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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.
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.
Evolutionary strategy is increasingly used for optimization in various machine learning problems. It can scale very well, even to high dimensional problems, and its ability to globally self optimize in flexible ways provides new and exciting opportunities when combined with more recent machine learning methods. This paper describes a novel approach for the optimization of models with a data driven evolutionary strategy. The optimization can directly be applied as a preprocessing step and is therefore independent of the machine learning algorithm used. The experimental analysis of six different use cases show that, on average, better results are attained than without evolutionary strategy. Furthermore it is shown, that the best individual models are also achieved with the help of evolutionary strategy. The six different use cases were of different complexity which reinforces the idea that the approach is universal and not depending on specific use cases.
Up until now, it has been shown that big parts of the so called Industry 4.0 are impacted by Machine Learning (ML) in some way or another. In many shopfloor situations, there are different sensors involved and all data is eventually structured, accumulated and prepared for application in various ML-based scenarios, e.g., predictive maintenance of a machine, quality monitoring of manufactured workpieces or handling domain-specific aspect of the respective fabricator or product. As the physical environment of Cyber Physical System (CPS) can change rapidly, the overall Data Acquisition (DAQ) process and ML training is impacted, too. This work focuses on datasets which consist of small amounts of tabular information and how to utilize them in image-based Neural Networks (NN) with respect to meta learning and multimodal transformations. Therefore, the conceptual utilization of an DAQ system in industrial environments is discussed regarding a variety of techniques for preprocessing and generating visual material from multimodal data. The outcome of such operations is a new dataset which is then applied in model training. Therefore, the presented approach is three-fold. In first analysing the concept of predicting the similarity of structured and numerical data in different datasets, indicators of the feasibility when applying the methodology in related but more sophisticated learning scenarios can be gained. Although ongoing time-series data is differing from simple multi-class data in terms of a chronologically dimension, basic classification concepts are applied to it and evaluated. In order to extend the similarity prediction with a temporal component, the discussed methods are extended by multimodal transformations and an subsequent utilization in Siamese Neural Networks (SNN). By discussing the concept of applying visual representations of structured time-series data in a meta-learning context, known trends and historic information can be utilized for generating real-world test-samples and predicting their validity on inference.
ARTHUR – Distributed Measuring System for Synchronous Data Acquisition from Different Data Sources
(2023)
In industrial manufacturing lines, different machines are well orchestrated and applied for their well-defined purpose. As each of these machines must be monitored and maintained in the first place, there are scenarios in which a Data Acquisition system brings enormous benefits. Since the cost of such professional systems is often not appropriate or feasible for research projects or prototyping, a proof of concept is often achieved by applying end-user hardware. In this work, a distributed measurement system for supporting the collection of data is described with respect to AI-based projects for research and teaching. ARTHUR (meAsuRing sysTem witH distribUted sensoRs) is arbitrarily expandable and has so far been used in the field of data acquisition on machine tools. Typical measured values are Accoustic Emission values, force plates X-Y-Z force values, simple PLC switching signals, OPC-UA machine parameters, etc., which were recorded by a wide variety of sensors. The overall ATHUR system is based on Raspberry Pis and consists of a master node, multiple independent measurement worker nodes, a streaming system realized with Redis, as well as a gateway that stores the data in the cloud. The major objectives of the ARTHUR system are scalability and the support for low-cost measuring components while solely applying open-source software. The work on hand discusses the advantages and disadvantages regarding the hard- and software of this TCP/IP-based system.
On the way to the smart factory, the manufacturing companies investigate the potential of Machine Learning approaches like visual quality inspection, process optimisation, maintenance prediction and more. In order to be able to assess the influence of Machine Learning based systems on business-relevant key figures, many companies go down the path of test before invest. This paper describes a novel and inexpensive distributed Data Acquisition System, ARTHUR (dAta collectoR sysTem witH distribUted sensoRs), to enable the collection of data for AI-based projects for research, education and the industry. ARTHUR is arbitrarily expandable and has so far been used in the field of data acquisition on machine tools. Typical measured values are Acoustic Emission values, force plate X-Y-Z force values, simple SPS signals, OPC-UA machine parameters, etc. which were recorded by a wide variety of sensors. The ARTHUR system consists of a master node, multiple measurement worker nodes, a local streaming system and a gateway that stores the data to the cloud. The authors describe the hardware and software of this system and discuss its advantages and disadvantages.
Cloud Computing an der HFU
(2010)