Abstract
Collaborative Data Mining (CDM) develops techniques to solve complex problems of data analysis requiring sets of experts in different domains that may be geographically separate. An important issue in CDM is the sharing of experience among the different experts. In this paper we report on a framework that enables users with different expertise to perform data analysis activities and profit, in a collaborative fashion, from expertise and results of other researchers. The collaborative process is supported by web services that seek for relevant knowledge available among the collaborative web sites. We have successfully designed and deployed a prototype for collaborative Data Mining in domains of Molecular Biology and Chemoinformatics.

Abstract
The oral microbiota is a highly complex and diversified part of the human microbiome. Being located at the interface between the human body and the exterior environment, this microbiota can deepen our understanding of the environmental impacts on the global status of human health. This research topic has been well addressed in Westernized populations, but these populations only represent a fraction of human diversity. Papua New Guinea hosts very diverse environments and one of the most unique human biological diversities worldwide. In this study we performed the first known characterization of the oral microbiome in 85 Papua New Guinean individuals living in different environments, using a qualitative and quantitative approach. We found a significant geographical structure of the Papua New Guineans oral microbiome, especially in the groups most isolated from urban spaces. In comparison to other global populations, two bacterial genera related to iron absorption were significantly more abundant in Papua New Guineans and Aboriginal Australians, which suggests a shared oral microbiome signature. Further studies will be needed to confirm and explore this possible regional-specific oral microbiome profile.

Abstract

Abstract
Bin picking is a challenging problem that involves using a robotic manipulator to remove, one-by-one, a set of objects randomly stacked in a container. When the objects are prone to entanglement, having an estimation of their pose and shape is highly valuable for more reliable grasp and motion planning. This paper focuses on modeling entangled tubes with varying degrees of curvature. An unconventional machine learning technique, Inductive Logic Programming (ILP), is used to construct sets of rules (theories) capable of modeling multiple tubes when given the cylinders that constitute them. Datasets of entangled tubes are created via simulation in Gazebo. Experiments using Aleph and SWI-Prolog illustrate how ILP can build explainable theories with a high performance, using a relatively small dataset and low amount of time for training. Therefore, this work serves as a proof-of-concept that ILP is a valuable method to acquire knowledge and validate heuristics for pose and shape estimation in complex bin picking scenarios.