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Retrospective Analysis of Training and Its Response in Marathon Finishers Based on Fitness App Data
(2021)
EIT Image Interpretation
(2021)
In dem vorliegenden Beitrag wird der aktuelle Trend, Payas-you-live-Systeme (PAYL) in Verbindung mit Krankenversicherungen anzubieten, analysiert. PAYL-Systeme bedeutet konkret die kontinuierliche Erfassung von Gesundheitsdaten mithilfe technischer Geräte und Übermittlung dieser Daten an Versicherungen sowie die Auszahlung von Boni für erreichte Fitnessziele. Zunächst wird eine Definition von PAYL-Systemen dargelegt, dann werden die technischen Rahmenbedingungen erläutert, schließlich werden die Auswirkungen dieser soziotechnischen Systeme näher beleuchtet. Dies geschieht auf Grundlage unserer empirischen Untersuchung. Die drei identifizierten Hauptkonflikte betreffen die Genese von PAYL, seine Auswirkungen auf den Wert der Datensouveränität besonders für Versicherte und die Schwierigkeit, für den Anspruch auf Prävention und Kostenersparnis durch PAYL Evidenz zu erzeugen. Diese Konflikte werden diskutiert, um sowohl die direkten wie auch indirekten Auswirkungen der Digitalisierung und der Ökonomisierung des Sozialen durch PAYL zu beleuchten.
Nonlinearity of Magnetostrictive Torque Sensor under Varying External Magnetic Field Strength
(2021)
Auch während Pandemien müssen die Blutspendedienste kontinuierlich BlutspenderInnen rekrutieren, um die Versorgung mit Blutprodukten zu gewährleisten. Während im Anschluss an andere Katastrophen wie Erdbeben oder Terroranschlägen die Spendebereitschaft meist sprunghaft ansteigt, zeigte sich in der Vergangenheit bei beginnenden Pandemien zunächst ein Rückgang des Spendeaufkommens. Viele SpenderInnen fürchten eine Infektion oder eine Schwächung ihres Immunsystems und bleiben zu Hause. Auch fällt es den Blutspendediensten zunächst schwer, die gewohnte Anzahl an mobilen Spendeterminen zu organisieren, wodurch das Spendenaufkommen zurückgeht. In der aktuellen SARS-CoV2-Pandemie betrug dieser Rückgang in vielen Ländern mehr als 10%.
SpenderInnen, die auch während einer Pandemie spenden, sind in der Regel erfahrener und besitzen bereits eine ausgebildete Spenderidentiät. Viele dieser SpenderInnen berichten, dass sie gezielt einen Beitrag zur Überwindung der Krise leisten möchten. Auch während einer Pandemie ist demnach eine hohe Solidarität unter Blutspendern zu finden. Potentielle ErstpenderInnen lassen sich durch die unsicheren Rahmenbedingungen dagegen von einer Spende eher abhalten und es bedarf gezielter Rekrutierungsstrategien unter Einsatz von Social-Media-Kanälen, um neue SpenderInnen zu gewinnen. Erste Befunde unter deutschen Blutspendern lassen hierbei eine hohe Rückkehrintention der ErstspenderInnen erwarten.
Um die Blutversorgung auch während einer Pandemie aufrechterhalten zu können, sollten die Blutspendedienste neben der Rekrutierung von ErstspenderInnen versuchen, schnell die Anzahl ihrer Spendetermine zu erhöhen. Die Ansprache bestehender SpenderInnen sollte vor allem die Verunsicherung reduzieren und das Vertrauen in die Blutspendedienste stärken. Je größer das Vertrauen in die Spendeeinrichtung ausfällt, desto geringer ist die Risikowahrnehmung der SpenderInnen. Auch sollten alternative Kontaktwege etwa über Messenger-Dienste getestet werden, da diese eine schnelle Ansprache erlauben.
Editorial
(2021)
ASiG §1 Grundsatz
(2021)
In Industry 4.0 machine learning approaches are a state-of-the art for predictive maintenance, machine condition monitoring, and others. Distributed decision trees are one of the learning algorithms for such applications. A new approach of node based parallelization for the construction is presented and allows to classify data through a network of nodes. Attacks on the nodes are discussed based on different attack scenarios and attack classifications are presented. A thorough analysis of protection measurements is given, such that classification is not maliciously modified by an attacker. Different countermeasures are proposed and analyzed. A quorum-based system allows for a good balance between computational overhead and robustness of the algorithm.
Distributed machine learning algorithms that employ Deep Neural Networks (DNNs) are widely used in Industry 4.0 applications, such as smart manufacturing. The layers of a DNN can be mapped onto different nodes located in the cloud, edge and shop floor for preserving privacy. The quality of the data that is fed into and processed through the DNN is of utmost importance for critical tasks, such as inspection and quality control. Distributed Data Validation Networks (DDVNs) are used to validate the quality of the data. However, they are prone to single points of failure when an attack occurs. This paper proposes QUDOS, an approach that enhances the security of a distributed DNN that is supported by DDVNs using quorums. The proposed approach allows individual nodes that are corrupted due to an attack to be detected or excluded when the DNN produces an output. Metrics such as corruption factor and success probability of an attack are considered for evaluating the security aspects of DNNs. A simulation study demonstrates that if the number of corrupted nodes is less than a given threshold for decision-making in a quorum, the QUDOS approach always prevents attacks. Furthermore, the study shows that increasing the size of the quorum has a better impact on security than increasing the number of layers. One merit of QUDOS is that it enhances the security of DNNs without requiring any modifications to the algorithm and can therefore be applied to other classes of problems.
Umsetzung eines Pay-as-you-Live Systems : Konzeptioneller Ansatz für ein eigenständiges PAYL-System
(2021)
Chest Compresses with Ginger or Mustard Affect Warmth Regulation in Healthy Adults - A Randomized Co
(2021)
In recent years, both the Internet of Things (IoT) and blockchain technologies have been highly influential and revolutionary. IoT enables companies to embrace Industry 4.0, the Fourth Industrial Revolution, which benefits from communication and connectivity to reduce cost and to increase productivity through sensor-based autonomy. These automated systems can be further refined with smart contracts that are executed within a blockchain, thereby increasing transparency through continuous and indisputable logging. Ideally, the level of security for these IoT devices shall be very high, as they are specifically designed for this autonomous and networked environment. This paper discusses a use case of a company with legacy devices that wants to benefit from the features and functionality of blockchain technology. In particular, the implications of retrofit solutions are analyzed. The use of the BISS:4.0 platform is proposed as the underlying infrastructure. BISS:4.0 is intended to integrate the blockchain technologies into existing enterprise environments. Furthermore, a security analysis of IoT and blockchain present attacks and countermeasures are presented that are identified and applied to the mentioned use case.
The usage of machine learning models for prediction is growing rapidly and proof that the intended requirements are met is essential. Audits are a proven method to determine whether requirements or guidelines are met. However, machine learning models have intrinsic characteristics, such as the quality of training data, that make it difficult to demonstrate the required behavior and make audits more challenging. This paper describes an ML audit framework that evaluates and reviews the risks of machine learning applications, the quality of the training data, and the machine learning model. We evaluate and demonstrate the functionality of the proposed framework by auditing an steel plate fault prediction model.
The Elephant in the Room - Educating Practitioners on Software Development for Sustainability
(2021)
"Tafel"
(2021)
Detection of Concept Drift in Manufacturing Data with SHAP Values to Improve Error Prediction
(2021)
Digital transformation strengthens the interconnection of companies in order to develop optimized and better customized, cross-company business models. These models require secure, reliable, and trace- able evidence and monitoring of contractually agreed information to gain trust between stakeholders. Blockchain technology using smart contracts allows the industry to establish trust and automate cross- company business processes without the risk of losing data control. A typical cross-company industry use case is equipment maintenance. Machine manufacturers and service providers offer maintenance for their machines and tools in order to achieve high availability at low costs. The aim of this chapter is to demonstrate how maintenance use cases are attempted by utilizing hyperledger fabric for building a chain of trust by hardened evidence logging of the maintenance process to achieve legal certainty. Contracts are digitized into smart contracts automating business that increase the security and mitigate the error-proneness of the business processes.