TY - CONF AB - Digital Servitization is one of the significant trends affecting the manufacturing industry. Companies try to tackle challenges regarding their differentiation and profitability using digital services. One specific type of digital services are smart services, which are digital services built on data from smart products. Introducing these kinds of offerings into the portfolio of manufacturing companies is not trivial. Moreover, they require conscious action to align all relevant capabilities to realize the respective business goals. However, what capabilities are generally relevant for smart services remains opaque. We conducted a systematic literature review to identify them and extended the results through an interview study. Our analysis results in 78 capabilities clustered among 12 principles and six dimensions. These results provide significant support for the smart service transformation of manufacturing companies and for structuring the research field of smart services. AU - Koldewey, Christian AU - Fichtler, Timm AU - Scholtysik, Michel AU - Biehler, Jan AU - Schreiner, Nick AU - Sommer, Franziska AU - Schacht, Maximilian AU - Kaufmann, Jonas AU - Rabe, Martin AU - Sedlmeier, Joachim AU - Dumitrescu, Roman ID - 48632 KW - Digital Servitization KW - Transformation KW - Capabilities KW - Maturity KW - Smart Services TI - Exploring Capabilities for the Smart Service Transformation in Manufacturing: Insights from Theory and Practice ER - TY - JOUR AU - Herbert, Franziska AU - Becker, Steffen AU - Buckmann, Annalina AU - Kowalewski, Marvin AU - Hielscher, Jonas AU - Acar, Yasemin AU - Dürmuth, Markus AU - Sasse, M. Angela AU - Zou, Yixin ID - 47275 JF - IEEE Symposium on Security and Privacy. IEEE, New York, NY, USA TI - Digital Security -- A Question of Perspective. A Large-Scale Telephone Survey with Four At-Risk User Groups ER - TY - CONF AU - Afroze, Lameya AU - Merkelbach, Silke AU - von Enzberg, Sebastian AU - Dumitrescu, Roman ID - 49354 T2 - ML4CPS 2023 TI - Domain Knowledge Injection Guidance for Predictive Maintenance ER - TY - CONF AU - Scholtysik, Michel AU - Rohde, Malte AU - Koldewey, Christian AU - Dumitrescu, Roman ID - 49363 TI - Circular Product-Service-System Ideation Canvas – A Framework for the Design of circular Product-Service-System Ideas ER - TY - CONF AU - Scholtysik, Michel AU - Rohde, Malte AU - Koldewey, Christian AU - Dumitrescu, Roman ID - 49364 TI - Business strategy taxonomy and solution patterns for the circular economy ER - TY - CONF AU - Krings, Sarah Claudia AU - Yigitbas, Enes ID - 50476 T2 - Proceedings of the 16th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2024) (to appear) TI - TARPS: A Toolbox for Enhancing Privacy and Security for Collaborative AR ER - TY - CONF AU - Dou, Feng AU - Wang, Lin AU - Chen, Shutong AU - Liu, Fangming ID - 50066 T2 - Proceedings of the IEEE International Conference on Computer Communications (INFOCOM) TI - X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics ER - TY - CONF AU - Blöcher, Marcel AU - Nedderhut, Nils AU - Chuprikov, Pavel AU - Khalili, Ramin AU - Eugster, Patrick AU - Wang, Lin ID - 50065 T2 - Proceedings of the IEEE International Conference on Computer Communications (INFOCOM) TI - Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES ER - TY - CONF AU - Hu, Haichuan AU - Liu, Fangming AU - Pei, Qiangyu AU - Yuan, Yongjie AU - Xu, Zichen AU - Wang, Lin ID - 50807 T2 - Proceedings of the ACM Web Conference (WWW) TI - 𝜆Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing ER - TY - GEN AB - We rigorously derive novel and sharp finite-data error bounds for highly sample-efficient Extended Dynamic Mode Decomposition (EDMD) for both i.i.d. and ergodic sampling. In particular, we show all results in a very general setting removing most of the typically imposed assumptions such that, among others, discrete- and continuous-time stochastic processes as well as nonlinear partial differential equations are contained in the considered system class. Besides showing an exponential rate for i.i.d. sampling, we prove, to the best of our knowledge, the first superlinear convergence rates for ergodic sampling of deterministic systems. We verify sharpness of the derived error bounds by conducting numerical simulations for highly-complex applications from molecular dynamics and chaotic flame propagation. AU - Philipp, Friedrich M. AU - Schaller, Manuel AU - Boshoff, Septimus AU - Peitz, Sebastian AU - Nüske, Feliks AU - Worthmann, Karl ID - 51160 T2 - arXiv:2402.02494 TI - Extended Dynamic Mode Decomposition: Sharp bounds on the sample efficiency ER - TY - JOUR AB - We derive efficient algorithms to compute weakly Pareto optimal solutions for smooth, convex and unconstrained multiobjective optimization problems in general Hilbert spaces. To this end, we define a novel inertial gradient-like dynamical system in the multiobjective setting, which trajectories converge weakly to Pareto optimal solutions. Discretization of this system yields an inertial multiobjective algorithm which generates sequences that converge weakly to Pareto optimal solutions. We employ Nesterov acceleration to define an algorithm with an improved convergence rate compared to the plain multiobjective steepest descent method (Algorithm 1). A further improvement in terms of efficiency is achieved by avoiding the solution of a quadratic subproblem to compute a common step direction for all objective functions, which is usually required in first-order methods. Using a different discretization of our inertial gradient-like dynamical system, we obtain an accelerated multiobjective gradient method that does not require the solution of a subproblem in each step (Algorithm 2). While this algorithm does not converge in general, it yields good results on test problems while being faster than standard steepest descent. AU - Sonntag, Konstantin AU - Peitz, Sebastian ID - 46019 JF - Journal of Optimization Theory and Applications TI - Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems ER - TY - GEN AB - The efficient optimization method for locally Lipschitz continuous multiobjective optimization problems from [1] is extended from finite-dimensional problems to general Hilbert spaces. The method iteratively computes Pareto critical points, where in each iteration, an approximation of the subdifferential is computed in an efficient manner and then used to compute a common descent direction for all objective functions. To prove convergence, we present some new optimality results for nonsmooth multiobjective optimization problems in Hilbert spaces. Using these, we can show that every accumulation point of the sequence generated by our algorithm is Pareto critical under common assumptions. Computational efficiency for finding Pareto critical points is numerically demonstrated for multiobjective optimal control of an obstacle problem. AU - Sonntag, Konstantin AU - Gebken, Bennet AU - Müller, Georg AU - Peitz, Sebastian AU - Volkwein, Stefan ID - 51334 T2 - arXiv:2402.06376 TI - A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces ER - TY - JOUR AB - We present a convolutional framework which significantly reduces the complexity and thus, the computational effort for distributed reinforcement learning control of dynamical systems governed by partial differential equations (PDEs). Exploiting translational equivariances, the high-dimensional distributed control problem can be transformed into a multi-agent control problem with many identical, uncoupled agents. Furthermore, using the fact that information is transported with finite velocity in many cases, the dimension of the agents’ environment can be drastically reduced using a convolution operation over the state space of the PDE, by which we effectively tackle the curse of dimensionality otherwise present in deep reinforcement learning. In this setting, the complexity can be flexibly adjusted via the kernel width or by using a stride greater than one (meaning that we do not place an actuator at each sensor location). Moreover, scaling from smaller to larger domains – or the transfer between different domains – becomes a straightforward task requiring little effort. We demonstrate the performance of the proposed framework using several PDE examples with increasing complexity, where stabilization is achieved by training a low-dimensional deep deterministic policy gradient agent using minimal computing resources. AU - Peitz, Sebastian AU - Stenner, Jan AU - Chidananda, Vikas AU - Wallscheid, Oliver AU - Brunton, Steven L. AU - Taira, Kunihiko ID - 40171 JF - Physica D: Nonlinear Phenomena TI - Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning VL - 461 ER - TY - GEN AU - Dorociak, Svitlana ID - 52318 TI - Implementierung eines Algorithmus zur motivbasierten Schnitt-Sparsifizierung ER - TY - CONF AB - Android applications collecting data from users must protect it according to the current legal frameworks. Such data protection has become even more important since the European Union rolled out the General Data Protection Regulation (GDPR). Since app developers are not legal experts, they find it difficult to write privacy-aware source code. Moreover, they have limited tool support to reason about data protection throughout their app development process. This paper motivates the need for a static analysis approach to diagnose and explain data protection in Android apps. The analysis will recognize personal data sources in the source code, and aims to further examine the data flow originating from these sources. App developers can then address key questions about data manipulation, derived data, and the presence of technical measures. Despite challenges, we explore to what extent one can realize this analysis through static taint analysis, a common method for identifying security vulnerabilities. This is a first step towards designing a tool-based approach that aids app developers and assessors in ensuring data protection in Android apps, based on automated static program analysis. AU - Khedkar, Mugdha AU - Bodden, Eric ID - 52235 KW - static program analysis KW - data protection and privacy KW - GDPR compliance T2 - Proceedings of the 9th International Conference on Mobile Software Engineering and Systems TI - Toward an Android Static Analysis Approach for Data Protection ER - TY - JOUR AU - Bodden, Eric AU - Pottebaum, Jens AU - Fockel, Markus AU - Gräßler, Iris ID - 52587 IS - 1 JF - IEEE Security & Privacy KW - Law KW - Electrical and Electronic Engineering KW - Computer Networks and Communications SN - 1540-7993 TI - Evaluating Security Through Isolation and Defense in Depth VL - 22 ER - TY - JOUR AB - Data-driven models for nonlinear dynamical systems based on approximating the underlying Koopman operator or generator have proven to be successful tools for forecasting, feature learning, state estimation, and control. It has become well known that the Koopman generators for control-affine systems also have affine dependence on the input, leading to convenient finite-dimensional bilinear approximations of the dynamics. Yet there are still two main obstacles that limit the scope of current approaches for approximating the Koopman generators of systems with actuation. First, the performance of existing methods depends heavily on the choice of basis functions over which the Koopman generator is to be approximated; and there is currently no universal way to choose them for systems that are not measure preserving. Secondly, if we do not observe the full state, we may not gain access to a sufficiently rich collection of such functions to describe the dynamics. This is because the commonly used method of forming time-delayed observables fails when there is actuation. To remedy these issues, we write the dynamics of observables governed by the Koopman generator as a bilinear hidden Markov model, and determine the model parameters using the expectation-maximization (EM) algorithm. The E-step involves a standard Kalman filter and smoother, while the M-step resembles control-affine dynamic mode decomposition for the generator. We demonstrate the performance of this method on three examples, including recovery of a finite-dimensional Koopman-invariant subspace for an actuated system with a slow manifold; estimation of Koopman eigenfunctions for the unforced Duffing equation; and model-predictive control of a fluidic pinball system based only on noisy observations of lift and drag. AU - Otto, Samuel E. AU - Peitz, Sebastian AU - Rowley, Clarence W. ID - 33461 IS - 1 JF - SIAM Journal on Applied Dynamical Systems TI - Learning Bilinear Models of Actuated Koopman Generators from Partially-Observed Trajectories VL - 23 ER - TY - GEN AB - Context Static analyses are well-established to aid in understanding bugs or vulnerabilities during the development process or in large-scale studies. A low false-positive rate is essential for the adaption in practice and for precise results of empirical studies. Unfortunately, static analyses tend to report where a vulnerability manifests rather than the fix location. This can cause presumed false positives or imprecise results. Method To address this problem, we designed an adaption of an existing static analysis algorithm that can distinguish between a manifestation and fix location, and reports error chains. An error chain represents at least two interconnected errors that occur successively, thus building the connection between the fix and manifestation location. We used our tool CogniCryptSUBS for a case study on 471 GitHub repositories, a performance benchmark to compare different analysis configurations, and conducted an expert interview. Result We found that 50 % of the projects with a report had at least one error chain. Our runtime benchmark demonstrated that our improvement caused only a minimal runtime overhead of less than 4 %. The results of our expert interview indicate that with our adapted version participants require fewer executions of the analysis. Conclusion Our results indicate that error chains occur frequently in real-world projects, and ignoring them can lead to imprecise evaluation results. The runtime benchmark indicates that our tool is a feasible and efficient solution for detecting error chains in real-world projects. Further, our results gave a hint that the usability of static analyses may benefit from supporting error chains. AU - Wickert, Anna-Katharina AU - Schlichtig, Michael AU - Vogel, Marvin AU - Winter, Lukas AU - Mezini, Mira AU - Bodden, Eric ID - 52663 KW - Static analysis KW - error chains KW - false positive re- duction KW - empirical studies TI - Supporting Error Chains in Static Analysis for Precise Evaluation Results and Enhanced Usability ER - TY - JOUR AU - Ahmed, Qazi Arbab AU - Wiersema, Tobias AU - Platzner, Marco ID - 52686 JF - Journal of Hardware and Systems Security KW - General Engineering KW - Energy Engineering and Power Technology SN - 2509-3428 TI - Post-configuration Activation of Hardware Trojans in FPGAs ER - TY - CONF AU - Sparmann, Sören AU - Hüsing, Sven AU - Schulte, Carsten ID - 52380 T2 - Proceedings of the 23rd Koli Calling International Conference on Computing Education Research TI - JuGaze: A Cell-based Eye Tracking and Logging Tool for Jupyter Notebooks ER -