Now showing 1 - 10 of 2668
  • Publication
    Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems
    Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. We provide a definition and propose a concept for informed machine learning which illustrates its building blocks and distinguishes it from conventional machine learning. We introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Based on this taxonomy, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning.
  • Publication
    MotorFactory: A Blender Add-on for Large Dataset Generation of Small Electric Motors
    ( 2022)
    Wu, Chengzhi
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    Zhou, Kanran
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    Kaiser, Jan-Philipp
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    Mitschke, Norbert
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    Klein, Jan-Felix
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    Lanza, Gisela
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    Furmans, Kai
    To enable automatic disassembly of different product types with uncertain condition and degree of wear in remanufacturing, agile production systems that can adapt dynamically to changing requirements are needed. Machine learning algorithms can be employed due to their generalization capabilities of learning from various types and variants of products. However, in reality, datasets with a diversity of samples that can be used to train models are difficult to obtain in the initial period. This may cause bad performances when the system tries to adapt to new unseen input data in the future. In order to generate large datasets for different learning purposes, in our project, we present a Blender add-on named MotorFactory to generate customized mesh models of various motor instances. MotorFactory allows to create mesh models which, complemented with additional add-ons, can be further used to create synthetic RGB images, depth images, normal images, segmentation ground truth masks and 3D point cloud datasets with point-wise semantic labels. The created synthetic datasets may be used for various tasks including motor type classification, object detection for decentralized material transfer tasks, part segmentation for disassembly and handling tasks, or even reinforcement learning-based robotics control or view-planning.
  • Publication
    Cluster Crash: Learning from Recent Vulnerabilities in Communication Stacks
    ( 2022) ;
    Takacs, Philipp
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    To ensure functionality and security of network stacks in Industrial Devices, thorough testing is necessary. This includes blackbox network fuzzing, where fields in network packets are filled with unexpected values to test the device's behavior in edge cases. Due to resource constraints, the tests need to be efficient and such the input values need to be chosen intelligently. Previous solutions use heuristics based on vague knowledge from previous projects to make these decisions. We aim to structure existing knowledge by defining Vulnerability Anti-Patterns for network communication stacks based on an analysis of the recent vulnerability groups Ripple20, Amnesia:33, and Urgent/11. For our evaluation, we implement fuzzing test scripts based on the Vulnerability Anti-Patterns and run them against 8 Industrial Devices from 5 different device classes. We show (I) that similar vulnerabilities occur in implementations of the same protocol as well as in different protocols, (II) that similar vulnerabilities also spread over different device classes, and (III) that test scripts based on the Vulnerability Anti-Patterns help to identify these vulnerabilities.
  • Publication
    Intelligent Assistants: Conceptual Dimensions, Contextual Model, and Design Trends
    ( 2022)
    Dhiman, Hitesh
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    Wächter, Christoph
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    Fellmann, Michael
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    Intelligent assistants are an increasingly commonplace class of information systems spanning a broad range of form and complexity. But what characterizes an intelligent assistant, and how do we design better assistants? In the paper, the authors contribute to scientific research in the domain of intelligent assistants in three steps, each building on the previous. First, they investigate the historical context of assistance as human work. By examining qualitative studies regarding the work of human assistants, the authors inductively derive concepts crucial to modeling the context of assistance. This analysis informs the second step, in which they develop a conceptual typology of intelligent assistants using 111 published articles. This typology explicates the characteristics (what or how) of intelligent assistants and their use context (who or which). In the third and final step, the authors utilize this typology to shed light on historical trends and patterns in design and evaluation of intelligent assistants, reflect on missed opportunities, and discuss avenues for further exploration.
  • Publication
    Sovereign Digital Consent through Privacy Impact Quantification and Dynamic Consent
    ( 2022) ;
    Hornung, Marina
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    Kadow, Thomas
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    Digitization is becoming more and more important in the medical sector. Through electronic health records and the growing amount of digital data of patients available, big data research finds an increasing amount of use cases. The rising amount of data and the imposing privacy risks can be overwhelming for patients, so they can have the feeling of being out of control of their data. Several previous studies on digital consent have tried to solve this problem and empower the patient. However, there are no complete solution for the arising questions yet. This paper presents the concept of Sovereign Digital Consent by the combination of a consent privacy impact quantification and a technology for proactive sovereign consent. The privacy impact quantification supports the patient to comprehend the potential risk when sharing the data and considers the personal preferences regarding acceptance for a research project. The proactive dynamic consent implementation provides an implementation for fine granular digital consent, using medical data categorization terminology. This gives patients the ability to control their consent decisions dynamically and is research friendly through the automatic enforcement of the patients' consent decision. Both technologies are evaluated and implemented in a prototypical application. With the combination of those technologies, a promising step towards patient empowerment through Sovereign Digital Consent can be made.
  • Publication
    Siamese recurrent neural networks for the robust classification of grid disturbances in transmission power systems considering unknown events
    ( 2022)
    Kummerow, A.
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    Monsalve, C.
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    Bretschneider, P.
    The automated identification and localisation of grid disturbances is a major research area and key technology for the monitoring and control of future power systems. Current recognition systems rely on sufficient training data and are very error-prone to disturbance events, which are unseen during training. This study introduces a robust Siamese recurrent neural network using attention-based embedding functions to simultaneously identify and locate disturbances from synchrophasor data. Additionally, a novel double-sigmoid classifier is introduced for reliable differentiation between known and unknown disturbance types and locations. Different models are evaluated within an open-set classification problem for a generic power transmission system considering different unknown disturbance events. A detailed analysis of the results is provided and classification results are compared with a state-of-the-art open-set classifier.
  • Publication
    Feasibility of artificial intelligence-supported assessment of bone marrow infiltration using dual-energy computed tomography in patients with evidence of monoclonal protein - a retrospective observational study
    ( 2022)
    Fervers, P.
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    Lohneis, P.
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    Pollman-Schweckhors, P.
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    Zaytoun, H.
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    Rinneburger, M.
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    Maintz, D.
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    Große Hokamp, N.
    Objectives To demonstrate the feasibility of an automated, non-invasive approach to estimate bone marrow (BM) infiltration of multiple myeloma (MM) by dual-energy computed tomography (DECT) after virtual non-calcium (VNCa) post-processing. Methods Individuals with MM and monoclonal gammopathy of unknown significance (MGUS) with concurrent DECT and BM biopsy between May 2018 and July 2020 were included in this retrospective observational study. Two pathologists and three radiologists reported BM infiltration and presence of osteolytic bone lesions, respectively. Bone mineral density (BMD) was quantified CT-based by a CE-certified software. Automated spine segmentation was implemented by a pre-trained convolutional neural network. The non-fatty portion of BM was defined as voxels > 0 HU in VNCa. For statistical assessment, multivariate regression and receiver operating characteristic (ROC) were conducted. Results Thirty-five patients (mean age 65 ± 12 years; 18 female) were evaluated. The non-fatty portion of BM significantly predicted BM infiltration after adjusting for the covariable BMD (p = 0.007, r = 0.46). A non-fatty portion of BM > 0.93% could anticipate osteolytic lesions and the clinical diagnosis of MM with an area under the ROC curve of 0.70 [0.49-0.90] and 0.71 [0.54-0.89], respectively. Our approach identified MM-patients without osteolytic lesions on conventional CT with a sensitivity and specificity of 0.63 and 0.71, respectively. Conclusions Automated, AI-supported attenuation assessment of the spine in DECT VNCa is feasible to predict BM infiltration in MM. Further, the proposed method might allow for pre-selecting patients with higher pre-test probability of osteolytic bone lesions and support the clinical diagnosis of MM without pathognomonic lesions on conventional CT.
  • Publication
    In memoriam Fernando Puente Leon
    ( 2022)
    Heizmann, M.
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    Beyerer, J.
  • Publication
    Composition and Symmetries - Computational Analysis of Fine-Art Aesthetics
    ( 2022)
    Zhuravleva, Olga A.
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    Komarov, Andrei V.
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    Zherdev, Denis A.
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    Savkhalova, Natalie B.
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    Demina, Anna L.
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    Nikonorov, Artem V.
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    Nesterov, Alexander Y.
    This article deals with the problem of quantitative research of the aesthetic content of the fine-art object. The paper states that a fine-art object is a conceptually formed sequence of signs, and its composition is a structural form, that can be measured using mathematical models. The main approach is based on the perception of the formal order as a determinant of the aesthetic category of beauty. The composition of the image is directly related to the formation of aesthetic sensations and values, since it performs the function of controlling the viewer's perception of a work of art. The research is based on the studies of computational aesthetics by G. D. Birkhoff and M. Bense, as well as the studies of the receptive aesthetics of R. Ingarden, W. Iser, H. R. Jauss and Ya. Mukarzhovsky. The computational aesthetics methods, such as CNN-based object detectors, and gestalt-based symmetry analysis, are used to detect symmetry axes in fine-art images. Experimental analysis demonstrates that the applied computational approach is consistent with the philosophical analysis and the expert evaluations of the fine-art images, therefore it allows to obtain more detailed fine-art paintings description.
  • Publication
    Handheld spectral sensing devices should not mislead consumers as far as non-authentic food is concerned: A case study with adulteration of milk powder
    ( 2022)
    Delatour, Thierry
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    Romero, Roman
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    Panchaud, Alexandre
    With the rising trend of consumers being offered by start-up companies portable devices and applications for checking quality of purchased products, it appears of paramount importance to assess the reliability of miniaturized sensors embedded in such devices. Here, eight sensors were assessed for food fraud applications in skimmed milk powder. The performance was evaluated with dry- and wet-blended powders mimicking adulterated materials by addition of either ammonium sulfate, semicarbazide, or cornstarch in the range 0.5-10% of profit. The quality of the spectra was assessed for an adequate identification of the outliers prior to a deep assessment of performance for both non-targeted (soft independent modelling of class analogy, SIMCA) and targeted analyses (partial least square regression with orthogonal signal correction, OPLS). Here, we show that the sensors have generally difficulties in detecting adulterants at ca. 5% supplementation, and often fail in achieving adequate specificity and detection capability. This is a concern as they may mislead future users, particularly consumers, if they are intended to be developed for handheld devices available publicly in smartphone-based applications. Full article(This article belongs to the Special Issue Rapid Detection Methods for Food Fraud and Food Contaminants Series II).