16,010 research outputs found
Automated Fixing of Programs with Contracts
This paper describes AutoFix, an automatic debugging technique that can fix
faults in general-purpose software. To provide high-quality fix suggestions and
to enable automation of the whole debugging process, AutoFix relies on the
presence of simple specification elements in the form of contracts (such as
pre- and postconditions). Using contracts enhances the precision of dynamic
analysis techniques for fault detection and localization, and for validating
fixes. The only required user input to the AutoFix supporting tool is then a
faulty program annotated with contracts; the tool produces a collection of
validated fixes for the fault ranked according to an estimate of their
suitability.
In an extensive experimental evaluation, we applied AutoFix to over 200
faults in four code bases of different maturity and quality (of implementation
and of contracts). AutoFix successfully fixed 42% of the faults, producing, in
the majority of cases, corrections of quality comparable to those competent
programmers would write; the used computational resources were modest, with an
average time per fix below 20 minutes on commodity hardware. These figures
compare favorably to the state of the art in automated program fixing, and
demonstrate that the AutoFix approach is successfully applicable to reduce the
debugging burden in real-world scenarios.Comment: Minor changes after proofreadin
Managing big data experiments on smartphones
The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. Next generation smartphone applications are expected to be much larger-scale and complex, demanding that these undergo evaluation and testing under different real-world datasets, devices and conditions. In this paper, we present an architecture for managing such large-scale data management experiments on real smartphones. We particularly present the building blocks of our architecture that encompassed smartphone sensor data collected by the crowd and organized in our big data repository. The given datasets can then be replayed on our testbed comprising of real and simulated smartphones accessible to developers through a web-based interface. We present the applicability of our architecture through a case study that involves the evaluation of individual components that are part of a complex indoor positioning system for smartphones, coined Anyplace, which we have developed over the years. The given study shows how our architecture allows us to derive novel insights into the performance of our algorithms and applications, by simplifying the management of large-scale data on smartphones
Culture and E-Learning: Automatic Detection of a Users’ Culture from Survey Data
Knowledge about the culture of a user is especially important for the design
of e-learning applications. In the experiment reported here, questionnaire
data was used to build machine learning models to automatically predict the
culture of a user. This work can be applied to automatic culture detection
and subsequently to the adaptation of user interfaces in e-learning
Genetic Programming for Smart Phone Personalisation
Personalisation in smart phones requires adaptability to dynamic context
based on user mobility, application usage and sensor inputs. Current
personalisation approaches, which rely on static logic that is developed a
priori, do not provide sufficient adaptability to dynamic and unexpected
context. This paper proposes genetic programming (GP), which can evolve program
logic in realtime, as an online learning method to deal with the highly dynamic
context in smart phone personalisation. We introduce the concept of
collaborative smart phone personalisation through the GP Island Model, in order
to exploit shared context among co-located phone users and reduce convergence
time. We implement these concepts on real smartphones to demonstrate the
capability of personalisation through GP and to explore the benefits of the
Island Model. Our empirical evaluations on two example applications confirm
that the Island Model can reduce convergence time by up to two-thirds over
standalone GP personalisation.Comment: 43 pages, 11 figure
WordSup: Exploiting Word Annotations for Character based Text Detection
Imagery texts are usually organized as a hierarchy of several visual
elements, i.e. characters, words, text lines and text blocks. Among these
elements, character is the most basic one for various languages such as
Western, Chinese, Japanese, mathematical expression and etc. It is natural and
convenient to construct a common text detection engine based on character
detectors. However, training character detectors requires a vast of location
annotated characters, which are expensive to obtain. Actually, the existing
real text datasets are mostly annotated in word or line level. To remedy this
dilemma, we propose a weakly supervised framework that can utilize word
annotations, either in tight quadrangles or the more loose bounding boxes, for
character detector training. When applied in scene text detection, we are thus
able to train a robust character detector by exploiting word annotations in the
rich large-scale real scene text datasets, e.g. ICDAR15 and COCO-text. The
character detector acts as a key role in the pipeline of our text detection
engine. It achieves the state-of-the-art performance on several challenging
scene text detection benchmarks. We also demonstrate the flexibility of our
pipeline by various scenarios, including deformed text detection and math
expression recognition.Comment: 2017 International Conference on Computer Visio
Augmented reality usage for prototyping speed up
The first part of the article describes our approach for solution of this
problem by means of Augmented Reality. The merging of the real world model and
digital objects allows streamline the work with the model and speed up the
whole production phase significantly. The main advantage of augmented reality
is the possibility of direct manipulation with the scene using a portable
digital camera. Also adding digital objects into the scene could be done using
identification markers placed on the surface of the model. Therefore it is not
necessary to work with special input devices and lose the contact with the real
world model. Adjustments are done directly on the model. The key problem of
outlined solution is the ability of identification of an object within the
camera picture and its replacement with the digital object. The second part of
the article is focused especially on the identification of exact position and
orientation of the marker within the picture. The identification marker is
generalized into the triple of points which represents a general plane in
space. There is discussed the space identification of these points and the
description of representation of their position and orientation be means of
transformation matrix. This matrix is used for rendering of the graphical
objects (e. g. in OpenGL and Direct3D).Comment: Keywords: augmented reality, prototyping, pose estimation,
transformation matri
Driver Distraction Identification with an Ensemble of Convolutional Neural Networks
The World Health Organization (WHO) reported 1.25 million deaths yearly due
to road traffic accidents worldwide and the number has been continuously
increasing over the last few years. Nearly fifth of these accidents are caused
by distracted drivers. Existing work of distracted driver detection is
concerned with a small set of distractions (mostly, cell phone usage).
Unreliable ad-hoc methods are often used.In this paper, we present the first
publicly available dataset for driver distraction identification with more
distraction postures than existing alternatives. In addition, we propose a
reliable deep learning-based solution that achieves a 90% accuracy. The system
consists of a genetically-weighted ensemble of convolutional neural networks,
we show that a weighted ensemble of classifiers using a genetic algorithm
yields in a better classification confidence. We also study the effect of
different visual elements in distraction detection by means of face and hand
localizations, and skin segmentation. Finally, we present a thinned version of
our ensemble that could achieve 84.64% classification accuracy and operate in a
real-time environment.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949
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