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While prospect of tracking mobile devices' users is widely discussed all over European countries to counteract COVID-19 propagation, we propose a Bloom filter based construction providing users' location privacy and preventing mass surveillance.
We apply a solution based on Bloom filters data structure that allows a third party, a government agency, to perform some privacy-preserving set relations on a mobile telco's access logfile.
By computing set relations, the government agency, given the knowledge of two identified persons, has an instrument that provides a (possible) infection chain from the initial to the final infected user no matter at which location on a worldwide scale they are.
The benefit of our approach is that intermediate possible infected users can be identified and subsequently contacted by the agency. With such approach, we state that solely identities of possible infected users will be revealed and location privacy of others will be preserved. To this extent, it meets General Data Protection Regulation (GDPR)requirements in this area.
Should social robots become part of our society?
Embedded in an exciting crime story, the science comic by Oliver Korn and Jonas Grund conveys the current state and outlook of science on social robots.
The story takes place in the near future: In an international project, scientists are researching the requirements for social robots for the health sector. Shortly before the prototype of a nursing robot is used in a field study, it disappears without a trace. In her first major case, the young inspector Kira embarks on a journey into the world of science. In the process, she learns a lot about social robots, AI and the world of international scientific cooperation. Again and again, critical voices are heard: anti-robot activists protest against automation and a dwindling of humanity and empathy. Even in the Commissioner's family, the possible care of elderly people by robots is controversially discussed.
The overarching goal is to build knowledge across all age groups so that the advantages and disadvantages of these new technologies can be discussed controversially but knowledgeably. "Social Robots - a Science Comic" is a contribution to an informed discussion in the fields of robotics, artificial intelligence, ethics and politics and is thus also suitable as a teaching and learning tool.
The comic was conceived and designed for young people and adults. In particular, however, for those who have hardly come into contact with social robots and artificial intelligence so far. Another target group are people working in the health care sector, because the care and nursing of elderly people are considered to be one of the most important areas of application for social robots in the future.
DINA4 portrait format, hardcover thread stitching, published in German and English. Self-published by the Affective & Cognitive Institute (ACI), Offenburg University.
Restoring hand motion to people experiencing amputation, paralysis, and stroke is a critical area of research and development. While electrode-based systems that use input from the brain or muscle have proven successful, these systems tend to be expensive and di¨cult to learn. One group of researchers is exploring the use of augmented reality (AR) as a new way of controlling hand prostheses. A camera mounted on eyeglasses tracks LEDs on a prosthetic to execute opening and closing commands using one of two different AR systems. One system uses a rectangular command window to control motion: crossing horizontally signals “open” along one direction and “close” in the opposite direction. The second system uses a circular command window: once control is enabled, gripping strength can be controlled by the direction of head motion. While the visual system remains to be tested with patients, its low cost, ease of use, and lack of electrodes make the device a promising solution for restoring hand motion.
The aim of this essay is to analyse and evaluate the Italian government measures for exporters in response to COVID-19. The unexpected, rapid and hardly predictable consequences of the pandemic paralyzed the entire globe. For a long time, Italy was the epicentre of the virus, which caused severe damage in the Italian export economy dropping temporarily more than 40%. The Italian government reacted exemplary fast and took multiple countermeasures of high extent especially through the Italian export credit agency SACE. On the one hand, the internationally compared broad structure of SACE was a huge advantage, which allowed to release quickly numerous measures. On the other hand, there is room for improvement regarding the accessibility of measure-related information, which has been partially only available in Italian. Furthermore, there is a remarkable risk resulting from the combination of the high monetary effort to enable the numerous measures, the difficult financial situation of the Italian government and the unpredictability of the COVID-19 consequences.
Generative adversarial networks are the state of the art approach towards learned synthetic image generation. Although early successes were mostly unsupervised, bit by bit, this trend has been superseded by approaches based on labelled data. These supervised methods allow a much finer-grained control of the output image, offering more flexibility and stability. Nevertheless, the main drawback of such models is the necessity of annotated data. In this work, we introduce an novel framework that benefits from two popular learning techniques, adversarial training and representation learning, and takes a step towards unsupervised conditional GANs. In particular, our approach exploits the structure of a latent space (learned by the representation learning) and employs it to condition the generative model. In this way, we break the traditional dependency between condition and label, substituting the latter by unsupervised features coming from the latent space. Finally, we show that this new technique is able to produce samples on demand keeping the quality of its supervised counterpart.
In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. Specifically, we train a convolutional neural network to learn discriminative features by optimizing two popular versions of the Triplet Loss in order to study their clustering properties under the assumption of noisy labels. Additionally, we propose a new, simple Triplet Loss formulation, which shows desirable properties with respect to formal clustering objectives and outperforms the existing methods. We evaluate all three Triplet loss formulations for K-means and correlation clustering on the CIFAR-10 image classification dataset.
British Government long-term Measures for Exporters in the Manufacturing Sector in Times of COVID-19
(2020)
The authors of this paper have addressed the question of what measures have been taken by the British government to support exporters in the manufacturing sector in the era of COVID-19. A classification of the manufacturing export industry in the British economy as a whole and the impending economic impact of COVID-19 were also examined. It should be noted that the United Kingdom is facing major structural changes as a result of the Corona pandemic and its withdrawal from the European Union, which are examined more in detail in this paper. The UKEF, in cooperation with other institutions, provides a number of finance facilities for exporters already before Corona crisis. The access to get this support has been facilitated for the COVID-19 affected exporters, but no additional measures were made available.
This essay is about Estonia’s measures to help its exporters responding to COVID-19. The purpose is to analyse the companies’ need for help measures and the governmental objectives behind the measures and finally to analyse the possible effects. We used the two latest surveys dealing with the entrepreneurship situation and conducted two inter-views with governmental representatives exposing their objectives. The outcomes show that more than half of Estonian companies are asking for governmental help mainly as a consequence of a drop of demand. Limiting the increase of unemployment and bankrupt-cies as well as strengthening the economic recovery were identified as the main govern-mental objectives while restraining fiscal costs is a subordinated objective but becomes more important the more money will be spent. The help measures offered by KredEx are in line with these objectives. After the crisis the implications of the established measures should be analysed so that others can learn from the Estonian Government’s approach.