8,228 research outputs found
A Generalized Notion of Time for Modeling Temporal Networks
Most approaches for modeling and analyzing temporal networks do not explicitly discuss the underlying notion
of time. In this paper, we therefore introduce a generalized notion of time for temporal networks. Our
approach also allows for considering non-deterministic time and incomplete data, two issues that are often
found when analyzing data-sets extracted from online social networks, for example. In order to demonstrate
the consequences of our generalized notion of time, we also discuss the implications for the computation of
(shortest) temporal paths in temporal networks
Route Planning in Transportation Networks
We survey recent advances in algorithms for route planning in transportation
networks. For road networks, we show that one can compute driving directions in
milliseconds or less even at continental scale. A variety of techniques provide
different trade-offs between preprocessing effort, space requirements, and
query time. Some algorithms can answer queries in a fraction of a microsecond,
while others can deal efficiently with real-time traffic. Journey planning on
public transportation systems, although conceptually similar, is a
significantly harder problem due to its inherent time-dependent and
multicriteria nature. Although exact algorithms are fast enough for interactive
queries on metropolitan transit systems, dealing with continent-sized instances
requires simplifications or heavy preprocessing. The multimodal route planning
problem, which seeks journeys combining schedule-based transportation (buses,
trains) with unrestricted modes (walking, driving), is even harder, relying on
approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4,
previously published by Microsoft Research. This work was mostly done while
the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at
Microsoft Research Silicon Valle
Algorithm Engineering for Realistic Journey Planning in Transportation Networks
Diese Dissertation beschäftigt sich mit der Routenplanung in Transportnetzen. Es werden neue, effiziente algorithmische Ansätze zur Berechnung optimaler Verbindungen in öffentlichen Verkehrsnetzen, Straßennetzen und multimodalen Netzen, die verschiedene Transportmodi miteinander verknüpfen, eingeführt. Im Fokus der Arbeit steht dabei die Praktikabilität der Ansätze, was durch eine ausführliche experimentelle Evaluation belegt wird
FairFuzz: Targeting Rare Branches to Rapidly Increase Greybox Fuzz Testing Coverage
In recent years, fuzz testing has proven itself to be one of the most
effective techniques for finding correctness bugs and security vulnerabilities
in practice. One particular fuzz testing tool, American Fuzzy Lop or AFL, has
become popular thanks to its ease-of-use and bug-finding power. However, AFL
remains limited in the depth of program coverage it achieves, in particular
because it does not consider which parts of program inputs should not be
mutated in order to maintain deep program coverage. We propose an approach,
FairFuzz, that helps alleviate this limitation in two key steps. First,
FairFuzz automatically prioritizes inputs exercising rare parts of the program
under test. Second, it automatically adjusts the mutation of inputs so that the
mutated inputs are more likely to exercise these same rare parts of the
program. We conduct evaluation on real-world programs against state-of-the-art
versions of AFL, thoroughly repeating experiments to get good measures of
variability. We find that on certain benchmarks FairFuzz shows significant
coverage increases after 24 hours compared to state-of-the-art versions of AFL,
while on others it achieves high program coverage at a significantly faster
rate
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