STEAM: A Platform for Scalable Spatiotemporal Analytics

Spatiotemporal datasets have become increasingly available with the introduction of a various set of applications and services trac- ing the behavior of moving objects. Recently, there has been a high demand in understanding these datasets using spatiotemporal analytics. While being considered of high value, spatiotemporal analytics did not yet see a wide spreading into the actual business workflow or the direct configuration of services and applications. The computational complexity for spatiotemporal datasets and the heterogeneity of data sources are considered key factors for the current state. This paper introduces STEAM, a platform for distributed spatiotemporal analytics on heterogeneous spatiotemporal datasets. STEAM introduces a framework that abstracts the key components from incoming spatiotemporal datasets that originate from various positioning systems. This abstraction provides a common base for distributed and scalable analytics methods that is not bound to a specific underlying positioning technique. STEAM provides a distributed state-of-the-art implementation and is evaluated on a multi-machine testbed for linear scalability.BMBF, 01IS12056, Software Campus (TU Berlin

A Scalable Consent, Transparency and Compliance Architecture

In this demo we present the SPECIAL consent, transparency and compliance system. The objective of the system is to afford data subjects more control over personal data processing and sharing, while at the same time enabling data controllers and processors to comply with consent and transparency obligations mandated by the European General Data Protection Regulation. A short promotional video can be found at https://purl.com/specialprivacy/demos/ESWC2018

Carbon nanotubes and graphene oxide applications in optochemical sensors

Simple and sensitive ethanol sensors based on uncladded plastic optical fiber (UCPOF) and tapered silica fiber coated with carbon nanotubes (CNT) and graphene oxide (GO) thin films have been developed for detecting different ethanol concentrations at room temperature. Simple and low-cost techniques were used for the fabrication of the proposed optical fiber sensors. The developed probes were coated with CNT and GO using drop-casting and dip-coating techniques. Experiments demonstrated that the CNT-based UCPOF and GO-based tapered fiber tip sensors show significant sensitivity to aqueous ethanol with superior selectivity to ethanol among different organic compounds. CNT-based UCPOF sensor shows better sensing performance compared with the GO-based tapered fiber because of the absorbance sensing mechanism. The excellent sensing performance of the developed modified optical fiber sensors toward ethanol indicates their high efficiency to be used in practical applications for ensuring safety necessities in the industrial fields

Inertial properties of the German Shepherd Dog.

One of the most popular dog breeds deployed by both the police and military has been the German Shepherd yet little is known about the morphology or body segment parameters of this breed. Such measures are essential for developing biomechanical models which, in turn, may guide clinicians in developing surgical interventions, injury treatment and prevention procedures. This paper provides a complete set of body segment parameters and inertial properties for the German Shepherd. Morphometric measures and 3-dimensional inertial properties, including mass, centre of mass, moment of inertia and volume, were measured from 17 segments from 6 German Shepherd police service dog cadavers. Using whole body mass and geometric modelling, 11 regression equations were developed for predicting segment masses, and 33 equations were developed for predicting moments of inertia. Using these data, inverse dynamic analyses may be applied in future investigations of canine mechanics, guiding surgical procedures, rehabilitation and training especially for the German Shepherd breed but potentially for other breeds too

Galaxy-Classification Activity for All Ages

Classification is a general tool of science; it is used to sort and categorize biological organisms, chemical elements, astronomical objects, and many other things. In scientific classification, taxonomy often reflects shared physical properties that, in turn, may indicate shared origins and/or evolution. A “hands-on” galaxy-classification activity developed and implemented by Professional Development Program (PDP) participants, for a high-school summer STEM enrichment program, has been adopted for various age groups and venues, from young (K–3) to college students. We detail the basic tools required, outline the general activity, and describe the modifications to the activity based on learners’ ages and learning objectives. We describe the facilitation strategies learned through PDP training and used when implementing the activity, including prompts to motivate the students. We also discuss how we connected the classification process to astronomy and science more broadly during the concluding remarks