Based Approach to Context-Sensitive Spelling Correction
A large class of machine-learning problems in natural language require the
characterization of linguistic context. Two characteristic properties of such
problems are that their feature space is of very high dimensionality, and their
target concepts refer to only a small subset of the features in the space.
Under such conditions, multiplicative weight-update algorithms such as Winnow
have been shown to have exceptionally good theoretical properties. We present
an algorithm combining variants of Winnow and weighted-majority voting, and
apply it to a problem in the aforementioned class: context-sensitive spelling
correction. This is the task of fixing spelling errors that happen to result in
valid words, such as substituting "to" for "too", "casual" for "causal", etc.
We evaluate our algorithm, WinSpell, by comparing it against BaySpell, a
statistics-based method representing the state of the art for this task. We
find: (1) When run with a full (unpruned) set of features, WinSpell achieves
accuracies significantly higher than BaySpell was able to achieve in either the
pruned or unpruned condition; (2) When compared with other systems in the
literature, WinSpell exhibits the highest performance; (3) The primary reason
that WinSpell outperforms BaySpell is that WinSpell learns a better linear
separator; (4) When run on a test set drawn from a different corpus than the
training set was drawn from, WinSpell is better able than BaySpell to adapt,
using a strategy we will present that combines supervised learning on the
training set with unsupervised learning on the (noisy) test set.Comment: To appear in Machine Learning, Special Issue on Natural Language
Learning, 1999. 25 page
A Purely Functional Computer Algebra System Embedded in Haskell
We demonstrate how methods in Functional Programming can be used to implement
a computer algebra system. As a proof-of-concept, we present the
computational-algebra package. It is a computer algebra system implemented as
an embedded domain-specific language in Haskell, a purely functional
programming language. Utilising methods in functional programming and prominent
features of Haskell, this library achieves safety, composability, and
correctness at the same time. To demonstrate the advantages of our approach, we
have implemented advanced Gr\"{o}bner basis algorithms, such as Faug\`{e}re's
and , in a composable way.Comment: 16 pages, Accepted to CASC 201
Using Multiple Fidelity Numerical Models for Floating Offshore Wind Turbine Advanced Control Design
This paper summarises the tuning process of the Aerodynamic Platform Stabiliser control loop and its performance with Floating Offshore Wind Turbine model. Simplified Low-Order Wind turbine numerical models have been used for the system identification and control tuning process. Denmark Technical University's 10 MW wind turbine model mounted on the TripleSpar platform concept was used for this study. Time-domain simulations were carried out in a fully coupled non-linear aero-hydro-elastic simulation tool FAST, in which wind and wave disturbances were modelled. This testing yielded significant improvements in the overall Floating Offshore Wind Turbine performance and load reduction, validating the control technique presented in this work.This work was partially funded by the Spanish Ministry of Economy and Competitiveness through the research project DPI2017-82930-C2-2-R
End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification
As shown in computer vision, the power of deep learning lies in automatically
learning relevant and powerful features for any perdition task, which is made
possible through end-to-end architectures. However, deep learning approaches
applied for classifying medical images do not adhere to this architecture as
they rely on several pre- and post-processing steps. This shortcoming can be
explained by the relatively small number of available labeled subjects, the
high dimensionality of neuroimaging data, and difficulties in interpreting the
results of deep learning methods. In this paper, we propose a simple 3D
Convolutional Neural Networks and exploit its model parameters to tailor the
end-to-end architecture for the diagnosis of Alzheimer's disease (AD). Our
model can diagnose AD with an accuracy of 94.1\% on the popular ADNI dataset
using only MRI data, which outperforms the previous state-of-the-art. Based on
the learned model, we identify the disease biomarkers, the results of which
were in accordance with the literature. We further transfer the learned model
to diagnose mild cognitive impairment (MCI), the prodromal stage of AD, which
yield better results compared to other methods
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