32,167 research outputs found
Formalising the Continuous/Discrete Modeling Step
Formally capturing the transition from a continuous model to a discrete model
is investigated using model based refinement techniques. A very simple model
for stopping (eg. of a train) is developed in both the continuous and discrete
domains. The difference between the two is quantified using generic results
from ODE theory, and these estimates can be compared with the exact solutions.
Such results do not fit well into a conventional model based refinement
framework; however they can be accommodated into a model based retrenchment.
The retrenchment is described, and the way it can interface to refinement
development on both the continuous and discrete sides is outlined. The approach
is compared to what can be achieved using hybrid systems techniques.Comment: In Proceedings Refine 2011, arXiv:1106.348
MORPH: A Reference Architecture for Configuration and Behaviour Self-Adaptation
An architectural approach to self-adaptive systems involves runtime change of
system configuration (i.e., the system's components, their bindings and
operational parameters) and behaviour update (i.e., component orchestration).
Thus, dynamic reconfiguration and discrete event control theory are at the
heart of architectural adaptation. Although controlling configuration and
behaviour at runtime has been discussed and applied to architectural
adaptation, architectures for self-adaptive systems often compound these two
aspects reducing the potential for adaptability. In this paper we propose a
reference architecture that allows for coordinated yet transparent and
independent adaptation of system configuration and behaviour
Iterative criteria-based approach to engineering the requirements of software development methodologies
Software engineering endeavours are typically based on and governed by the requirements of the target software; requirements identification is therefore an integral part of software development methodologies. Similarly, engineering a software development methodology (SDM) involves the identification of the requirements of the target methodology. Methodology engineering approaches pay special attention to this issue; however, they make little use of existing methodologies as sources of insight into methodology requirements. The authors propose an iterative method for eliciting and specifying the requirements of a SDM using existing methodologies as supplementary resources. The method is performed as the analysis phase of a methodology engineering process aimed at the ultimate design and implementation of a target methodology. An initial set of requirements is first identified through analysing the characteristics of the development situation at hand and/or via delineating the general features desirable in the target methodology. These initial requirements are used as evaluation criteria; refined through iterative application to a select set of relevant methodologies. The finalised criteria highlight the qualities that the target methodology is expected to possess, and are therefore used as a basis for de. ning the final set of requirements. In an example, the authors demonstrate how the proposed elicitation process can be used for identifying the requirements of a general object-oriented SDM. Owing to its basis in knowledge gained from existing methodologies and practices, the proposed method can help methodology engineers produce a set of requirements that is not only more complete in span, but also more concrete and rigorous
A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images
Semantic segmentation is the pixel-wise labelling of an image. Since the
problem is defined at the pixel level, determining image class labels only is
not acceptable, but localising them at the original image pixel resolution is
necessary. Boosted by the extraordinary ability of convolutional neural
networks (CNN) in creating semantic, high level and hierarchical image
features; excessive numbers of deep learning-based 2D semantic segmentation
approaches have been proposed within the last decade. In this survey, we mainly
focus on the recent scientific developments in semantic segmentation,
specifically on deep learning-based methods using 2D images. We started with an
analysis of the public image sets and leaderboards for 2D semantic
segmantation, with an overview of the techniques employed in performance
evaluation. In examining the evolution of the field, we chronologically
categorised the approaches into three main periods, namely pre-and early deep
learning era, the fully convolutional era, and the post-FCN era. We technically
analysed the solutions put forward in terms of solving the fundamental problems
of the field, such as fine-grained localisation and scale invariance. Before
drawing our conclusions, we present a table of methods from all mentioned eras,
with a brief summary of each approach that explains their contribution to the
field. We conclude the survey by discussing the current challenges of the field
and to what extent they have been solved.Comment: Updated with new studie
Domain-specific functional software testing: A progress report
Software Engineering is a knowledge intensive activity that involves defining, designing, developing, and maintaining software systems. In order to build effective systems to support Software Engineering activities, Artificial Intelligence techniques are needed. The application of Artificial Intelligence technology to Software Engineering is called Knowledge-based Software Engineering (KBSE). The goal of KBSE is to change the software life cycle such that software maintenance and evolution occur by modifying the specifications and then rederiving the implementation rather than by directly modifying the implementation. The use of domain knowledge in developing KBSE systems is crucial. Our work is mainly related to one area of KBSE that is called automatic specification acquisition. One example is the WATSON prototype on which our current work is based. WATSON is an automatic programming system for formalizing specifications for telephone switching software mainly restricted to POTS, i.e., plain old telephone service. Our current approach differentiates itself from other approaches in two antagonistic ways. On the one hand, we address a large and complex real-world problem instead of a 'toy domain' as in many research prototypes. On the other hand, to allow such scaling, we had to relax the ambitious goal of complete automatic programming, to the easier task of automatic testing
Abstract State Machines 1988-1998: Commented ASM Bibliography
An annotated bibliography of papers which deal with or use Abstract State
Machines (ASMs), as of January 1998.Comment: Also maintained as a BibTeX file at http://www.eecs.umich.edu/gasm
Riemann-Langevin Particle Filtering in Track-Before-Detect
Track-before-detect (TBD) is a powerful approach that consists in providing
the tracker with sensor measurements directly without pre-detection. Due to the
measurement model non-linearities, online state estimation in TBD is most
commonly solved via particle filtering. Existing particle filters for TBD do
not incorporate measurement information in their proposal distribution. The
Langevin Monte Carlo (LMC) is a sampling method whose proposal is able to
exploit all available knowledge of the posterior (that is, both prior and
measurement information). This letter synthesizes recent advances in LMC-based
filtering to describe the Riemann-Langevin particle filter and introduces its
novel application to TBD. The benefits of our approach are illustrated in a
challenging low-noise scenario.Comment: Minor grammatical update
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