knowlEdge Project –Concept, methodology and innovations for artificial intelligence in industry 4.0

AI is one of the biggest megatrends towards the 4th industrial revolution. Although these technologies promise business sustainability as well as product and process quality, it seems that the ever-changing market demands, the complexity of technologies and fair concerns about privacy, impede broad application and reuse of Artificial Intelligence (AI) models across the industry. To break the entry barriers for these technologies and unleash its full potential, the knowlEdge project will develop a new generation of AI methods, systems, and data management infrastructure. Subsequently, as part of the knowlEdge project we propose several major innovations in the areas of data management, data analytics and knowledge management including (i) a set of AI services that allows the usage of edge deployments as computational and live data infrastructure as well as a continuous learning execution pipeline on the edge, (ii) a digital twin of the shop-floor able to test AI models, (iii) a data management framework deployed along the edge-to-cloud continuum ensuring data quality, privacy and confidentiality, (iv) Human-AI Collaboration and Domain Knowledge Fusion tools for domain experts to inject their experience into the system, (v) a set of standardisation mechanisms for the exchange of trained AI models from one context to another, and (vi) a knowledge marketplace platform to distribute and interchange trained AI models. In this paper, we present a short overview of the EU Project knowlEdge –Towards Artificial Intelligence powered manufacturing services, processes, and products in an edge-to-cloud-knowledge continuum for humans [in-the-loop], which is funded by the Horizon 2020 (H2020) Framework Programme of the European Commission under Grant Agreement 957331. Our overview includes a description of the project’s main concept and methodology as well as the envisioned innovations.The research leading to these results has received funding from the Horizon 2020 Programme of the European Commission under Grant Agreement No. 957331 for EU Project knowlEdge –Towards Artificial Intelligence powered manufacturing services, processes, and products in an edge-to-cloud-knowledge continuum for humans [in-the-loop].Peer ReviewedTreball signat per 21 autors/autores: Sergio Alvarez-Napagao, Barcelona Supercomputing Center, Spain; Boki Ashmore, ICE, United Kingdom; Marta Barroso, Barcelona Supercomputing Center, Spain; Cristian Barrué, Barcelona Supercomputing Center, Spain; Christian Beecks, University of Münster, Germany; Fabian Berns, University of Münster, Germany; Ilaria Bosi, LINKS Foundation, Italy; Sisay Adugna Chala, Fraunhofer FIT, Germany; Nicola Ciulli, Nextworks, Italy; Marta Garcia-Gasulla, Barcelona Supercomputing Center, Spain; Alexander Grass, Fraunhofer FIT, Germany; Dimosthenis Ioannidis, CERTH/ITI, Greece; Natalia Jakubiak, Universitat Politècnica de Catalunya, Spain; Karl Köpke, Kautex Textron, Germany; Ville Lämsä, VTT Technical Research Centre, Finland; Pedro Megias, Barcelona Supercomputing Center, Spain; Alexandros Nizamis, CERTH/ITI, Greece; Claudio Pastrone, LINKS Foundation, Italy; Rosaria Rossini, LINKS Foundation, Italy; Miquel Sànchez-Marrè, Universitat Politècnica de Catalunya, Spain; Luca Ziliotti, Parmalat, ItalyPostprint (author's final draft

A Variable Stiffness Actuator Module With Favorable Mass Distribution for a Bio-inspired Biped Robot

Achieving human-like locomotion with humanoid platforms often requires the use of variable stiffness actuators (VSAs) in multi-degree-of-freedom robotic joints. VSAs possess 2 motors for the control of both stiffness and equilibrium position. Hence, they add mass and mechanical complexity to the design of humanoids. Mass distribution of the legs is an important design parameter, because it can have detrimental effects on the cost of transport. This work presents a novel VSA module, designed to be implemented in a bio-inspired humanoid robot, Binocchio, that houses all components on the same side of the actuated joint. This feature allowed to place the actuator's mass to more proximal locations with respect to the actuated joint instead of concentrating it at the joint level, creating a more favorable mass distribution in the humanoid. Besides, it also facilitated it's usage in joints with centralized multi-degree of freedom (DoF) joints instead of cascading single DoF modules. The design of the VSA module is presented, including it's integration in the multi-DoFs joints of Binocchio. Experiments validated the static characteristics of the VSA module to accurately estimate the output torque and stiffness. The dynamic responses of the driving and stiffening mechanisms are shown. Finally, experiments show the ability of the actuation system to replicate the envisioned human-like kinematic, torque and stiffness profiles for Binocchio

A Systematic Testing Approach for Autonomous Mobile Robots Using Domain-Specific Languages

Abstract. One aspect often neglected during the development of autonomous mobile robots is the systematic validation of their overall behavior. Especially large robots applied to real-world scenarios may cause injuries or even human death and must therefore be classified as safety-critical. In this paper, a generic approach to defining and executing purposeful test runs using domain-specific languages (dsls) is presented. Test cases can be defined in an appropriate test description language (first dsl). These test cases can be derived automatically using a modelbased testing approach, for which a test model has to be created. Hence, aseconddsl for the creation of the test model is presented. It is further shown how the generated test cases are automatically executed and evaluated. The paper concludes with the application of the approach to the autonomous off-road robot ravon.

V3C1 Dataset: An Evaluation of Content Characteristics

In this work we analyze content statistics of the V3C1 dataset, which is the first partition of the Vimeo Creative Commons Collection (V3C). The dataset has been designed to represent true web videos in the wild, with good visual quality and diverse content characteristics, and will serve as evaluation basis for the Video Browser Showdown 2019-2021 and TREC Video Retrieval (TRECVID) Ad-Hoc Video Search tasks 2019-2021. The dataset comes with a shot segmentation (around 1 million shots) for which we analyze content specifics and statistics. Our research shows that the content of V3C1 is very diverse, has no predominant characteristics and provides a low self-similarity. Thus it is very well suited for video retrieval evaluations as well as for participants of TRECVID AVS or the VBS

V3C1 Dataset: An Evaluation of Content Characteristics

In this work we analyze content statistics of the V3C1 dataset, which is the first partition of the Vimeo Creative Commons Collection (V3C). The dataset has been designed to represent true web videos in the wild, with good visual quality and diverse content characteristics, and will serve as evaluation basis for the Video Browser Showdown 2019-2021 and TREC Video Retrieval (TRECVID) Ad-Hoc Video Search tasks 2019-2021. The dataset comes with a shot segmentation (around 1 million shots) for which we analyze content specifics and statistics. Our research shows that the content of V3C1 is very diverse, has no predominant characteristics and provides a low self-similarity. Thus it is very well suited for video retrieval evaluations as well as for participants of TRECVID AVS or the VBS

The V3C1 Dataset: Advancing the State of the Art in Video Retrieval

Standardized datasets are of vital importance in multimedia research, as they form the basis for reproducible experiments and evaluations. In the area of video retrieval, widely used datasets such as the IACC, which has formed the basis for the TRECVID Ad-Hoc Video Search Task and other retrieval-related challenges, have started to show their age. For example, IACC is no longer representative of video content as it is found in the wild. This is illustrated by the figures below, showing the distribution of video age and duration across various datasets in comparison with a sample drawn from Vimeo and Youtube

Miletus Documentation Manual: Surveying, Forms, Find Processing and Database-Usage, v2.0

This manual has been created in the course of the DFG and ANR funded project 'Forms of Life in the Megapolis: Miletus in the Longue Durée' (MegaMil) led by Prof. Dr. Christof Berns (UHH) and Prof. Dr. Julien Zurbach (ENS). In the course of this project the previously used Milesian documentation system was comprehensively modernized and adapted to the use of digital tools. The explanations and guidelines in this document serve as a 'documentation of the documentation' and are intended to help staff members use the system in a consistent manner across multiple projects, as well as preserve our methods of working and organizing for future generations of archaeologists and transparently explain our research methods to the public. The manual aims to facilitate the use and to provide an overview of the data structure for long-term archiving and cooperation. The manual is published in German and English, while the corresponding forms are available alongside it in German, English and French. We provide several additional resources along with the digital version of this manual. While the excavation is mainly organized by three universities (University of Hamburg, Mimar Sinan Güzel Sanatlar Üniversitesi in Istanbul and Ecole Normale Supérieure Paris), we also want to thank our many cooperation partners listed in the preface without whom none of our work would be possible. Please note: While the whole manual and documentation are a group effort, and many more people have contributed to this project with their time, ideas and corrections, people responsible for a section of the current or previous version of this manual have been listed in this repository as authors. For details, see the 'List of Authors' in the preface. Authors may change with different versions of the manual

A Variable Stiffness Actuator Module with Favorable Mass Distribution for a Bio-inspired Biped Robot

Achieving human-like locomotion with humanoid platforms often requires the use of variable stiffness actuators (VSAs) in multi-degree-of-freedom robotic joints. VSAs possess 2 motors for the control of both stiffness and equilibrium position. Hence, they add mass and mechanical complexity to the design of humanoids. Mass distribution of the legs is an important design parameter, because it can have detrimental effects on the cost of transport. This work presents a novel VSA module, designed to be implemented in a bio-inspired humanoid robot, Binocchio, that houses all components on the same side of the actuated joint. This feature allowed to place the actuator’s mass to more proximal locations with respect to the actuated joint instead of concentrating it at the joint level, creating a more favorable mass distribution in the humanoid. Besides, it also facilitated it’s usage in joints with centralized multi-degree of freedom (DoF) joints instead of cascading single DoF modules. The design of the VSA module is presented, including it’s integration in the multi-DoFs joints of Binocchio. Experiments validated the static characteristics of the VSA module to accurately estimate the output torque and stiffness. The dynamic responses of the driving and stiffening mechanisms are shown. Finally, experiments show the ability of the actuation system to replicate the envisioned human-like kinematic, torque and stiffness profiles for Binocchio