7,465 research outputs found
Coalgebraic Behavioral Metrics
We study different behavioral metrics, such as those arising from both
branching and linear-time semantics, in a coalgebraic setting. Given a
coalgebra for a functor , we define a framework for deriving pseudometrics on which
measure the behavioral distance of states.
A crucial step is the lifting of the functor on to a
functor on the category of pseudometric spaces.
We present two different approaches which can be viewed as generalizations of
the Kantorovich and Wasserstein pseudometrics for probability measures. We show
that the pseudometrics provided by the two approaches coincide on several
natural examples, but in general they differ.
If has a final coalgebra, every lifting yields in a
canonical way a behavioral distance which is usually branching-time, i.e., it
generalizes bisimilarity. In order to model linear-time metrics (generalizing
trace equivalences), we show sufficient conditions for lifting distributive
laws and monads. These results enable us to employ the generalized powerset
construction
Polygonal Building Segmentation by Frame Field Learning
While state of the art image segmentation models typically output
segmentations in raster format, applications in geographic information systems
often require vector polygons. To help bridge the gap between deep network
output and the format used in downstream tasks, we add a frame field output to
a deep segmentation model for extracting buildings from remote sensing images.
We train a deep neural network that aligns a predicted frame field to ground
truth contours. This additional objective improves segmentation quality by
leveraging multi-task learning and provides structural information that later
facilitates polygonization; we also introduce a polygonization algorithm that
utilizes the frame field along with the raster segmentation. Our code is
available at https://github.com/Lydorn/Polygonization-by-Frame-Field-Learning.Comment: CVPR 2021 - IEEE Conference on Computer Vision and Pattern
Recognition, Jun 2021, Pittsburg / Virtual, United State
Intelligent Data-Driven Reverse Engineering of Software Design Patterns
Recognising implemented instances of Design Patterns (DPs) in software design discloses and recovers a wealth of information about the intention of the original designers and the rationale for their design decisions. Because it is often the case that the documentation available for software systems, if any, is poor and/or obsolete, recovering such information can be of great help and importance for maintenance tasks. However, since DPs are abstractly and vaguely defined, a set of software classes with exactly the same relationships as expected for a DP instance may actually be only accidentally similar. On the other hand, a set of classes with relationships that are, to an extent, different from those typically expected can still be a true DP instance. The deciding factor is mainly concerned with whether or not the set of classes is actually intended to solve the design problem addressed by the DP, thus making the intent a fundamental and defining characteristic of DPs.
Discerning the intent of potential instances requires building complex models that cannot be built using only the descriptions of DPs in books and catalogues. Accordingly, a paradigm shift in DP recognition towards fully machine learning based approaches is required. The problem is that no accurate and sufficiently large DP datasets exist, and it is difficult to manually construct one. Moreover, there is a lack of research on the feature set that should be used in DP recognition. The main aim of this thesis is to enable the required paradigm shift by laying down an accurate, comprehensive and information-rich foundation of feature and data sets. In order to achieve this aim, a large set of features is developed to cover a wide range of design aspects, with particular focus on design intent. This set serves as a global feature set from which different subsets can be objectively selected for different DPs. A new and feasible approach to DP dataset construction is designed and used to construct training datasets. The feature and data sets are then used experimentally to build and train DP classifiers. The results demonstrate the accuracy and utility of the sets introduced, and show that fully machine learning based approaches are capable of providing appropriate and well-equipped solutions for the problem of DP recognition.Saudi Cultural Burea
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