26,393 research outputs found
Do the Fix Ingredients Already Exist? An Empirical Inquiry into the Redundancy Assumptions of Program Repair Approaches
Much initial research on automatic program repair has focused on experimental
results to probe their potential to find patches and reduce development effort.
Relatively less effort has been put into understanding the hows and whys of
such approaches. For example, a critical assumption of the GenProg technique is
that certain bugs can be fixed by copying and re-arranging existing code. In
other words, GenProg assumes that the fix ingredients already exist elsewhere
in the code. In this paper, we formalize these assumptions around the concept
of ''temporal redundancy''. A temporally redundant commit is only composed of
what has already existed in previous commits. Our experiments show that a large
proportion of commits that add existing code are temporally redundant. This
validates the fundamental redundancy assumption of GenProg.Comment: ICSE - 36th IEEE International Conference on Software Engineering
(2014
Proton tracking in a high-granularity Digital Tracking Calorimeter for proton CT purposes
Radiation therapy with protons as of today utilizes information from x-ray CT
in order to estimate the proton stopping power of the traversed tissue in a
patient. The conversion from x-ray attenuation to proton stopping power in
tissue introduces range uncertainties of the order of 2-3% of the range,
uncertainties that are contributing to an increase of the necessary planning
margins added to the target volume in a patient. Imaging methods and
modalities, such as Dual Energy CT and proton CT, have come into consideration
in the pursuit of obtaining an as good as possible estimate of the proton
stopping power. In this study, a Digital Tracking Calorimeter is benchmarked
for proof-of-concept for proton CT purposes. The Digital Tracking Calorimeteris
applied for reconstruction of the tracks and energies of individual high energy
protons. The presented prototype forms the basis for a proton CT system using a
single technology for tracking and calorimetry. This advantage simplifies the
setup and reduces the cost of a proton CT system assembly, and it is a unique
feature of the Digital Tracking Calorimeter. Data from the AGORFIRM beamline at
KVI-CART in Groningen in the Netherlands and Monte Carlo simulation results are
used to in order to develop a tracking algorithm for the estimation of the
residual ranges of a high number of concurrent proton tracks. The range of the
individual protons can at present be estimated with a resolution of 4%. The
readout system for this prototype is able to handle an effective proton
frequency of 1 MHz by using 500 concurrent proton tracks in each readout frame,
which is at the high end range of present similar prototypes. A future further
optimized prototype will enable a high-speed and more accurate determination of
the ranges of individual protons in a therapeutic beam.Comment: 21 pages, 8 figure
Lost in Time: Temporal Analytics for Long-Term Video Surveillance
Video surveillance is a well researched area of study with substantial work
done in the aspects of object detection, tracking and behavior analysis. With
the abundance of video data captured over a long period of time, we can
understand patterns in human behavior and scene dynamics through data-driven
temporal analytics. In this work, we propose two schemes to perform descriptive
and predictive analytics on long-term video surveillance data. We generate
heatmap and footmap visualizations to describe spatially pooled trajectory
patterns with respect to time and location. We also present two approaches for
anomaly prediction at the day-level granularity: a trajectory-based statistical
approach, and a time-series based approach. Experimentation with one year data
from a single camera demonstrates the ability to uncover interesting insights
about the scene and to predict anomalies reasonably well.Comment: To Appear in Springer LNE
Theory and Practice of Data Citation
Citations are the cornerstone of knowledge propagation and the primary means
of assessing the quality of research, as well as directing investments in
science. Science is increasingly becoming "data-intensive", where large volumes
of data are collected and analyzed to discover complex patterns through
simulations and experiments, and most scientific reference works have been
replaced by online curated datasets. Yet, given a dataset, there is no
quantitative, consistent and established way of knowing how it has been used
over time, who contributed to its curation, what results have been yielded or
what value it has.
The development of a theory and practice of data citation is fundamental for
considering data as first-class research objects with the same relevance and
centrality of traditional scientific products. Many works in recent years have
discussed data citation from different viewpoints: illustrating why data
citation is needed, defining the principles and outlining recommendations for
data citation systems, and providing computational methods for addressing
specific issues of data citation.
The current panorama is many-faceted and an overall view that brings together
diverse aspects of this topic is still missing. Therefore, this paper aims to
describe the lay of the land for data citation, both from the theoretical (the
why and what) and the practical (the how) angle.Comment: 24 pages, 2 tables, pre-print accepted in Journal of the Association
for Information Science and Technology (JASIST), 201
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