1,684 research outputs found
Incremental and Commutative Composition of State-Machine Models of Features
In this paper, we present a technique for incre- mental and commutative composition of state-machine models of features, using the FeatureHouse framework. The inputs to FeatureHouse are feature state-machines (or state-machine fragments) modelled in a feature-oriented requirement modelling language called FORML and the outputs are two state-machine models: (1) a model of the whole product line with optional features guarded by presence conditions; this model is suitable for family-based analysis of the product line; and (2) an intermediate model of composition that facilitates incremental composition of future features. We discuss the challenges and benefits of our approach and our implementation in the FeatureHouse.NSERC / Automotive Partnership Canada, APCPJ 386797 - 09 ||
Ontario Research Fund, RE05-044 ||
NSERC Discovery Grant 155243-1
Distributional composition using higher-order dependency vectors
This paper concerns how to apply compositional methods to vectors based on grammatical dependency relation vectors. We demonstrate the potential of a novel approach which uses higher-order grammatical dependency relations as features. We apply the approach to adjective-noun compounds with promising results in the prediction of the vectors for (held-out) observed phrases
Towards MKM in the Large: Modular Representation and Scalable Software Architecture
MKM has been defined as the quest for technologies to manage mathematical
knowledge. MKM "in the small" is well-studied, so the real problem is to scale
up to large, highly interconnected corpora: "MKM in the large". We contend that
advances in two areas are needed to reach this goal. We need representation
languages that support incremental processing of all primitive MKM operations,
and we need software architectures and implementations that implement these
operations scalably on large knowledge bases.
We present instances of both in this paper: the MMT framework for modular
theory-graphs that integrates meta-logical foundations, which forms the base of
the next OMDoc version; and TNTBase, a versioned storage system for XML-based
document formats. TNTBase becomes an MMT database by instantiating it with
special MKM operations for MMT.Comment: To appear in The 9th International Conference on Mathematical
Knowledge Management: MKM 201
MLI: An API for Distributed Machine Learning
MLI is an Application Programming Interface designed to address the
challenges of building Machine Learn- ing algorithms in a distributed setting
based on data-centric computing. Its primary goal is to simplify the
development of high-performance, scalable, distributed algorithms. Our initial
results show that, relative to existing systems, this interface can be used to
build distributed implementations of a wide variety of common Machine Learning
algorithms with minimal complexity and highly competitive performance and
scalability
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