AW
A.K. Wolska
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
2 records found
1
WebDSL is a DSL for creating web applications, combining many different aspects and domains of web design in a single language. The dynamic semantics of this language are not defined, despite multiple attempts, abandoned due to complexity of the language and lack of expression of chosen frameworks. We adapt the algebraic effects and handlers approach and the framework introduced in Datatypes a la carte (Swierstra, 2008) to create a modular denotational semantics model of WebDSL, extending the framework by a bifunctor formulation for multi-sort syntax definition that allows us to distinguish between effects raised by different components of the language. In the process of defining the framework and semantics in Haskell, we encountered obstacles and of working with algebraic effects and handlers paradigm in the language, leading us to compile workarounds, solutions and pitfalls to avoid when constructing and maintaining such model. In evaluation of the framework approach with earlier attempts at defining dynamic semantics for WebDSL, we find algebraic effects and handlers to be a viable and successful approach for modelling a rich DSL such as WebDSL, and propose possible improvements to the WebDSL compiler.
...
WebDSL is a DSL for creating web applications, combining many different aspects and domains of web design in a single language. The dynamic semantics of this language are not defined, despite multiple attempts, abandoned due to complexity of the language and lack of expression of chosen frameworks. We adapt the algebraic effects and handlers approach and the framework introduced in Datatypes a la carte (Swierstra, 2008) to create a modular denotational semantics model of WebDSL, extending the framework by a bifunctor formulation for multi-sort syntax definition that allows us to distinguish between effects raised by different components of the language. In the process of defining the framework and semantics in Haskell, we encountered obstacles and of working with algebraic effects and handlers paradigm in the language, leading us to compile workarounds, solutions and pitfalls to avoid when constructing and maintaining such model. In evaluation of the framework approach with earlier attempts at defining dynamic semantics for WebDSL, we find algebraic effects and handlers to be a viable and successful approach for modelling a rich DSL such as WebDSL, and propose possible improvements to the WebDSL compiler.
Optimal decision trees are not easily improvable in terms of accuracy. However, improving the pre-processing of underlying dataset can be the answer to creating more accurate decision trees. In this paper, multiple methods of binarising datasets are considered and the resulting decision trees compared. The binarisation is divided into two stages: discretisation and encoding, with various algorithms considered for both of the stages. Additionally, processing the data during the decision tree building, referred to as online processing, instead of beforehand, was considered. It was discovered that for smaller datasets, unsupervised discretisation was preferred, and extending one-hot encoding to also consider multiple categories at once as target gave better accuracy for trees with lower depth. For bigger datasets, online processing has shown to be beneficial.
...
Optimal decision trees are not easily improvable in terms of accuracy. However, improving the pre-processing of underlying dataset can be the answer to creating more accurate decision trees. In this paper, multiple methods of binarising datasets are considered and the resulting decision trees compared. The binarisation is divided into two stages: discretisation and encoding, with various algorithms considered for both of the stages. Additionally, processing the data during the decision tree building, referred to as online processing, instead of beforehand, was considered. It was discovered that for smaller datasets, unsupervised discretisation was preferred, and extending one-hot encoding to also consider multiple categories at once as target gave better accuracy for trees with lower depth. For bigger datasets, online processing has shown to be beneficial.