9,252 research outputs found
Cinnamons: A Computation Model Underlying Control Network Programming
We give the easily recognizable name "cinnamon" and "cinnamon programming" to
a new computation model intended to form a theoretical foundation for Control
Network Programming (CNP). CNP has established itself as a programming paradigm
combining declarative and imperative features, built-in search engine, powerful
tools for search control that allow easy, intuitive, visual development of
heuristic, nondeterministic, and randomized solutions. We define rigorously the
syntax and semantics of the new model of computation, at the same time trying
to keep clear the intuition behind and to include enough examples. The
purposely simplified theoretical model is then compared to both WHILE-programs
(thus demonstrating its Turing-completeness), and the "real" CNP. Finally,
future research possibilities are mentioned that would eventually extend the
cinnamon programming into the directions of nondeterminism, randomness, and
fuzziness.Comment: 7th Intl Conf. on Computer Science, Engineering & Applications
(ICCSEA 2017) September 23~24, 2017, Copenhagen, Denmar
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Which Classes of Origin Graphs Are Generated by Transducers.
We study various models of transducers equipped with origin information. We consider the semantics of these models as particular graphs, called origin graphs, and we characterise the families of such graphs recognised by streaming string transducers
Piloting an Empirical Study on Measures for Workflow Similarity
Service discovery of state dependent services has to take workflow aspects into account. To increase the usability of a service discovery, the result list of services should be ordered with regard to the relevance of the services. Means of ordering a list of workflows due to their similarity with regard to a query are missing. This paper presents a pilot of an empirical study on the influence of different measures on workflow similarity. It turns out that, although preliminary, relations between different measures are indicated and that a similarity definition depends on the application scenario in which the service discovery is applied
A Grammatical Inference Approach to Language-Based Anomaly Detection in XML
False-positives are a problem in anomaly-based intrusion detection systems.
To counter this issue, we discuss anomaly detection for the eXtensible Markup
Language (XML) in a language-theoretic view. We argue that many XML-based
attacks target the syntactic level, i.e. the tree structure or element content,
and syntax validation of XML documents reduces the attack surface. XML offers
so-called schemas for validation, but in real world, schemas are often
unavailable, ignored or too general. In this work-in-progress paper we describe
a grammatical inference approach to learn an automaton from example XML
documents for detecting documents with anomalous syntax.
We discuss properties and expressiveness of XML to understand limits of
learnability. Our contributions are an XML Schema compatible lexical datatype
system to abstract content in XML and an algorithm to learn visibly pushdown
automata (VPA) directly from a set of examples. The proposed algorithm does not
require the tree representation of XML, so it can process large documents or
streams. The resulting deterministic VPA then allows stream validation of
documents to recognize deviations in the underlying tree structure or
datatypes.Comment: Paper accepted at First Int. Workshop on Emerging Cyberthreats and
Countermeasures ECTCM 201
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