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In recent years, physically unclonable functions (PUFs) have gained significant attraction in IoT security applications, such as cryptographic key generation and entity authentication. PUFs extract the uncontrollable production characteristics of different devices to generate unique fingerprints for security applications. When generating PUF-based secret keys, the reliability and entropy of the keys are vital factors. This study proposes a novel method for generating PUF-based keys from a set of measurements. Firstly, it formulates the group-based key generation problem as an optimization problem and solves it using integer linear programming (ILP), which guarantees finding the optimum solution. Then, a novel scheme for the extraction of keys from groups is proposed, which we call positioning syndrome coding (PSC). The use of ILP as well as the introduction of PSC facilitates the generation of high-entropy keys with low error correction costs. These new methods have been tested by applying them on the output of a capacitor network PUF. The results confirm the application of ILP and PSC in generating high-quality keys.
The global pathway to net zero emissions by 2050 requires governments to implement and strengthen climate policies as global emissions are reaching record level. Climate finance plays a crucial role in the net zero transition. It refers to local, national or transnational financing seeking to support mitigation and adaptation actions that address climate change. Public export-import banks (EXIMs) and government export credit agencies (ECAs) are highly influential actors for climate action. Although there is no consensus among EXIMs and ECAs on how to define climate finance, 20 institutions assessed in this report give evidence that they significantly support climate action related transactions: EXIM and ECA financing and insurance amounted to EUR 6.7-8.4 billion in 2020, much more than estimated by the Climate Policy Initiative (CPI). However, the results also show that EXIM and ECA lending and insurance activities must rise substantially in order to contribute to the climate finance volumes required by 2030 as estimated by CPI. To retain their current proportion relative to other climate finance flows, assessed institutions would need to increase their climate financing 6.8 times to between EUR 45.3 billion and EUR 57.4 billion by 2030.
This article presents a study of cultural differences affecting the acceptance and design preferences of social robots. Based on a survey with 794 participants from Germany and the three Arab countries of Egypt, Jordan, and Saudi Arabia, we discuss how culture influences the preferences for certain attributes. We look at social roles, abilities and appearance, emotional awareness and interactivity of social robots, as well as the attitude toward automation. Preferences were found to differ not only across cultures, but also within countries with similar cultural backgrounds. Our findings also show a nuanced picture of the impact of previously identified culturally variable factors, such as attitudes toward traditions and innovations. While the participants’ perspectives toward traditions and innovations varied, these factors did not fully account for the cultural variations in their perceptions of social robots. In conclusion, we believe that more real-life practices emerging from the situated use of robots should be investigated. Besides focusing on the impact of broader cultural values such as those associated with religion and traditions, future studies should examine how users interact, or avoid interaction, with robots within specific contexts of use.
In pandemic times, the possibilities for conventional sports activities are severely limited; many sports facilities are closed or can only be used with restrictions. To counteract this lack of health activities and social exchange, people are increasingly adopting new digital sports solutions—a behavior change that had already started with the trend towards fitness apps and activity trackers. Existing research suggests that digital solutions increase the motivation to move and stay active. This work further investigates the potentials of digital sports incorporating the dimensions gender and preference for team sports versus individual sports. The study focuses on potential users, who were mostly younger professionals and academics. The results show that the SARS-CoV-19 pandemic had a significant negative impact on sports activity, particularly on persons preferring team sports. To compensate, most participants use more digital sports than before, and there is a positive correlation between the time spent physically active during the pandemic and the increase in motivation through digital sports. Nevertheless, there is still considerable skepticism regarding the potential of digital sports solutions to increase the motivation to do sports, increase performance, or raise a sense of team spirit when done in groups.
This paper describes a taxonomy which allows to assess and compare different implementations of master data objects. A systematic breakdown of core entities provides a framework to tell apart four subdividing categories of master data objects: independent and dependent objects, relational objects, and reference objects that serve to attribute information. This supports the preparation of data migrations from one system to another.
Dieses Arbeitspapier behandelt den aktuellen Markt von Legal-Tech-Diensten in Deutschland und die rechtlichen Entwicklungen bezüglich der dort bestehenden Law-Tech-Branche. Ziel ist es dabei, anhand einer systematischen Analyse der beteiligten Marktkräfte, die Attraktivität der Legal-Tech-Branche einzuschätzen, um dem Leser dadurch eine Hilfestellung für die Strategiebildung innerhalb Law-Tech bezogener Unternehmen sowie Kanzleien zu bieten, denn die strategische Planung eines Unternehmens ist als Basis für den nachhaltigen Erfolg desselben unabdinglich.
Darüber hinaus zielt die Arbeit darauf ab, dem Leser einen Überblick über die rechtlichen Entwicklungen im Bereich von Legal-Tech sowie damit einhergehend ein Basiswissen über die Hintergründe der Gesetzgebung in Bezug auf die Law-Tech-Branche zu verschaffen.
Gegenstand dieser Arbeit wird die Frage sein, inwiefern es sich bei User-generated Content um eine sinnvolle Ergänzung für die Kommunikationspolitik von Marken handelt. Zudem soll umrissen werden, auf welche Art und Weise er sich bestmöglich in der Marketingkommunikation einsetzen lässt. Ein besonderes Augenmerk wird dabei der UGC-Kommunikation gewidmet werden, die Unternehmen auf ihren markeneigenen Kanälen betreiben. Da es sich hierbei um ein noch relativ junges, spezifisches und im wissenschaftlichen Diskurs nur wenig untersuchtes Themengebiet handelt, sollen strategische Hintergründe und Zielsetzungen beleuchtet werden, die Unternehmen und Marken dazu bewegen, User-generated Content zu fördern und auf eigenen Kanälen auszuspielen.
Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation
(2021)
This paper presents a systematic approach for accurate short-time cloud coverage prediction based on a machine learning (ML) approach. Based on a newly built omnidirectional ground-based sky camera system, local training and evaluation data sets were created. These were used to train several state-of-the-art deep neural networks for object detection and segmentation. For this purpose, the camera-generated a full hemispherical image every 30 min over two months in daylight conditions with a fish-eye lens. From this data set, a subset of images was selected for training and evaluation according to various criteria. Deep neural networks, based on the two-stage R-CNN architecture, were trained and compared with a U-net segmentation approach implemented by CloudSegNet. All chosen deep networks were then evaluated and compared according to the local situation.
Human–robot collaborative applications have been receiving increasing attention in industrial applications. The efficiency of the applications is often quite low compared to traditional robotic applications without human interaction. Especially for applications that use speed and separation monitoring, there is potential to increase the efficiency with a cost-effective and easy to implement method. In this paper, we proposed to add human–machine differentiation to the speed and separation monitoring in human–robot collaborative applications. The formula for the protective separation distance was extended with a variable for the kind of object that approaches the robot. Different sensors for differentiation of human and non-human objects are presented. Thermal cameras are used to take measurements in a proof of concept. Through differentiation of human and non-human objects, it is possible to decrease the protective separation distance between the robot and the object and therefore increase the overall efficiency of the collaborative application.
A Review on Kinetic Energy Harvesting with Focus on 3D Printed Electromagnetic Vibration Harvesters
(2021)
The increasing amount of Internet of Things (IoT) devices and wearables require a reliable energy source. Energy harvesting can power these devices without changing batteries. Three-dimensional printing allows us to manufacture tailored harvesting devices in an easy and fast way. This paper presents the development of hybrid and non-hybrid 3D printed electromagnetic vibration energy harvesters. Various harvesting approaches, their utilised geometry, functional principle, power output and the applied printing processes are shown. The gathered harvesters are analysed, challenges examined and research gaps in the field identified. The advantages and challenges of 3D printing harvesters are discussed. Reported applications and strategies to improve the performance of printed harvesting devices are presented.