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3-D Lung Visualization Using Electrical Impedance Tomography Combined with Body Plethysmography
(2013)
3D Computer Vision for the Industrial Metaverse - On the potentials of Neural Radiance Fields
(2023)
The industrial metaverse refers to the use of virtual reality (VR) and augmented reality (AR) technologies in the context of industry and manufacturing. It is envisioned as a shared, immersive digital space where people can interact with and manipulate virtual representations of physical objects and processes. The industrial metaverse has the potential to transform the way products are designed, manufactured, and maintained,
enabling new levels of collaboration, automation, and innovation.
It further includes virtual representations of humans, also known as avatars. These avatars can be used to enable remote collaboration and communication between people in the virtual space. In this way, the industrial metaverse can facilitate virtual meetings, trainings, and other interactive experiences that involve human participants.
Neural Radiance Fields (NeRFs) are a powerful tool for synthesizing photorealistic images of 3D objects, including virtual representations of humans known as avatars. In this talk, we will discuss the potential applications of NeRFs in generating high-fidelity objects and avatars for use in the industrial metaverse.
A Coherent Set of Customer Experience Factors for the Developers of Industrial Product Services
(2016)
A concept for modelling linear lung compliances using a mechanical artificially ventilated simulator
(2013)
A Development Environment for the Rapid Design of Operator Training Simulators for Biotechnology
(2005)
A Fog-Cloud Computing Infrastructure for Condition Monitoring and Distributing Industry 4.0 Services
(2019)
Explainable Artificial Intelligence (XAI) seeks to enhance transparency and trust in AI systems. Evaluating the quality of XAI explanation methods remains challenging due to limitations in existing metrics. To address these issues, we propose a novel metric called Explanation Significance Assessment (ESA) and its extension, the Weighted Explanation Significance Assessment (WESA). These metrics offer a comprehensive evaluation of XAI explanations, considering spatial precision, focus overlap, and relevance accuracy. In this paper, we demonstrate the applicability of ESA and WESA on medical data. These metrics quantify the understandability and reliability of XAI explanations, assisting practitioners in interpreting AI-based decisions and promoting informed choices in critical domains like healthcare. Moreover, ESA and WESA can play a crucial role in AI certification, ensuring both accuracy and explainability. By evaluating the performance of XAI methods and underlying AI models, these metrics contribute to trustworthy AI systems. Incorporating ESA and WESA in AI certification efforts advances the field of XAI and bridges the gap between accuracy and interpretability. In summary, ESA and WESA provide comprehensive metrics to evaluate XAI explanations, benefiting research, critical domains, and AI certification, thereby enabling trustworthy and interpretable AI systems.
A Review on Digital Wallets and Federated Service for Future of Cloud Services Identity Management
(2023)
In today’s technology-driven era, managing digital identities has become a critical concern due to the widespread use of online services and digital devices. This has led to a fragmented landscape of digital identities, burdening individuals with multiple usernames, passwords, and authentication methods. To address this challenge, digital wallets have emerged as a promising solution. These wallets empower users to store, manage, and utilize their digital assets, including personal data, payment information, and credentials. Additionally, federated services have gained prominence, enabling users to access multiple services using a single digital identity. Gaia-X is an example of such a service, aiming to establish a secure and trustworthy data infrastructure. This paper examines digital identity management, focusing on the application of digital wallets and federated services. It explores the categorization of identities needed for different cloud services, considering their unique requirements and characteristics. Furthermore, it discusses the future requirements for digital wallets and federated identity management in the cloud, along with the associated challenges and benefits. The paper also introduces a categorization scheme for cloud services based on security and privacy requirements, demonstrating how different identity types can be mapped to each category.
A Similarity Measure in Bayesian Classification Based on Characteristic Attributes of Objects
(2016)
A simulation-based approach for measuring the performance of human-AI collaborative decisions
(2022)
Despite the widespread adoption of artificial intelligence and machine learning for decision-making in organizations, a wealth of research shows that situations that involve open-ended decisions (novel contexts without predefined rules) will continue to require humans in the loop. However, such collaboration that blends formal machine and bounded human rationality also amplify the risk of what is known as local rationality, which is when rational decisions in a local setting lead to globally dysfunctional behavior. Therefore, it becomes crucial, especially in a data-abundant environment that characterizes algorithmic decision-making, to devise means to assess performance holistically, not just for decision fragments. There is currently a lack of quantitative models that address this issue.
The YOLO series of object detection algorithms, including YOLOv4 and YOLOv5, have shown superior performance in various medical diagnostic tasks, surpassing human ability in some cases. However, their black-box nature has limited their adoption in medical applications that require trust and explainability of model decisions. To address this issue, visual explanations for AI models, known as visual XAI, have been proposed in the form of heatmaps that highlight regions in the input that contributed most to a particular decision. Gradient-based approaches, such as Grad-CAM, and non-gradient-based approaches, such as Eigen-CAM, are applicable to YOLO models and do not require new layer implementation. This paper evaluates the performance of Grad-CAM and Eigen-CAM on the VinDrCXR Chest X-ray Abnormalities Detection dataset and discusses the limitations of these methods for explaining model decisions to data scientists.
Activating People with Dementia using Natural User Interface interaction on a Surface Computer
(2017)
Age-Related Impairments like Reduced Hearing Capacity – A Safety Issue for the Working World?
(2017)
The digital transformation of companies is expected to increase the digital interconnection between different companies to develop optimized, customized, hybrid business models. These cross-company business models require secure, reliable, and traceable logging and monitoring of contractually agreed information sharing between machine tools, operators, and service providers. This paper discusses how the major requirements for building hybrid business models can be tackled by the blockchain for building a chain of trust and smart contracts for digitized contracts. A machine maintenance use case is used to discuss the readiness of smart contracts for the automation of workflows defined in contracts. Furthermore, it is shown that the number of failures is significantly improved by using these contracts and a blockchain.