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„from stable to table“
(2008)
The Global Compact for Safe, Orderly and Regular Migration defines Global Skill Partnerships (GSP) as an innovative means of strengthen skills development among origin countries and countries of destination in mutually beneficial manner. However, GSPs are very limited in number and scope, and empirical analyses of them are, to date, relatively rare. This study helps fill this gap in data by presenting and examining existing GSPs or GSP-like approaches (e.g., transnational training partnerships). The aim of the study is to take stock of the various conceptual discourses on and practical experience with transnational skill partnerships. Using Kosovo as a case study, the study details the structure of such partnerships and the processes they entail. It documents the experience of those involved and catalogues the factors contributing to success. On this basis, the authors propose a means of categorizing the various practices that will help structure the empirical diversity of such approaches and render them conceptually feasible: Transnational Skills and Mobility Partnerships (TSMP).
Neuromorphic computing aims to mimic the computational principles of the brain in silico and has motivated research into event-based vision and spiking neural networks (SNNs). Event cameras (ECs) capture local, independent changes in brightness, and offer superior power consumption, response latencies, and dynamic ranges compared to frame-based cameras. SNNs replicate neuronal dynamics observed in biological neurons and propagate information in sparse sequences of ”spikes”. Apart from biological fidelity, SNNs have demonstrated potential as an alternative to conventional artificial neural networks (ANNs), such as in reducing energy expenditure and inference time in visual classification. Although potentially beneficial for robotics, the novel event-driven and spike-based paradigms remain scarcely explored outside the domain of aerial robots.
To investigate the utility of brain-inspired sensing and data processing in a robotics application, we developed a neuromorphic approach to real-time, online obstacle avoidance on a manipulator with an onboard camera. Our approach adapts high-level trajectory plans with reactive maneuvers by processing emulated event data in a convolutional SNN, decoding neural activations into avoidance motions, and adjusting plans in a dynamic motion primitive formulation. We conducted simulated and real experiments with a Kinova Gen3 arm performing simple reaching tasks involving static and dynamic obstacles. Our implementation was systematically tuned, validated, and tested in sets of distinct task scenarios, and compared to a non-adaptive baseline through formalized quantitative metrics and qualitative criteria.
The neuromorphic implementation facilitated reliable avoidance of imminent collisions in most scenarios, with 84% and 92% median success rates in simulated and real experiments, where the baseline consistently failed. Adapted trajectories were qualitatively similar to baseline trajectories, indicating low impacts on safety, predictability and smoothness criteria. Among notable properties of the SNN were the correlation of processing time with the magnitude of perceived motions (captured in events) and robustness to different event emulation methods. Preliminary tests with a DAVIS346 EC showed similar performance, validating our experimental event emulation method. These results motivate future efforts to incorporate SNN learning, utilize neuromorphic processors, and target other robot tasks to further explore this approach.
The increasing ubiquity of Artificial Intelligence (AI) poses significant political consequences. The rapid proliferation of AI over the past decade has prompted legislators and regulators to attempt to contain AI’s technological consequences. For Germany, relevant design requirements have been expressed by the European Commission’s High-Level Expert Group on Artificial Intelligence (HLEG AI), and, at the national level, by the German government’s Data Ethics Commission (DEK) as well as the German Bundestag’s Commission of Inquiry on Artificial Intelligence (EKKI).
In the field of automatic music generation, one of the greatest challenges is the consistent generation of pieces continuously perceived positively by the majority of the audience since there is no objective method to determine the quality of a musical composition. However, composing principles, which have been refined for millennia, have shaped the core characteristics of today's music. A hybrid music generation system, mlmusic, that incorporates various static, music-theory-based methods, as well as data-driven, subsystems, is implemented to automatically generate pieces considered acceptable by the average listener. Initially, a MIDI dataset, consisting of over 100 hand-picked pieces of various styles and complexities, is analysed using basic music theory principles, and the abstracted information is fed into explicitly constrained LSTM networks. For chord progressions, each individual network is specifically trained on a given sequence length, while phrases are created by consecutively predicting the notes' offset, pitch and duration. Using these outputs as a composition's foundation, additional musical elements, along with constrained recurrent rhythmic and tonal patterns, are statically generated. Although no survey regarding the pieces' reception could be carried out, the successful generation of numerous compositions of varying complexities suggests that the integration of these fundamentally distinctive approaches might lead to success in other branches.
This report has been prepared by the SETAC Europe Scientific Task Group on Global And RegionaL Impact Categories (SETAC-Europe/STG-GARLIC) that is installed by the 2nd SETAC Europe working group on life cycle impact assessment (WIA-2). This document is background to a chapter written by the same authors under the title “Climate change, stratospheric ozone depletion, photo-oxidant formation, acidification and eutrophication” in Udo de Haes et al. (2002). The chapter summarises the work of the STG-GARLIC and aims to give a state-of-the-art review of the best available practice(s) regarding category indicators and lists of concomitant characterisation factors for climate change, stratospheric ozone depletion, photo-oxidant formation, acidification, and aquatic and terrestrial eutrophication. Backgrounds on each of the specific impact categories are given in another background report from Klöpffer and Potting (2001).
This background report provides details on a selection of general issues relevant in relation to LCA and characterisation of impact in LCA. The document starts with a short introduction of the LCA methodology and impact assessment in LCA for non LCA-experts. LCA experts, on the other hand, will usually not be familiar in-depth with scientific and political backgrounds of the specific impact categories. A review of this is given. Also the discussion is provided about the issue of the position of the category indicator in the causality chain, and into the related issue of spatial differentiation. These two issues appeared to be one of the core items for SETAC-Europe/STG-GARLIC.
Effective Neighborhood Feature Exploitation in Graph CNNs for Point Cloud Object-Part Segmentation
(2022)
Part segmentation is the task of semantic segmentation applied on objects and carries a wide range of applications from robotic manipulation to medical imaging. This work deals with the problem of part segmentation on raw, unordered point clouds of 3D objects. While pioneering works on deep learning for point clouds typically ignore taking advantage of local geometric structure around individual points, the subsequent methods proposed to extract features by exploiting local geometry have not yielded significant improvements either. In order to investigate further, a graph convolutional network (GCN) is used in this work in an attempt to increase the effectiveness of such neighborhood feature exploitation approaches. Most of the previous works also focus only on segmenting complete point cloud data. Considering the impracticality of such approaches, taking into consideration the real world scenarios where complete point clouds are scarcely available, this work proposes approaches to deal with partial point cloud segmentation.
In the attempt to better capture neighborhood features, this work proposes a novel method to learn regional part descriptors which guide and refine the segmentation predictions. The proposed approach helps the network achieve state-of-the-art performance of 86.4% mIoU on the ShapeNetPart dataset for methods which do not use any preprocessing techniques or voting strategies. In order to better deal with partial point clouds, this work also proposes new strategies to train and test on partial data. While achieving significant improvements compared to the baseline performance, the problem of partial point cloud segmentation is also viewed through an alternate lens of semantic shape completion.
Semantic shape completion networks not only help deal with partial point cloud segmentation but also enrich the information captured by the system by predicting complete point clouds with corresponding semantic labels for each point. To this end, a new network architecture for semantic shape completion is also proposed based on point completion network (PCN) which takes advantage of a graph convolution based hierarchical decoder for completion as well as segmentation. In addition to predicting complete point clouds, results indicate that the network is capable of reaching within a margin of 5% to the mIoU performance of dedicated segmentation networks for partial point cloud segmentation.
Recent advances in Natural Language Processing have substantially improved contextualized representations of language. However, the inclusion of factual knowledge, particularly in the biomedical domain, remains challenging. Hence, many Language Models (LMs) are extended by Knowledge Graphs (KGs), but most approaches require entity linking (i.e., explicit alignment between text and KG entities). Inspired by single-stream multimodal Transformers operating on text, image and video data, this thesis proposes the Sophisticated Transformer trained on biomedical text and Knowledge Graphs (STonKGs). STonKGs incorporates a novel multimodal architecture based on a cross encoder that uses the attention mechanism on a concatenation of input sequences derived from text and KG triples, respectively. Over 13 million so-called text-triple pairs, coming from PubMed and assembled using the Integrated Network and Dynamical Reasoning Assembler (INDRA), were used in an unsupervised pre-training procedure to learn representations of biomedical knowledge in STonKGs. By comparing STonKGs to an NLP- and a KG-baseline (operating on either text or KG data) on a benchmark consisting of eight fine-tuning tasks, the proposed knowledge integration method applied in STonKGs was empirically validated. Specifically, on tasks with a comparatively small dataset size and a larger number of classes, STonKGs resulted in considerable performance gains, beating the F1-score of the best baseline by up to 0.083. Both the source code as well as the code used to implement STonKGs are made publicly available so that the proposed method of this thesis can be extended to many other biomedical applications.
High-dimensional and multi-variate data from dynamical systems such as turbulent flows and wind turbines can be analyzed with deep learning due to its capacity to learn representations in lower-dimensional manifolds. Two challenges of interest arise from data generated from these systems, namely, how to anticipate wind turbine failures and how to better understand air flow through car ventilation systems. There are deep neural network architectures that can project data into a lower-dimensional space with the goal of identifying and understanding patterns that are not distinguishable in the original dimensional space. Learning data representations in lower dimensions via non-linear mappings allows one to perform data compression, data clustering (for anomaly detection), data reconstruction and synthetic data generation.
In this work, we explore the potential that variational autoencoders (VAE) have to learn low-dimensional data representations in order to tackle the problems posed by the two dynamical systems mentioned above. A VAE is a neural network architecture that combines the mechanisms of the standard autoencoder and variational bayes. The goal here is to train a neural network to minimize a loss function defined by a reconstruction term together with a variational term defined as a Kulback-Leibler (KL) divergence.
The report discusses the results obtained for the two different data domains: wind turbine time series and turbulence data from computational fluid dynamics (CFD) simulations.
We report on the reconstruction, clustering and unsupervised anomaly detection of wind turbine multi-variate time series data using a variant of a VAE called Variational Recurrent Autoencoder (VRAE). We trained a VRAE to cluster normal and abnormal wind turbine series (two class problem) as well as normal and multiple abnormal series (multi-class problem). We found that the model is capable of distinguishing between normal and abnormal cases by reducing the dimensionality of the input data and projecting it to two dimensions using techniques such as Principal Component Analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). A set of anomaly scoring methods is applied on top of these latent vectors in order to compute unsupervised clustering. We have achieved an accuracy of up to 96% with the KM eans + + algorithm.
We also report the data reconstruction and generation results of two dimensional turbulence slices corresponding to CFD simulation of a HVAC air duct. For this, we have trained a Convolutional Variational Autoencoder (CVAE). We have found that the model is capable of reconstructing laminar flows up to a certain degree of resolution as well generating synthetic turbulence data from the learned latent distribution.
A Comparative Study of Uncertainty Estimation Methods in Deep Learning Based Classification Models
(2020)
Deep learning models produce overconfident predictions even for misclassified data. This work aims to improve the safety guarantees of software-intensive systems that use deep learning based classification models for decision making by performing comparative evaluation of different uncertainty estimation methods to identify possible misclassifications.
In this work, uncertainty estimation methods applicable to deep learning models are reviewed and those which can be seamlessly integrated to existing deployed deep learning architectures are selected for evaluation. The different uncertainty estimation methods, deep ensembles, test-time data augmentation and Monte Carlo dropout with its variants, are empirically evaluated on two standard datasets (CIFAR-10 and CIFAR-100) and two custom classification datasets (optical inspection and RoboCup@Work dataset). A relative ranking between the methods is provided by evaluating the deep learning classifiers on various aspects such as uncertainty quality, classifier performance and calibration. Standard metrics like entropy, cross-entropy, mutual information, and variance, combined with a rank histogram based method to identify uncertain predictions by thresholding on these metrics, are used to evaluate uncertainty quality.
The results indicate that Monte Carlo dropout combined with test-time data augmentation outperforms all other methods by identifying more than 95% of the misclassifications and representing uncertainty in the highest number of samples in the test set. It also yields a better classifier performance and calibration in terms of higher accuracy and lower Expected Calibration Error (ECE), respectively. A python based uncertainty estimation library for training and real-time uncertainty estimation of deep learning based classification models is also developed.
Human and robot tasks in household environments include actions such as carrying an object, cleaning a surface, etc. These tasks are performed by means of dexterous manipulation, and for humans, they are straightforward to accomplish. Moreover, humans perform these actions with reasonable accuracy and precision but with much less energy and stress on the actuators (muscles) than the robots do. The high agility in controlling their forces and motions is actually due to "laziness", i.e. humans exploit the existing natural forces and constraints to execute the tasks.
The above-mentioned properties of the human lazy strategy motivate us to relax the problem of controlling robot motions and forces, and solve it with the help of the environment. Therefore, in this work, we developed a lazy control strategy, i.e. task specification models and control architectures that relax several aspects of robot control by exploiting prior knowledge about the task and environment. The developed control strategy is realized in four different robotics use cases. In this work, the Popov-Vereshchagin hybrid dynamics solver is used as one of the building blocks in the proposed control architectures. An extension of the solver’s interface with the artificial Cartesian force and feed-forward joint torque task-drivers is proposed in this thesis.
To validate the proposed lazy control approach, an experimental evaluation was performed in a simulation environment and on a real robot platform.
This work provides a short but technical introduction to the main building blocks of a blockchain. It argues that a blockchain is not a revolutionary technology but rather a clever combination of three fields: cryptography, decentralization and game theory. In addition, it summaries the differences between a public, private and federate blockchain model and the two prominent consensus mechanism Proof-of-Work (POW) and Proof-of-Stake (POS).
An essential measure of autonomy in service robots designed to assist humans is adaptivity to the various contexts of human-oriented tasks. These robots may have to frequently execute the same action, but subject to subtle variations in task parameters that determine optimal behaviour. Such actions are traditionally executed by robots using pre-determined, generic motions, but a better approach could utilize robot arm maneuverability to learn and execute different trajectories that work best in each context.
In this project, we explore a robot skill acquisition procedure that allows incorporating contextual knowledge, adjusting executions according to context, and improvement through experience, as a step towards more adaptive service robots. We propose an apprenticeship learning approach to achieving context-aware action generalisation on the task of robot-to-human object hand-over. The procedure combines learning from demonstration, with which a robot learns to imitate a demonstrator’s execution of the task, and a reinforcement learning strategy, which enables subsequent experiential learning of contextualized policies, guided by information about context that is integrated into the learning process. By extending the initial, static hand-over policy to a contextually adaptive one, the robot derives and executes variants of the demonstrated action that most appropriately suit the current context. We use dynamic movement primitives (DMPs) as compact motion representations, and a model-based Contextual Relative Entropy Policy Search (C-REPS) algorithm for learning policies that can specify hand-over position, trajectory shape, and execution speed, conditioned on context variables. Policies are learned using simulated task executions, before transferring them to the robot and evaluating emergent behaviours.
We demonstrate the algorithm’s ability to learn context-dependent hand-over positions, and new trajectories, guided by suitable reward functions, and show that the current DMP implementation limits learning context-dependent execution speeds. We additionally conduct a user study involving participants assuming different postures and receiving an object from the robot, which executes hand-overs by either exclusively imitating a demonstrated motion, or selecting hand-over positions based on learned contextual policies and adapting its motion accordingly. The results confirm the hypothesized improvements in the robot’s perceived behaviour when it is context-aware and adaptive, and provide useful insights that can inform future developments.
Currently, a variety of methods exist for creating different types of spatio-temporal world models. Despite the numerous methods for this type of modeling, there exists no methodology for comparing the different approaches or their suitability for a given application e.g. logistics robots. In order to establish a means for comparing and selecting the best-fitting spatio-temporal world modeling technique, a methodology and standard set of criteria must be established. To that end, state-of-the-art methods for this type of modeling will be collected, listed, and described. Existing methods used for evaluation will also be collected where possible.
Using the collected methods, new criteria and techniques will be devised to enable the comparison of various methods in a qualitative manner. Experiments will be proposed to further narrow and ultimately select a spatio-temporal model for a given purpose. An example network of autonomous logistic robots, ROPOD, will serve as a case study used to demonstrate the use of the new criteria. This will also serve to guide the design of future experiments that aim to select a spatio-temporal world modeling technique for a given task. ROPOD was specifically selected as it operates in a real-world, human shared environment. This type of environment is desirable for experiments as it provides a unique combination of common and novel problems that arise when selecting an appropriate spatio-temporal world model. Using the developed criteria, a qualitative analysis will be applied to the selected methods to remove unfit options.
Then, experiments will be run on the remaining methods to provide comparative benchmarks. Finally, the results will be analyzed and recommendations to ROPOD will be made.
Multi-robot systems (MRS) are capable of performing a set of tasks by dividing them among the robots in the fleet. One of the challenges of working with multirobot systems is deciding which robot should execute each task. Multi-robot task allocation (MRTA) algorithms address this problem by explicitly assigning tasks to robots with the goal of maximizing the overall performance of the system. The indoor transportation of goods is a practical application of multi-robot systems in the area of logistics. The ROPOD project works on developing multi-robot system solutions for logistics in hospital facilities. The correct selection of an MRTA algorithm is crucial for enhancing transportation tasks. Several multi-robot task allocation algorithms exist in the literature, but just few experimental comparative analysis have been performed. This project analyzes and assesses the performance of MRTA algorithms for allocating supply cart transportation tasks to a fleet of robots. We conducted a qualitative analysis of MRTA algorithms, selected the most suitable ones based on the ROPOD requirements, implemented four of them (MURDOCH, SSI, TeSSI, and TeSSIduo), and evaluated the quality of their allocations using a common experimental setup and 10 experiments. Our experiments include off-line and semi on-line allocation of tasks as well as scalability tests and use virtual robots implemented as Docker containers. This design should facilitate deployment of the system on the physical robots. Our experiments conclude that TeSSI and TeSSIduo suit best the ROPOD requirements. Both use temporal constraints to build task schedules and run in polynomial time, which allow them to scale well with the number of tasks and robots. TeSSI distributes the tasks among more robots in the fleet, while TeSSIduo tends to use a lower percentage of the available robots.
Subsequently, we have integrated TeSSI and TeSSIduo to perform multi-robot task allocation for the ROPOD project.
In this paper, we provide a participatory design study of a mobile health platform for older adults that provides an integrative perspective on health data collected from different devices and apps. We illustrate the diversity and complexity of older adults’ perspectives in the context of health and technology use, the challenges which follow on for the design of mobile health platforms that support active and healthy ageing (AHA) and our approach to addressing these challenges through a participatory design (PD) process. Interviews were conducted with older adults aged 65+ in a two-month study with the goal of understanding perspectives on health and technologies for AHA support. We identified challenges and derived design ideas for a mobile health platform called “My-AHA”. For researchers in this field, the structured documentation of our procedures and results, as well as the implications derived provide valuable insights for the design of mobile health platforms for older adults.
Designing consumption feedback to support sustainable behavior is an active research topic. In recent years, relevant work has suggested a variety of possible design strategies. Addressing the more recent developments in this field, this paper presents a structured literature review, providing an overview of current information design approaches and highlighting open research questions. We suggest a literature-based taxonomy of used strategies, data source and output media with a special focus on design. In particular, we analyze which visual forms are used in current research to reach the identified strategy goals. Our survey reveals that the trend is towards more complex and contextualized feedback and almost every design within sustainable HCI adopts common visualization forms. Furthermore, adopting more advanced visual forms and techniques from information visualization research is helpful when dealing with ever-increasing data sources at home. Yet so far, this combination has often been neglected in feedback design.
Education is widely seen as an important means of addressing both national and international problems, such as political or religious extremism, poverty, and hunger. If publicly available educational resources (OERs) shall help overcoming the educational gap, localization is one of the major issues we need to deal with. Educators as well as learners need to be supported to determine adaptation needs. This paper provides a list of possible in-fluence factors on educational scenarios which are defined as context metadata. In the given form, the list needs to be understood as an addendum for the paper entitled ‘Open Educational Resources: Education for the World?’ from Thomas richter and Maggie McPherson; It is being published in the volume 3, issue 2 of the Journal Distance Education in 2012.
This study presents the findings of a quantitative study on the use of Open Educational Resources (OER) and Open Educational Practices (OEP) in Higher Education and Adult Learning Institutions. The study is based on the results of an online survey targeted at four educational roles: educational policy makers; institutional policy makers/managers; educational professionals; and learners. The report encompasses five chapters and four annexes. Chapter I presents the survey and Chapter II discloses the main research questions and models. Chapter III characterises the universe of respondents. Chapter IV advances with a detailed survey analysis including an overview of key statistical data. Finally, Chapter V provides an exploratory in-depth analysis of some key issues: representations, attitudes and uses of OEP. The table of contents and the complete list of diagrams and tables can be found at the end of the report.
The roadmap for quality and innovation through open educational practices has been conceived as a number of steps, a conceptual document, which can be used by organisations, leaners or professionals in order to improve their open educational practices. After the development of the core concept of the OPAL project, the guidelines for OEP, it became clear that these guidelines, would have to play an important part on the roadmap exercise, because they represent the very essence of how to foster and stimulate open educational practices. The roadmap therefore is meant to be an instrument, a tool which helps the different stakeholders to use the guidelines for their own context and purpose.