Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning - Peitz, Sebastian; Stenner, Jan; Chidananda, Vikas; Wallscheid, Oliver; Brunton, Steven L.; Taira, Kunihiko (Physica D: Nonlinear Phenomena)
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.

Implementierung eines Algorithmus zur motivbasierten Schnitt-Sparsifizierung - Dorociak, Svitlana

Toward an Android Static Analysis Approach for Data Protection - Khedkar, Mugdha; Bodden, Eric (Proceedings of the 9th International Conference on Mobile Software Engineering and Systems)
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.
Keywords: static program analysis; data protection and privacy; GDPR compliance

Learning Bilinear Models of Actuated Koopman Generators from Partially-Observed Trajectories - Otto, Samuel E.; Peitz, Sebastian; Rowley, Clarence W. (SIAM Journal on Applied Dynamical Systems)
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.

Supporting Error Chains in Static Analysis for Precise Evaluation Results and Enhanced Usability - Wickert, Anna-Katharina; Schlichtig, Michael; Vogel, Marvin; Winter, Lukas; Mezini, Mira; Bodden, Eric
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.
Keywords: Static analysis; error chains; false positive re- duction; empirical studies

Post-configuration Activation of Hardware Trojans in FPGAs - Ahmed, Qazi Arbab; Wiersema, Tobias; Platzner, Marco (Journal of Hardware and Systems Security)
Keywords: General Engineering; Energy Engineering and Power Technology

Using Worked Examples for Engaging in Epistemic Programming Projects - Hüsing, Sven; Schulte, Carsten; Sparmann, Sören; Bolte, Mario (Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1)

Differentially Private Natural Language Models: Recent Advances and Future Directions - Hu, Lijie; Habernal, Ivan; Shen, Lei; Wang, Di (Findings of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta, March 17-22, 2024)

DP-NMT: Scalable Differentially Private Machine Translation - Igamberdiev, Timour; Vu, Doan Nam Long; Kuennecke, Felix; Yu, Zhuo; Holmer, Jannik; Habernal, Ivan (Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations)
Neural machine translation (NMT) is a widely popular text generation task, yet there is a considerable research gap in the development of privacy-preserving NMT models, despite significant data privacy concerns for NMT systems. Differentially private stochastic gradient descent (DP-SGD) is a popular method for training machine learning models with concrete privacy guarantees; however, the implementation specifics of training a model with DP-SGD are not always clarified in existing models, with differing software libraries used and code bases not always being public, leading to reproducibility issues. To tackle this, we introduce DP-NMT, an open-source framework for carrying out research on privacy-preserving NMT with DP-SGD, bringing together numerous models, datasets, and evaluation metrics in one systematic software package. Our goal is to provide a platform for researchers to advance the development of privacy-preserving NMT systems, keeping the specific details of the DP-SGD algorithm transparent and intuitive to implement. We run a set of experiments on datasets from both general and privacy-related domains to demonstrate our framework in use. We make our framework publicly available and welcome feedback from the community.

Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling - Razavi, Kamran; Ghafouri, Saeid; Mühlhäuser, Max; Jamshidi, Pooyan; Wang, Lin (Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024)

A Dispersion Algorithm for Robot Swarms Inside Polygonal Boundary Shapes - De Groote, Carsten

Optimal Drone Strategies For Packet Delivery - Doddegowda, Rajesh

Evaluating the Implications - Thakur, Heena

On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems - Preuß, Oliver Ludger; Rook, Jeroen; Trautmann, Heike (Applications of Evolutionary Computation)

Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP - Vinzent Seiler, Moritz; Rook, Jeroen; Heins, Jonathan; Preuß, Oliver Ludger; Bossek, Jakob; Trautmann, Heike (2023 IEEE Symposium Series on Computational Intelligence (SSCI))

Error bounds for kernel-based approximations of the Koopman operator - Philipp, Friedrich; Schaller, Manuel; Worthmann, Karl; Peitz, Sebastian; Nüske, Feliks (Applied and Computational Harmonic Analysis)
We consider the data-driven approximation of the Koopman operator for stochastic differential equations on reproducing kernel Hilbert spaces (RKHS). Our focus is on the estimation error if the data are collected from long-term ergodic simulations. We derive both an exact expression for the variance of the kernel cross-covariance operator, measured in the Hilbert-Schmidt norm, and probabilistic bounds for the finite-data estimation error. Moreover, we derive a bound on the prediction error of observables in the RKHS using a finite Mercer series expansion. Further, assuming Koopman-invariance of the RKHS, we provide bounds on the full approximation error. Numerical experiments using the Ornstein-Uhlenbeck process illustrate our results.

Characterizing Microheterogeneity in Liquid Mixtures via Local Density Fluctuations - Lass, Michael; Kenter, Tobias; Plessl, Christian; Brehm, Martin (Entropy)
We present a novel approach to characterize and quantify microheterogeneity and microphase separation in computer simulations of complex liquid mixtures. Our post-processing method is based on local density fluctuations of the different constituents in sampling spheres of varying size. It can be easily applied to both molecular dynamics (MD) and Monte Carlo (MC) simulations, including periodic boundary conditions. Multidimensional correlation of the density distributions yields a clear picture of the domain formation due to the subtle balance of different interactions. We apply our approach to the example of force field molecular dynamics simulations of imidazolium-based ionic liquids with different side chain lengths at different temperatures, namely 1-ethyl-3-methylimidazolium chloride, 1-hexyl-3-methylimidazolium chloride, and 1-decyl-3-methylimidazolium chloride, which are known to form distinct liquid domains. We put the results into the context of existing microheterogeneity analyses and demonstrate the advantages and sensitivity of our novel method. Furthermore, we show how to estimate the configuration entropy from our analysis, and we investigate voids in the system. The analysis has been implemented into our program package TRAVIS and is thus available as free software.

Optimisation and Evaluation of Breadth First Search with oneAPI/SYCL on Intel FPGAs: from Describing Algorithms to Describing Architectures - Olgu, Kaan; Kenter, Tobias; Nunez-Yanez, Jose; Mcintosh-Smith, Simon (Proceedings of the 12th International Workshop on OpenCL and SYCL)

IPA: Inference Pipeline Adaptation to Achieve High Accuracy and Cost-Efficiency - Ghafouri, Saeid; Razavi, Kamran; Salmani, Mehran; Sanaee, Alireza; Lorido Botran, Tania; Wang, Lin; Doyle, Joseph; Jamshidi, Pooyan (Journal of Systems Research (JSys))

Empowering Students for the Data-Driven World: A Qualitative Study of the Relevance of Learning about Data-Driven Technologies - Höper, Lukas; Schulte, Carsten (Informatics in Education)
In K-12 computing education, there is a need to identify and teach concepts that are relevant to understanding machine learning technologies. Studies of teaching approaches often evaluate whether students have learned the concepts. However, scant research has examined whether such concepts support understanding digital artefacts from everyday life and developing agency in a digital world. This paper presents a qualitative study that explores students’ perspectives on the relevance of learning concepts of data-driven technologies for navigating the digital world. The underlying approach of the study is data awareness, which aims to support students in understanding and reflecting on such technologies to develop agency in a data-driven world. This approach teaches students an explanatory model encompassing several concepts of the role of data in data-driven technologies. We developed an intervention and conducted retrospective interviews with students. Findings from the analysis of the interviews indicate that students can analyse and understand data-driven technologies from their everyday lives according to the central role of data. In addition, students’ answers revealed four areas of how learning about data-driven technologies becomes relevant to them. The paper concludes with a preliminary model suggesting how computing education can make concepts of data-driven technologies meaningful for students to understand and navigate the digital world.
Keywords: Computer Science Applications; Communication; Education; General Engineering

Noctua 2 Supercomputer - Bauer, Carsten; Kenter, Tobias; Lass, Michael; Mazur, Lukas; Meyer, Marius; Nitsche, Holger; Riebler, Heinrich; Schade, Robert; Schwarz, Michael; Winnwa, Nils; Wiens, Alex; Wu, Xin; et al. (Journal of large-scale research facilities)
Noctua 2 is a supercomputer operated at the Paderborn Center for Parallel Computing (PC2) at Paderborn University in Germany. Noctua 2 was inaugurated in 2022 and is an Atos BullSequana XH2000 system. It consists mainly of three node types: 1) CPU Compute nodes with AMD EPYC processors in different main memory configurations, 2) GPU nodes with NVIDIA A100 GPUs, and 3) FPGA nodes with Xilinx Alveo U280 and Intel Stratix 10 FPGA cards. While CPUs and GPUs are known off-the-shelf components in HPC systems, the operation of a large number of FPGA cards from different vendors and a dedicated FPGA-to-FPGA network are unique characteristics of Noctua 2. This paper describes in detail the overall setup of Noctua 2 and gives insights into the operation of the cluster from a hardware, software and facility perspective.
Keywords: Noctua 2; Supercomputer; FPGA; PC2; Paderborn Center for Parallel Computing

A Little Certainty is All We Need: Discovery and Synchronization Acceleration in Battery-Free IoT - Liu, Gaosheng; Nigade, Vinod; Bal, Henri; Wang, Lin (Proceedings of the 8th ACM Asia Pacific Workshop on Networking (APNET))

TARPS: A Toolbox for Enhancing Privacy and Security for Collaborative AR - Krings, Sarah Claudia; Yigitbas, Enes (Proceedings of the ACM on Human-Computer Interaction, EICS)

Augmented and Virtual Reality for Diet and Nutritional Education: A Systematic Literature Review - Yigitbas, Enes; Mazur, Janet (Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA 2024))

Development and Usability Evaluation of Transitional Cross-Reality Interfaces - Schmidt, Leonard; Yigitbas, Enes (Proceedings of the ACM on Human-Computer Interaction, EICS)

Gamification- and Virtual Reality-Based Learning Environment for UML Class Diagram Modeling - Yigitbas, Enes; Schmidt, Maximilian; Bucchiarone, Antonio; Bassanelli, Simone; Engels, Gregor (IEEE Global Engineering Education Conference 2024)

Effects of Human Avatar Representation in Virtual Reality on Inter-Brain Connections - Yigitbas, Enes; Kaltschmidt, Christian (Proceedings of the 8th International Conference on Artificial Intelligence and Virtual Reality (AIVR’24))

Interaction Techniques for Remote Maintenance in an AR Shared Environment - Krings, Sarah Claudia; Biermeier, Kai; Yigitbas, Enes (Proceedings of the 10th International Working Conference on Human-Centered Software Engineering (HCSE'24))

An Exploratory Study of Fear-Inducing Factors in Virtual Reality Experiences - Leichtweiß, Justus; Yigitbas, Enes (Proceedings of the 12th International Conference on Serious Games and Applications for Health (SeGAH'24))

Prototyping Cross-Reality Escape Rooms - Krois, Sebastian; Yigitbas, Enes (Proceedings of the 10th International Working Conference on Human-Centered Software Engineering (HCSE'24))

Developing a VR Factory Walkthrough for Use in Schools - Krings, Sarah Claudia; Yigitbas, Enes; Sauer, Stefan (Proceedings of the 10th International Working Conference on Human-Centered Software Engineering (HCSE'24))

Common pitfalls to avoid while using multiobjective optimization in machine learning - Akhter, Junaid; Fährmann, Paul David; Sonntag, Konstantin; Peitz, Sebastian (arXiv)

Evaluating Security Through Isolation and Defense in Depth - Bodden, Eric; Pottebaum, Jens; Fockel, Markus; Gräßler, Iris (IEEE Security & Privacy)
Keywords: Law; Electrical and Electronic Engineering; Computer Networks and Communications

Symbol-Specific Sparsification of Interprocedural Distributive Environment Problems - Karakaya, Kadiray; Bodden, Eric (Proceedings of the IEEE/ACM 46th International Conference on Software Engineering)
Previous work has shown that one can often greatly speed up static analysis by computing data flows not for every edge in the program’s control-flow graph but instead only along definition-use chains. This yields a so-called sparse static analysis. Recent work on SparseDroid has shown that specifically taint analysis can be “sparsified” with extraordinary effectiveness because the taint state of one variable does not depend on those of others. This allows one to soundly omit more flow-function computations than in the general case. In this work, we now assess whether this result carries over to the more generic setting of so-called Interprocedural Distributive Environment (IDE) problems. Opposed to taint analysis, IDE comprises distributive problems with large or even infinitely broad domains, such as typestate analysis or linear constant propagation. Specifically, this paper presents Sparse IDE, a framework that realizes sparsification for any static analysis that fits the IDE framework. We implement Sparse IDE in SparseHeros, as an extension to the popular Heros IDE solver, and evaluate its performance on real-world Java libraries by comparing it to the baseline IDE algorithm. To this end, we design, implement and evaluate a linear constant propagation analysis client on top of SparseHeros. Our experiments show that, although IDE analyses can only be sparsified with respect to symbols and not (numeric) values, Sparse IDE can nonetheless yield significantly lower runtimes and often also memory consumptions compared to the original IDE.

SootUp: A Redesign of the Soot Static Analysis Framework - Karakaya, Kadiray; Schott, Stefan; Klauke, Jonas; Bodden, Eric; Schmidt, Markus; Luo, Linghui; He, Dongjie (Tools and Algorithms for the Construction and Analysis of Systems)
Abstract Since its inception two decades ago, Soot has become one of the most widely used open-source static analysis frameworks. Over time it has been extended with the contributions of countless researchers. Yet, at the same time, the requirements for Soot have changed over the years and become increasingly at odds with some of the major design decisions that underlie it. In this work, we thus present SootUp , a complete reimplementation of Soot that seeks to fulfill these requirements with a novel design, while at the same time keeping elements that Soot users have grown accustomed to.

Detecting Security-Relevant Methods using Multi-label Machine Learning - Johnson, Oshando; Piskachev, Goran; Krishnamurthy, Ranjith; Bodden, Eric (Proceedings of the 46th International Conference on Software Engineering, IDE Workshop)
To detect security vulnerabilities, static analysis tools need to be configured with security-relevant methods. Current approaches can automatically identify such methods using binary relevance machine learning approaches. However, they ignore dependencies among security-relevant methods, over-generalize and perform poorly in practice. Additionally, users have to nevertheless manually configure static analysis tools using the detected methods. Based on feedback from users and our observations, the excessive manual steps can often be tedious, error-prone and counter-intuitive. In this paper, we present Dev-Assist, an IntelliJ IDEA plugin that detects security-relevant methods using a multi-label machine learning approach that considers dependencies among labels. The plugin can automatically generate configurations for static analysis tools, run the static analysis, and show the results in IntelliJ IDEA. Our experiments reveal that Dev-Assist's machine learning approach has a higher F1-Measure than related approaches. Moreover, the plugin reduces and simplifies the manual effort required when configuring and using static analysis tools.

TypeEvalPy: A Micro-benchmarking Framework for Python Type Inference Tools - Shivarpatna Venkatesh, Ashwin Prasad; Sabu, Samkutty; Wang, Jiawei; Mir, Amir M.; Li, Li; Bodden, Eric (Proceedings of the 46th International Conference on Software Engineering, Tool Demo Track)

Measuring Visual Attention Capacity Across xReality - Biermeier, Kai; Scharlau, Ingrid; Yigitbas, Enes (Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA 2024))

Exploring Custom FPGA Accelerators for DNN-based RF Fingerprinting - Henke, Luca-Sebastian

Towards New Realities: Implications of Personalized Online Layers in Our Daily Lives - Herder, Eelco; Stojko, Laura; Strecker, Jannis; Neumayr, Thomas; Yigitbas, Enes; Augstein, Mirjam (i-com - Journal of Interactive Media, Special Issue on “The Future of Human-Computer Interaction” (to appear))

Digitale Plattformen im industriellen Mittelstand

"I'm actually going to go and change these passwords": Analyzing the Usability of Credential Audit Interfaces in Password Managers - Kablo, Emiram; Kader, Katharina; Arias Cabarcos, Patricia (Extended Abstracts of the CHI Conference on Human Factors in Computing Systems)

Enabling Performance Portability for Shallow Water Equations on CPUs, GPUs, and FPGAs with SYCL - Büttner, Markus; Alt, Christoph; Kenter, Tobias; Köstler, Harald; Plessl, Christian; Aizinger, Vadym (Proceedings of the Platform for Advanced Scientific Computing Conference (PASC))

ESLM: Improving Entity Summarization by Leveraging Language Models - Firmansyah, Asep Fajar; Moussallem, Diego; Ngomo, Axel-Cyrille Ngonga (The Semantic Web)

Security Analysis of BigBlueButton and eduMEET - Heitmann, Nico; Siewert, Hendrik; Moog, Sven; Somorovsky, Juraj (Applied Cryptography and Network Security)
Video conferencing systems have become an indispensable part of our world. Using video conferencing systems implies the expectation that online meetings run as smoothly as in-person meetings. Thus, online meetings need to be just as secure and private as in-person meetings, which are secured against disruptive factors and unauthorized persons by physical access control mechanisms. To show the security dangers of conferencing systems and raise general awareness when using these technologies, we analyze the security of two widely used research and education open-source video conferencing systems: BigBlueButton and eduMEET. Because both systems are very different, we analyzed their architectures, considering the respective components with their main tasks, features, and user roles. In the following systematic security analyses, we found 50 vulnerabilities. These include broken access control, NoSQL injection, and denial of service (DoS). The vulnerabilities have root causes of different natures. While BigBlueButton has a lot of complexity due to many components, eduMEET, which is relatively young, focuses more on features than security. The sheer amount of results and the lack of prior work indicate a research gap that needs to be closed since video conferencing systems continue to play a significant role in research, education, and everyday life.

Beyond One-Shot Explanations: A Systematic Literature Review of Dialogue-Based XAI Approaches - Mindlin, Dimitry; Beer, Fabian; Sieger, Leonie Nora; Heindorf, Stefan; Cimiano, Philipp; Esposito, Elena; Ngonga-Ngomo, Axel-Cyrille
In the last decade, there has been increasing interest in allowing users to understand how the predictions of machine-learned models come about, thus increasing transparency and empowering users to understand and potentially contest those decisions.Dialogue-based approaches, in contrast to traditional one-shot eXplainable Artificial Intelligence (XAI) methods, facilitate interactive, in-depth exploration through multi-turn dialogues, simulating expert conversations. This paper reviews the current state of dialogue-based XAI, presenting a systematic review of 1,339 publications, narrowed down to 14 based on inclusion criteria. We explore theoretical foundations of the systems, propose key dimensions along which different solutions to dialogue-based XAI differ, and identify key use cases, target audiences, system components, and the types of supported queries and responses. Furthermore, we investigate the current paradigms by which systems are evaluated and highlight their key limitations. Key findings include identifying the main use cases, objectives, and audiences targeted by dialogue-based XAI methods, and summarize the main types of questions and information needs. Beyond discussing avenues for future work, we present a meta-architecture for these systems from existing literature and outlined prevalent theoretical frameworks.

Universal Knowledge Graph Embeddings - KOUAGOU, N'Dah Jean; Demir, Caglar; Zahera, Hamada Mohamed Abdelsamee; Wilke, Adrian; Heindorf, Stefan; Li, Jiayi; Ngonga Ngomo, Axel-Cyrille (Companion Proceedings of the ACM on Web Conference 2024)

DeepApprox: Rapid Deep Learning based Design Space Exploration of Approximate Circuits via Check-pointing - Awais, Muhammad; Ghasemzadeh Mohammadi, Hassan; Platzner, Marco (To apear in IEEE ISVLSI 2024)

Exploratory Landscape Analysis for Mixed-Variable Problems - Prager, Raphael Patrick; Trautmann, Heike (IEEE Transactions on Evolutionary Computation)
Keywords: Optimization; Evolutionary computation; Benchmark testing; Hyperparameter optimization; Portfolios; Extraterrestrial measurements; Dispersion; Exploratory landscape analysis; mixed-variable problem; mixed search spaces; automated algorithm selection

UpCy: Safely Updating Outdated Dependencies - Dann, Andreas Peter; Hermann, Ben; Bodden, Eric