136,035 research outputs found
Real-time multiple-look synthetic aperture radar processor for spacecraft applications
A spaceborne synthetic aperture radar (SAR) having pipeline multiple-look data processing is described which makes use of excessive azimuth bandwidth in radar echo signals to produce multiple-looking images. Time multiplexed single-look image lines from an azimuth correlator go through an energy analyzer which analyzes the mean energy in each separate look to determine the radar antenna electric boresight for use in generating the correct reference functions for the production of high quality SAR images. The multiplexed single look image lines also go through a registration delay to produce multi-look images
IIFA: Modular Inter-app Intent Information Flow Analysis of Android Applications
Android apps cooperate through message passing via intents. However, when
apps do not have identical sets of privileges inter-app communication (IAC) can
accidentally or maliciously be misused, e.g., to leak sensitive information
contrary to users expectations. Recent research considered static program
analysis to detect dangerous data leaks due to inter-component communication
(ICC) or IAC, but suffers from shortcomings with respect to precision,
soundness, and scalability. To solve these issues we propose a novel approach
for static ICC/IAC analysis. We perform a fixed-point iteration of ICC/IAC
summary information to precisely resolve intent communication with more than
two apps involved. We integrate these results with information flows generated
by a baseline (i.e. not considering intents) information flow analysis, and
resolve if sensitive data is flowing (transitively) through components/apps in
order to be ultimately leaked. Our main contribution is the first fully
automatic sound and precise ICC/IAC information flow analysis that is scalable
for realistic apps due to modularity, avoiding combinatorial explosion: Our
approach determines communicating apps using short summaries rather than
inlining intent calls, which often requires simultaneously analyzing all tuples
of apps. We evaluated our tool IIFA in terms of scalability, precision, and
recall. Using benchmarks we establish that precision and recall of our
algorithm are considerably better than prominent state-of-the-art analyses for
IAC. But foremost, applied to the 90 most popular applications from the Google
Playstore, IIFA demonstrated its scalability to a large corpus of real-world
apps. IIFA reports 62 problematic ICC-/IAC-related information flows via two or
more apps/components
Spatial-temporal data mining procedure: LASR
This paper is concerned with the statistical development of our
spatial-temporal data mining procedure, LASR (pronounced ``laser''). LASR is
the abbreviation for Longitudinal Analysis with Self-Registration of
large--small- data. It was motivated by a study of ``Neuromuscular
Electrical Stimulation'' experiments, where the data are noisy and
heterogeneous, might not align from one session to another, and involve a large
number of multiple comparisons. The three main components of LASR are: (1) data
segmentation for separating heterogeneous data and for distinguishing outliers,
(2) automatic approaches for spatial and temporal data registration, and (3)
statistical smoothing mapping for identifying ``activated'' regions based on
false-discovery-rate controlled -maps and movies. Each of the components is
of interest in its own right. As a statistical ensemble, the idea of LASR is
applicable to other types of spatial-temporal data sets beyond those from the
NMES experiments.Comment: Published at http://dx.doi.org/10.1214/074921706000000707 in the IMS
Lecture Notes--Monograph Series
(http://www.imstat.org/publications/lecnotes.htm) by the Institute of
Mathematical Statistics (http://www.imstat.org
Argumentation Mining in User-Generated Web Discourse
The goal of argumentation mining, an evolving research field in computational
linguistics, is to design methods capable of analyzing people's argumentation.
In this article, we go beyond the state of the art in several ways. (i) We deal
with actual Web data and take up the challenges given by the variety of
registers, multiple domains, and unrestricted noisy user-generated Web
discourse. (ii) We bridge the gap between normative argumentation theories and
argumentation phenomena encountered in actual data by adapting an argumentation
model tested in an extensive annotation study. (iii) We create a new gold
standard corpus (90k tokens in 340 documents) and experiment with several
machine learning methods to identify argument components. We offer the data,
source codes, and annotation guidelines to the community under free licenses.
Our findings show that argumentation mining in user-generated Web discourse is
a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in
User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17
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