Mathematical Formulae in Wikimedia Projects 2020

This poster summarizes our contributions to Wikimedia's processing pipeline for mathematical formulae. We describe how we have supported the transition from rendering formulae as course-grained PNG images in 2001 to providing modern semantically enriched language-independent MathML formulae in 2020. Additionally, we describe our plans to improve the accessibility and discoverability of mathematical knowledge in Wikimedia projects further.Comment: Submitted to JCDL 2020: Proceedings of the ACM/ IEEE Joint Conference on Digital Libraries in 2020 (JCDL '20), August 1-5, 2020, Virtual Event, Chin

DEvIANT: Discovering Significant Exceptional (Dis-)Agreement Within Groups

We strive to find contexts (i.e., subgroups of entities) under which exceptional (dis-)agreement occurs among a group of individuals , in any type of data featuring individuals (e.g., parliamentarians , customers) performing observable actions (e.g., votes, ratings) on entities (e.g., legislative procedures, movies). To this end, we introduce the problem of discovering statistically significant exceptional contextual intra-group agreement patterns. To handle the sparsity inherent to voting and rating data, we use Krippendorff's Alpha measure for assessing the agreement among individuals. We devise a branch-and-bound algorithm , named DEvIANT, to discover such patterns. DEvIANT exploits both closure operators and tight optimistic estimates. We derive analytic approximations for the confidence intervals (CIs) associated with patterns for a computationally efficient significance assessment. We prove that these approximate CIs are nested along specialization of patterns. This allows to incorporate pruning properties in DEvIANT to quickly discard non-significant patterns. Empirical study on several datasets demonstrates the efficiency and the usefulness of DEvIANT. Technical Report Associated with the ECML/PKDD 2019 Paper entitled: "DEvIANT: Discovering Significant Exceptional (Dis-)Agreement Within Groups"

Optimal Subgroup Discovery in Purely Numerical Data

International audienceSubgroup discovery in labeled data is the task of discovering patterns in the description space of objects to find subsets of objects whose labels show an interesting distribution, for example the disproportionate representation of a label value. Discovering interesting subgroups in purely numerical data-attributes and target label-has received little attention so far. Existing methods make use of discretization methods that lead to a loss of information and suboptimal results. This is the case for the reference algorithm SD-Map*. We consider here the discovery of optimal subgroups according to an interestingness measure in purely numerical data. We leverage the concept of closed interval patterns and advanced enumeration and pruning techniques. The performances of our algorithm are studied empirically and its added-value w.r.t. SD-Map* is illustrated