On Detecting Concurrency Defects Automatically at the Design Level

We describe an automated approach for detecting concurrency defects from design diagrams of a software, in particular, sequence diagrams. From a given sequence diagram, we automatically infer a formal, parallel specification that generalizes the communication behavior that is designed informally and incompletely in the diagram. We model-check the parallel specification against generic concurrency defect patterns. No additional specification of the software is needed. We present several case-studies to evaluate our approach. The results show that our approach is technically feasible, and effective in detecting nasty concurrency defects at the design level

A Framework for Coupled Simulations of Robots and Spiking Neuronal Networks

Bio-inspired robots still rely on classic robot control although advances in neurophysiology allow adaptation to control as well. However, the connection of a robot to spiking neuronal networks needs adjustments for each purpose and requires frequent adaptation during an iterative development. Existing approaches cannot bridge the gap between robotics and neuroscience or do not account for frequent adaptations. The contribution of this paper is an architecture and domain-specific language (DSL) for connecting robots to spiking neuronal networks for iterative testing in simulations, allowing neuroscientists to abstract from implementation details. The framework is implemented in a web-based platform. We validate the applicability of our approach with a case study based on image processing for controlling a four-wheeled robot in an experiment setting inspired by Braitenberg vehicles

Anonymität und Mobilität - Whitepaper zum Begriffs- und Domänenverständnis des Kompetenzcluster ANYMOS – Anonymisierung für vernetzte Mobilitätssysteme

In ANYMOS werden Anforderungen und Methoden für eine Anonymisierung und anschließende Auswertung von zuvor personenbezogenen Daten untersucht. Dabei wird im Kompetenzcluster die Anwendungsdomäne Mobilität betrachtet und sich auf den Personenverkehr, da durch die Mobilität von Gütern nicht immer unmittelbar personenbezogene Daten anfallen, fokussiert. Die Notwendigkeit des Kompetenzclusters ANYMOS ergibt sich daraus, dass im Mobilitätsbereich bei zahlreichen Anwendungen große Datenmengen anfallen und es aufgrund der zu erwartenden Entwicklungen zu einem weiteren Anstieg dieser Datenmenge kommen wird. Um diese Daten in Zukunft sinnvoll nutzen zu können, ohne dabei durch die Verwendung personenbezogener Daten Persönlichkeitsrechte und/oder rechtliche Vorgaben zu verletzen, muss zunächst erforscht werden, wann diese Daten gesammelt werden und inwieweit sie auch nach einer Anonymisierung noch über einen Nutzwert verfügen. Im zweiten Abschnitt des Whitepapers wird daher zunächst Anonymität beschrieben und das Spannungsfeld zwischen juristischem und technischen Begriffsverständnis erörtert. Im dritten Abschnitt erfolgt eine Strukturierung der Mobilitätsdomäne. Dadurch soll das gemeinsame Verständnis der Begrifflichkeiten und der Relevanz der verschiedenen Themenbereiche für das Kompetenzcluster ANYMOS gefördert werden. Abschließend wird ein Ausblick – auch auf die weiteren Arbeiten in ANYMOS gegeben

Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform

Combined efforts in the fields of neuroscience, computer science, and biology allowed to design biologically realistic models of the brain based on spiking neural networks. For a proper validation of these models, an embodiment in a dynamic and rich sensory environment, where the model is exposed to a realistic sensory-motor task, is needed. Due to the complexity of these brain models that, at the current stage, cannot deal with real-time constraints, it is not possible to embed them into a real-world task. Rather, the embodiment has to be simulated as well. While adequate tools exist to simulate either complex neural networks or robots and their environments, there is so far no tool that allows to easily establish a communication between brain and body models. The Neurorobotics Platform is a new web-based environment that aims to fill this gap by offering scientists and technology developers a software infrastructure allowing them to connect brain models to detailed simulations of robot bodies and environments and to use the resulting neurorobotic systems for in silico experimentation. In order to simplify the workflow and reduce the level of the required programming skills, the platform provides editors for the specification of experimental sequences and conditions, environments, robots, and brain–body connectors. In addition to that, a variety of existing robots and environments are provided. This work presents the architecture of the first release of the Neurorobotics Platform developed in subproject 10 “Neurorobotics” of the Human Brain Project (HBP).1 At the current state, the Neurorobotics Platform allows researchers to design and run basic experiments in neurorobotics using simulated robots and simulated environments linked to simplified versions of brain models. We illustrate the capabilities of the platform with three example experiments: a Braitenberg task implemented on a mobile robot, a sensory-motor learning task based on a robotic controller, and a visual tracking embedding a retina model on the iCub humanoid robot. These use-cases allow to assess the applicability of the Neurorobotics Platform for robotic tasks as well as in neuroscientific experiments.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 (Human Brain Project) and from the European Unions Horizon 2020 Research and Innovation Programme under Grant Agreement No. 720270 (HBP SGA1)