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Item type:Publication, Access status: with files , Local explanations for classification of ventilation data by neural networksNeural networks (NNs) have great potential to improve individualization of medicine, e.g., through analysis of signals. However, they are generally not interpretable. Understanding NN decisions is crucial, especially in safety-critical domains such as medicine. This work presents a new method to provide local explanations for classifications of signals made by NNs. Our method extends the Sig-LIME explanation method from one-dimensional signals to multidimensional signals by introducing new perturbation techniques. We evaluate the proposed method on an NN that classifies the positive end-expiratory pressure (PEEP) applied by a ventilator. The evaluation shows that the generated explanations are plausible, stable and concise.Publicationtype: Conference Paper18 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Configurable abstraction of signals using signal temporal logicAbstractions of signals help to avoid cognitive and storage overload, especially in systems with many signals such as cyber-physical systems (CPS). For assessing trends and reconstructing behavior, it often suffices to have a rough understanding how signals evolve, e.g., whether their behavior is monotonic or periodic. This work provides a configurable abstraction of signal behavior, where signals are described by a sequence of oscillation and linear patterns. We formalize templates of both behaviors in parameterized signal temporal logic (PSTL) and provide an algorithm that abstracts signals in terms of those patterns. The templates are configurable such that they allow for choosing the level of abstraction, e.g., by limiting the approximation error or the minimal oscillation frequency. For segmentation of the signal, we solve an optimization problem using a modified version of TeLEx. The evaluation demonstrates that configuration of the patterns is suitable to define the level of abstraction. Further, on control output from the ARCH wind turbine benchmark and flow data from medical ventilation, it demonstrates that the abstraction method can be applied to real-world signals.Publicationtype: Conference Paper27 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Code smell detection using features from version historyCode smells are indicators of bad quality in software. There exist several detection techniques for smells, which mainly base on static properties of the source code. Those detectors usually show weak performance in detection of context-sensitive smells since static properties hardly capture information about relations in the code. To address this information gap, we propose a strategy to extract information about interdependencies from version history. We use static and the new historical features to identify code smells by a random forest. Experiments show that the introduced historical features improve detection of code smells that focus on interdependencies.Publicationtype: Conference Paper58