Increased interest in web-based education has spurred the proliferation of online learning environments. However, these platforms suffer from high dropout rates due to lack of su...
Sparse learning has been proven to be a powerful technique in supervised feature selection, which allows to embed feature selection into the classification (or regression) proble...
When scheduling tasks for field-deployable systems, our solutions must be robust to the uncertainty inherent in the real world. Although human intuition is trusted to balance rew...
Module extraction—the task of computing a (preferably small) fragment M of an ontology T that preserves entailments over a signature Σ—has found many applications in recent y...
Ana Armas Romero, Mark Kaminski, Bernardo Cuenca G...
Temperature Discovery Search (TDS) is a forward search method for computing or approximating the temperature of a combinatorial game. Temperature and mean are important concepts i...
The rapid urban expansion has greatly extended the physical boundary of users’ living area and developed a large number of POIs (points of interest). POI recommendation is a tas...
We present a novel semantics for extracting bounded-level modules from RDF ontologies and databases augmented with safe inference rules, `a la Datalog. Dealing with a recursive ru...
We present the first model of optimal voting under adversarial noise. From this viewpoint, voting rules are seen as errorcorrecting codes: their goal is to correct errors in the ...
This paper represents a paradigm shift in what advice agents should provide people. Contrary to what was previously thought, we empirically show that agents that dispense optimal ...
Avshalom Elmalech, David Sarne, Avi Rosenfeld, Ede...
We design a tractable Horn fragment of the Halpern-Shoham temporal logic and extend it to interval-based temporal description logics, instance checking in which is P-complete for ...
Alessandro Artale, Roman Kontchakov, Vladislav Ryz...