110,593 research outputs found
Neural-Augmented Static Analysis of Android Communication
We address the problem of discovering communication links between
applications in the popular Android mobile operating system, an important
problem for security and privacy in Android. Any scalable static analysis in
this complex setting is bound to produce an excessive amount of
false-positives, rendering it impractical. To improve precision, we propose to
augment static analysis with a trained neural-network model that estimates the
probability that a communication link truly exists. We describe a
neural-network architecture that encodes abstractions of communicating objects
in two applications and estimates the probability with which a link indeed
exists. At the heart of our architecture are type-directed encoders (TDE), a
general framework for elegantly constructing encoders of a compound data type
by recursively composing encoders for its constituent types. We evaluate our
approach on a large corpus of Android applications, and demonstrate that it
achieves very high accuracy. Further, we conduct thorough interpretability
studies to understand the internals of the learned neural networks.Comment: Appears in Proceedings of the 2018 ACM Joint European Software
Engineering Conference and Symposium on the Foundations of Software
Engineering (ESEC/FSE
Strategy Logic with Imperfect Information
We introduce an extension of Strategy Logic for the imperfect-information
setting, called SLii, and study its model-checking problem. As this logic
naturally captures multi-player games with imperfect information, the problem
turns out to be undecidable. We introduce a syntactical class of "hierarchical
instances" for which, intuitively, as one goes down the syntactic tree of the
formula, strategy quantifications are concerned with finer observations of the
model. We prove that model-checking SLii restricted to hierarchical instances
is decidable. This result, because it allows for complex patterns of
existential and universal quantification on strategies, greatly generalises
previous ones, such as decidability of multi-player games with imperfect
information and hierarchical observations, and decidability of distributed
synthesis for hierarchical systems. To establish the decidability result, we
introduce and study QCTL*ii, an extension of QCTL* (itself an extension of CTL*
with second-order quantification over atomic propositions) by parameterising
its quantifiers with observations. The simple syntax of QCTL* ii allows us to
provide a conceptually neat reduction of SLii to QCTL*ii that separates
concerns, allowing one to forget about strategies and players and focus solely
on second-order quantification. While the model-checking problem of QCTL*ii is,
in general, undecidable, we identify a syntactic fragment of hierarchical
formulas and prove, using an automata-theoretic approach, that it is decidable.
The decidability result for SLii follows since the reduction maps hierarchical
instances of SLii to hierarchical formulas of QCTL*ii
Reasoning about Knowledge and Strategies under Hierarchical Information
Two distinct semantics have been considered for knowledge in the context of
strategic reasoning, depending on whether players know each other's strategy or
not. The problem of distributed synthesis for epistemic temporal specifications
is known to be undecidable for the latter semantics, already on systems with
hierarchical information. However, for the other, uninformed semantics, the
problem is decidable on such systems. In this work we generalise this result by
introducing an epistemic extension of Strategy Logic with imperfect
information. The semantics of knowledge operators is uninformed, and captures
agents that can change observation power when they change strategies. We solve
the model-checking problem on a class of "hierarchical instances", which
provides a solution to a vast class of strategic problems with epistemic
temporal specifications on hierarchical systems, such as distributed synthesis
or rational synthesis
Models for CSP with availability information
We consider models of CSP based on recording what events are available as
possible alternatives to the events that are actually performed. We present
many different varieties of such models. For each, we give a compositional
semantics, congruent to the operational semantics, and prove full abstraction
and no-junk results. We compare the expressiveness of the different models.Comment: In Proceedings EXPRESS'10, arXiv:1011.601
Geospatial information infrastructures
Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Geospatial information infrastructures (GIIs) provide the technological, semantic,organizationalandlegalstructurethatallowforthediscovery,sharing,and use of geospatial information (GI). In this chapter, we introduce the overall concept and surrounding notions such as geographic information systems (GIS) and spatial datainfrastructures(SDI).WeoutlinethehistoryofGIIsintermsoftheorganizational andtechnologicaldevelopmentsaswellasthecurrentstate-of-art,andreflectonsome of the central challenges and possible future trajectories. We focus on the tension betweenincreasedneedsforstandardizationandtheever-acceleratingtechnological changes. We conclude that GIIs evolved as a strong underpinning contribution to implementation of the Digital Earth vision. In the future, these infrastructures are challengedtobecomeflexibleandrobustenoughtoabsorbandembracetechnological transformationsandtheaccompanyingsocietalandorganizationalimplications.With this contribution, we present the reader a comprehensive overview of the field and a solid basis for reflections about future developments
Probabilistic Argumentation with Epistemic Extensions and Incomplete Information
Abstract argumentation offers an appealing way of representing and evaluating
arguments and counterarguments. This approach can be enhanced by a probability
assignment to each argument. There are various interpretations that can be
ascribed to this assignment. In this paper, we regard the assignment as
denoting the belief that an agent has that an argument is justifiable, i.e.,
that both the premises of the argument and the derivation of the claim of the
argument from its premises are valid. This leads to the notion of an epistemic
extension which is the subset of the arguments in the graph that are believed
to some degree (which we defined as the arguments that have a probability
assignment greater than 0.5). We consider various constraints on the
probability assignment. Some constraints correspond to standard notions of
extensions, such as grounded or stable extensions, and some constraints give us
new kinds of extensions
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