4,194 research outputs found
Target Directed Event Sequence Generation for Android Applications
Testing is a commonly used approach to ensure the quality of software, of
which model-based testing is a hot topic to test GUI programs such as Android
applications (apps). Existing approaches mainly either dynamically construct a
model that only contains the GUI information, or build a model in the view of
code that may fail to describe the changes of GUI widgets during runtime.
Besides, most of these models do not support back stack that is a particular
mechanism of Android. Therefore, this paper proposes a model LATTE that is
constructed dynamically with consideration of the view information in the
widgets as well as the back stack, to describe the transition between GUI
widgets. We also propose a label set to link the elements of the LATTE model to
program snippets. The user can define a subset of the label set as a target for
the testing requirements that need to cover some specific parts of the code. To
avoid the state explosion problem during model construction, we introduce a
definition "state similarity" to balance the model accuracy and analysis cost.
Based on this model, a target directed test generation method is presented to
generate event sequences to effectively cover the target. The experiments on
several real-world apps indicate that the generated test cases based on LATTE
can reach a high coverage, and with the model we can generate the event
sequences to cover a given target with short event sequences
Identifying DNA motifs based on match and mismatch alignment information
The conventional way of identifying DNA motifs, solely based on match
alignment information, is susceptible to a high number of spurious sites. A
novel scoring system has been introduced by taking both match and mismatch
alignment information into account. The mismatch alignment information is
useful to remove spurious sites encountered in DNA motif searching. As an
example, a correct TATA box site in Homo sapiens H4/g gene has successfully
been identified based on match and mismatch alignment information
Helicity hardens the gas
A screw generally works better than a nail, or a complicated rope knot better
than a simple one, in fastening solid matter, but a gas is more tameless.
However, a flow itself has a physical quantity, helicity, measuring the
screwing strength of the velocity field and the degree of the knottedness of
the vorticity ropes. It is shown that helicity favors the partition of energy
to the vortical modes, compared to others such as the dilatation and pressure
modes of turbulence; that is, helicity stiffens the flow, with nontrivial
implications for aerodynamics, such as aeroacoustics, and conducting fluids,
among others
LINE: Large-scale Information Network Embedding
This paper studies the problem of embedding very large information networks
into low-dimensional vector spaces, which is useful in many tasks such as
visualization, node classification, and link prediction. Most existing graph
embedding methods do not scale for real world information networks which
usually contain millions of nodes. In this paper, we propose a novel network
embedding method called the "LINE," which is suitable for arbitrary types of
information networks: undirected, directed, and/or weighted. The method
optimizes a carefully designed objective function that preserves both the local
and global network structures. An edge-sampling algorithm is proposed that
addresses the limitation of the classical stochastic gradient descent and
improves both the effectiveness and the efficiency of the inference. Empirical
experiments prove the effectiveness of the LINE on a variety of real-world
information networks, including language networks, social networks, and
citation networks. The algorithm is very efficient, which is able to learn the
embedding of a network with millions of vertices and billions of edges in a few
hours on a typical single machine. The source code of the LINE is available
online.Comment: WWW 201
Fourier-based classification of protein secondary structures
The correct prediction of protein secondary structures is one of the key
issues in predicting the correct protein folded shape, which is used for
determining gene function. Existing methods make use of amino acids properties
as indices to classify protein secondary structures, but are faced with a
significant number of misclassifications. The paper presents a technique for
the classification of protein secondary structures based on protein
"signal-plotting" and the use of the Fourier technique for digital signal
processing. New indices are proposed to classify protein secondary structures
by analyzing hydrophobicity profiles. The approach is simple and
straightforward. Results show that the more types of protein secondary
structures can be classified by means of these newly-proposed indices
- …