Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems

2023 , Rueden, Laura von , Mayer, Sebastian , Beckh, Katharina , Georgiev, Bogdan , Giesselbach, Sven , Heese, Raoul , Kirsch, Birgit , Walczak, Michal , Pfrommer, Julius , Pick, Annika , Ramamurthy, Rajkumar , Garcke, Jochen , Bauckhage, Christian , Schuecker, Jannis

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.

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Anwendungsszenarien für AR in der Produktion: Use Cases und Technologielösungen

2022 , Deppe, Sahar , Brünninghaus, Marc , Voit, Michael , Röcker, Carsten

Mit der steigenden Leistungsfähigkeit von mobilen Computern und Anzeigegeräten hat sich die Nutzung von Augmented-Reality-Technologien in den letzten zehn Jahren verstärkt. Augmented Reality (AR) ist eine Technik, die es den Nutzern ermöglicht, mit ihrer physischen Umgebung durch die Überlagerung digitaler Informationen zu interagieren. Diese Technologie hebt bestimmte Merkmale der physischen Welt hervor, verbessert das Verständnis für diese Merkmale und bietet intelligente und zugängliche Einblicke. AR-Anwendungen haben das Potenzial, enorme Auswirkungen auf Branchen wie Produktion, Medizin, Forschung, Ausbildung und Unterhaltung zu bewirken. Der Fokus dieses Artikels liegt auf den AR-Anwendungen im Bereich der Produktion, die vor allem in den Bereichen Montage, Reparatur, Diagnose und Schulung eingesetzt werden. Außerdem werden die Effektivität und Effizienz von AR-Technologien in diesem Bereich anhand von vier dieser Anwendungen vorgestellt.

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Sovereign Digital Consent through Privacy Impact Quantification and Dynamic Consent

2022 , Appenzeller, Arno , Hornung, Marina , Kadow, Thomas , Krempel, Erik , Beyerer, Jürgen

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.

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Multiagent Self-Redundancy Identification and Tuned Greedy-Exploration

2022 , Martinez, D.A. , Mojica-Nava, E. , Watson, K. , Usländer, T.

The constant development of sensing applications using innovative and affordable measurement devices has increased the amount of data transmitted through networks, carrying in many cases, redundant information that requires more time to be analyzed or larger storage centers. This redundancy is mainly present because the network nodes do not recognize environmental variations requiring exploration, which causes a repetitive data collection in a set of limited locations. In this work, we propose a multiagent learning framework that uses the Gaussian process regression (GPR) to allow the agents to predict the environmental behavior by means of the neighborhood measurements, and the rate distortion function to establish a border in which the environmental information is neither misunderstood nor redundant. We apply this framework to a mobile sensor network and demonstrate that the nodes can tune the parameter s of the Blahut-Arimoto algorithm in order to adjust the gathered environment information and to become more or less exploratory within a sensing area.

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Cluster Crash: Learning from Recent Vulnerabilities in Communication Stacks

2022 , Borcherding, Anne , Takacs, Philipp , Beyerer, Jürgen

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.

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Mixture of Experts of Neural Networks and Kalman Filters for Optical Belt Sorting

2022 , Thumm, Jakob , Reith-Braun, Marcel , Pfaff, Florian , Hanebeck, Uwe D. , Flitter, Merle , Maier, Georg , Gruna, Robin , Längle, Thomas , Bauer, Albert , Kruggel-Emden, Harald

In optical sorting of bulk material, the composition of particles may frequently change. State-of-the-art sorting approaches rely on tuning physical models of the particle motion. The aim of this work is to increase the prediction accuracy in complex, fast-changing sorting scenarios with data-driven approaches. We propose two neural network (NN) experts for accurate prediction of a priori known particle types. To handle the large variety of particle types that can occur in real-world sorting scenarios, we introduce a simple but effective mixture of experts approach that combines NNs with hand-crafted motion models. Our new method not only improves the prediction accuracy for bulk material consisting of many particle classes, but also proves to be very adaptive and robust to new particle types.

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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 , Becker, Florian , Krause, Julius , Romero, Roman , Gruna, Robin , Längle, Thomas , 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).

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Reiteration theorem for R and L-spaces with the same parameter

2022 , Doktorski, Leo , Fernandez-Martinez, Pedro , Signes, Teresa

Let E,F,E0,E1 be rearrangement invariant spaces; let a,b,b0,b1 be slowly varying functions and 0<θ0,θ1<1. We characterize the interpolation spaces (Xâ¾Î¸0,b0,E0,a,FR,Xâ¾Î¸1,b1,E1,a,FL)η,b,E,0â¤Î·â¤1, when the parameters θ0 and θ1 are equal (under appropriate conditions on bi(t), i=0,1). This completes the study started in [11,12,22], which only considered the case θ0<θ1. As an application we recover and generalize interpolation identities for grand and small Lebesgue spaces given by [26].

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Intelligent Assistants: Conceptual Dimensions, Contextual Model, and Design Trends

2022 , Dhiman, Hitesh , Wächter, Christoph , Fellmann, Michael , Röcker, Carsten

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.

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Intelligente Bild- und Videoauswertung für die Sicherheit

2022 , Golda, Thomas , Cormier, Mickael , Beyerer, Jürgen

Das Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB befasst sich seit vielen Jahren mit der intelligenten Bild- und Videoauswertung im präventiv-polizeilichen und ermittlungstechnischen Bereich. Neuste Methoden der intelligenten Videoüberwachung werden dazu in realen Anwendungen getestet und weiterentwickelt. Bis 2023 wird beispielsweise gemeinsam mit dem Land Baden-Württemberg und dem Polizeipräsidium Mannheim eine intelligente Technik in einem Modellprojekt in Mannheim erprobt und weiterentwickelt, die zudem die Privatsphäre der Bevölkerung und den Datenschutz verbessert. Das Ziel ist es, ein Assistenzsystem zu entwickeln, das die Aufmerksamkeit der Videobeobachter im Führungs- und Lagezentrum auf polizeilich relevante Situationen lenkt, so dass die Beamten ausschließlich diese Szenen sehen und bewerten müssen. Zudem wird in diesem Beitrag das aktuelle Potenzial intelligenter Verfahren exemplarisch anhand des fraunhofereigenen Experimentalsystems ivisX aufgezeigt.

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