89,988 research outputs found
A Framework for Datatype Transformation
We study one dimension in program evolution, namely the evolution of the
datatype declarations in a program. To this end, a suite of basic
transformation operators is designed. We cover structure-preserving
refactorings, but also structure-extending and -reducing adaptations. Both the
object programs that are subject to datatype transformations, and the meta
programs that encode datatype transformations are functional programs.Comment: Minor revision; now accepted at LDTA 200
Recovering Grammar Relationships for the Java Language Specification
Grammar convergence is a method that helps discovering relationships between
different grammars of the same language or different language versions. The key
element of the method is the operational, transformation-based representation
of those relationships. Given input grammars for convergence, they are
transformed until they are structurally equal. The transformations are composed
from primitive operators; properties of these operators and the composed chains
provide quantitative and qualitative insight into the relationships between the
grammars at hand. We describe a refined method for grammar convergence, and we
use it in a major study, where we recover the relationships between all the
grammars that occur in the different versions of the Java Language
Specification (JLS). The relationships are represented as grammar
transformation chains that capture all accidental or intended differences
between the JLS grammars. This method is mechanized and driven by nominal and
structural differences between pairs of grammars that are subject to
asymmetric, binary convergence steps. We present the underlying operator suite
for grammar transformation in detail, and we illustrate the suite with many
examples of transformations on the JLS grammars. We also describe the
extraction effort, which was needed to make the JLS grammars amenable to
automated processing. We include substantial metadata about the convergence
process for the JLS so that the effort becomes reproducible and transparent
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Microservices and Machine Learning Algorithms for Adaptive Green Buildings
In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings
A collaborative platform for integrating and optimising Computational Fluid Dynamics analysis requests
A Virtual Integration Platform (VIP) is described which provides support for the integration of Computer-Aided Design (CAD) and Computational Fluid Dynamics (CFD) analysis tools into an environment that supports the use of these tools in a distributed collaborative manner. The VIP has evolved through previous EU research conducted within the VRShips-ROPAX 2000 (VRShips) project and the current version discussed here was developed predominantly within the VIRTUE project but also within the SAFEDOR project. The VIP is described with respect to the support it provides to designers and analysts in coordinating and optimising CFD analysis requests. Two case studies are provided that illustrate the application of the VIP within HSVA: the use of a panel code for the evaluation of geometry variations in order to improve propeller efficiency; and, the use of a dedicated maritime RANS code (FreSCo) to improve the wake distribution for the VIRTUE tanker. A discussion is included detailing the background, application and results from the use of the VIP within these two case studies as well as how the platform was of benefit during the development and a consideration of how it can benefit HSVA in the future
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