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Found 6 papers, 2 papers with code
Redescription Model Mining
This paper introduces Redescription Model Mining, a novel approach to identify interpretable patterns across two datasets that share only a subset of attributes and have no common instances.
MapLUR: Exploring a new Paradigm for Estimating Air Pollution using Deep Learning on Map Images
In this paper, we advocate a paradigm shift for LUR models: We propose the Data-driven, Open, Global (DOG) paradigm that entails models based on purely data-driven approaches using only openly and globally available data.
Scalable and Precise Estimation and Debugging of the Worst-Case Execution Time for Analysis-Friendly Processors
We define and identify such processors, and then we propose an automated tool set which estimates a precise WCET without unsafe manual inputs, and also reconstructs a maximum-detail view of the WCET path that can be examined in a debugger environment.
Software Engineering
Adaptive kNN using Expected Accuracy for Classification of Geo-Spatial Data
This paper proposes an adaptive kNN classifier where k is chosen dynamically for each instance (point) to be classified, such that the expected accuracy of classification is maximized.
Learning Semantic Relatedness From Human Feedback Using Metric Learning
To solve this, we propose to utilize a metric learning approach to improve existing semantic relatedness measures by learning from additional information, such as explicit human feedback.
Analyzing Features for the Detection of Happy Endings in German Novels
With regard to a computational representation of literary plot, this paper looks at the use of sentiment analysis for happy ending detection in German novels.
