14,208 research outputs found
Using High-Rising Cities to Visualize Performance in Real-Time
For developers concerned with a performance drop or improvement in their
software, a profiler allows a developer to quickly search and identify
bottlenecks and leaks that consume much execution time. Non real-time profilers
analyze the history of already executed stack traces, while a real-time
profiler outputs the results concurrently with the execution of software, so
users can know the results instantaneously. However, a real-time profiler risks
providing overly large and complex outputs, which is difficult for developers
to quickly analyze. In this paper, we visualize the performance data from a
real-time profiler. We visualize program execution as a three-dimensional (3D)
city, representing the structure of the program as artifacts in a city (i.e.,
classes and packages expressed as buildings and districts) and their program
executions expressed as the fluctuating height of artifacts. Through two case
studies and using a prototype of our proposed visualization, we demonstrate how
our visualization can easily identify performance issues such as a memory leak
and compare performance changes between versions of a program. A demonstration
of the interactive features of our prototype is available at
https://youtu.be/eleVo19Hp4k.Comment: 10 pages, VISSOFT 2017, Artifact:
https://github.com/sefield/high-rising-city-artifac
Model Transformations in MT
Model transformations are recognised as a vital aspect of Model Driven Development,but current approaches cover only a small part of the possible spectrum. In this paper I present the MT model transformation which shows how a QVT-like language can be extended with novel pattern matching constructs, how tracing information can be automatically constructed and visualized, and how the transformed model is pruned of extraneous elements. As MT is implemented as a DSL within the Converge language, this paper also demonstrates how a general purpose language can be embedded in a model transformation language, and how DSL development can aid experimentation and exploration of new parts of the model transformation spectrum
NEMESYS: Enhanced Network Security for Seamless Service Provisioning in the Smart Mobile Ecosystem
As a consequence of the growing popularity of smart mobile devices, mobile
malware is clearly on the rise, with attackers targeting valuable user
information and exploiting vulnerabilities of the mobile ecosystems. With the
emergence of large-scale mobile botnets, smartphones can also be used to launch
attacks on mobile networks. The NEMESYS project will develop novel security
technologies for seamless service provisioning in the smart mobile ecosystem,
and improve mobile network security through better understanding of the threat
landscape. NEMESYS will gather and analyze information about the nature of
cyber-attacks targeting mobile users and the mobile network so that appropriate
counter-measures can be taken. We will develop a data collection infrastructure
that incorporates virtualized mobile honeypots and a honeyclient, to gather,
detect and provide early warning of mobile attacks and better understand the
modus operandi of cyber-criminals that target mobile devices. By correlating
the extracted information with the known patterns of attacks from wireline
networks, we will reveal and identify trends in the way that cyber-criminals
launch attacks against mobile devices.Comment: Accepted for publication in Proceedings of the 28th International
Symposium on Computer and Information Sciences (ISCIS'13); 9 pages; 1 figur
Eligibility Propagation to Speed up Time Hopping for Reinforcement Learning
A mechanism called Eligibility Propagation is proposed to speed up the Time
Hopping technique used for faster Reinforcement Learning in simulations.
Eligibility Propagation provides for Time Hopping similar abilities to what
eligibility traces provide for conventional Reinforcement Learning. It
propagates values from one state to all of its temporal predecessors using a
state transitions graph. Experiments on a simulated biped crawling robot
confirm that Eligibility Propagation accelerates the learning process more than
3 times.Comment: 7 page
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