eXplainable Modeling (XM): Data Analysis for Intelligent Agents

Intelligent agents perform key tasks in several application domains by processing sensor data and taking actions that maximize reward functions based on internal models of the environment and the agent itself. In this paper we present eXplainable Modeling (XM), a Python software which supports data analysis for intelligent agents. XM enables to analyze state-models, namely models of the agent states, discovered from sensor traces by data-driven methods, and to interpret them for improved situation awareness. The main features of the tool are described through the analysis of a real case study concerning aquatic drones for water monitoring

Weight-based search to find clusters around medians in subspaces

International audienceThere exist several clustering paradigms, leading to different techniques that are complementary in the analyst toolbox, each having its own merits and interests. Among these techniques, the K-medians approach is recognized as being robust to noise and outliers, and is an important optimization task with many different applications (e.g., facility location). In the context of subspace clustering, several paradigms have been investigated (e.g., centroid-based, cell-based), while the median-based approach has received less attention. Moreover, using standard subspace clustering outputs (e.g., centroids, medoids) there is no straightforward procedure to compute the cluster membership that optimizes the dispersion around medians. This paper advocates for the use of median-based subspace clustering as a complementary tool. Indeed, it shows that such an approach exhibits satisfactory quality clusters when compared to well-established paradigms, while medians have still their own interests depending on the user application (robustness to noise/outliers and location optimality). This paper shows that a weight-based hill climbing algorithm using a stochastic local exploration step can be sufficient to produce the clusters

Weight-based search to find clusters around medians in subspaces

International audienceThere exist several clustering paradigms, leading to different techniques that are complementary in the analyst toolbox, each having its own merits and interests. Among these techniques, the K-medians approach is recognized as being robust to noise and outliers, and is an important optimization task with many different applications (e.g., facility location). In the context of subspace clustering, several paradigms have been investigated (e.g., centroid-based, cell-based), while the median-based approach has received less attention. Moreover, using standard subspace clustering outputs (e.g., centroids, medoids) there is no straightforward procedure to compute the cluster membership that optimizes the dispersion around medians. This paper advocates for the use of median-based subspace clustering as a complementary tool. Indeed, it shows that such an approach exhibits satisfactory quality clusters when compared to well-established paradigms, while medians have still their own interests depending on the user application (robustness to noise/outliers and location optimality). This paper shows that a weight-based hill climbing algorithm using a stochastic local exploration step can be sufficient to produce the clusters

EvoEvo Deliverable 6.8: Final report

Final report of the EvoEvo project

DH Benelux Journal 1. Integrating Digital Humanities.

The first volume of the DH Benelux Journal. This volume includes four full-length, peer-reviewed articles that are based on accepted contributions to the 2018 DH Benelux conference in Amsterdam (The Netherlands) on Integrating Digital Humanities. Contents: 1. Editors' Preface (Wout Dillen, Marijn Koolen, Marieke van Erp) 2. Introduction: Integrating Digital Humanities (Julie Birkholz and Gerben Zaagsma) 3. Boundary practices of digital humanities collaborations (Max Kemman) 4. Manuscripts, Metadata, and Medieval Multilingualism: Using a Manuscript Dataset to Analyze Language Use and Distribution in Medieval England (Krista A. Murchison and Ben Companjen) 5. Analysis of Fidel Castro Speeches Enhanced by Data Mining (Sergio Peignier and Patricia Zapata) 6. Character Centrality in Present-Day Dutch Literary Fiction (Roel Smeets, Eric Sanders, and Antal van den Bosch