Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs - Werner, Stefan; Peitz, Sebastian (arXiv:2302.07160)
The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by partial differential equations (PDEs). To breach the gap between the immense promises we see in RL and the applicability in complex engineering systems, the main challenges are the massive requirements in terms of the training data, as well as the lack of performance guarantees. We present a solution for the first issue using a data-driven surrogate model in the form of a convolutional LSTM with actuation. We demonstrate that learning an actuated model in parallel to training the RL agent significantly reduces the total amount of required data sampled from the real system. Furthermore, we show that iteratively updating the model is of major importance to avoid biases in the RL training. Detailed ablation studies reveal the most important ingredients of the modeling process. We use the chaotic Kuramoto-Sivashinsky equation do demonstarte our findings.

Quantum space, ground space traversal, and how to embed multi-prover interactive proofs into unentanglement - Gharibian, Sevag; Rudolph, Dorian (14th Innovations in Theoretical Computer Science (ITCS))
Savitch's theorem states that NPSPACE computations can be simulated in PSPACE. We initiate the study of a quantum analogue of NPSPACE, denoted Streaming-QCMASPACE (SQCMASPACE), where an exponentially long classical proof is streamed to a poly-space quantum verifier. Besides two main results, we also show that a quantum analogue of Savitch's theorem is unlikely to hold, as SQCMASPACE=NEXP. For completeness, we introduce Streaming-QMASPACE (SQMASPACE) with an exponentially long streamed quantum proof, and show SQMASPACE=QMA_EXP (quantum analogue of NEXP). Our first main result shows, in contrast to the classical setting, the solution space of a quantum constraint satisfaction problem (i.e. a local Hamiltonian) is always connected when exponentially long proofs are permitted. For this, we show how to simulate any Lipschitz continuous path on the unit hypersphere via a sequence of local unitary gates, at the expense of blowing up the circuit size. This shows quantum error-correcting codes can be unable to detect one codeword erroneously evolving to another if the evolution happens sufficiently slowly, and answers an open question of [Gharibian, Sikora, ICALP 2015] regarding the Ground State Connectivity problem. Our second main result is that any SQCMASPACE computation can be embedded into "unentanglement", i.e. into a quantum constraint satisfaction problem with unentangled provers. Formally, we show how to embed SQCMASPACE into the Sparse Separable Hamiltonian problem of [Chailloux, Sattath, CCC 2012] (QMA(2)-complete for 1/poly promise gap), at the expense of scaling the promise gap with the streamed proof size. As a corollary, we obtain the first systematic construction for obtaining QMA(2)-type upper bounds on arbitrary multi-prover interactive proof systems, where the QMA(2) promise gap scales exponentially with the number of bits of communication in the interactive proof.

On the structure of regularization paths for piecewise differentiable regularization terms - Gebken, Bennet; Bieker, Katharina; Peitz, Sebastian (Journal of Global Optimization)
Regularization is used in many different areas of optimization when solutions are sought which not only minimize a given function, but also possess a certain degree of regularity. Popular applications are image denoising, sparse regression and machine learning. Since the choice of the regularization parameter is crucial but often difficult, path-following methods are used to approximate the entire regularization path, i.e., the set of all possible solutions for all regularization parameters. Due to their nature, the development of these methods requires structural results about the regularization path. The goal of this article is to derive these results for the case of a smooth objective function which is penalized by a piecewise differentiable regularization term. We do this by treating regularization as a multiobjective optimization problem. Our results suggest that even in this general case, the regularization path is piecewise smooth. Moreover, our theory allows for a classification of the nonsmooth features that occur in between smooth parts. This is demonstrated in two applications, namely support-vector machines and exact penalty methods.

Beep-and-Sleep: Message and Energy Efficient Set Cover - Götte, Thorsten; Kolb, Christina; Scheideler, Christian; Werthmann, Julian (Theor. Comput. Sci.)

Supporting Construction and Architectural Visualization through BIM and AR/VR: A Systematic Literature Review - Yigitbas, Enes; Nowosad, Alexander; Engels, Gregor (Proceedings of the 19th IFIP TC13 International Conference on Human-Computer Interaction (INTERACT 2023))

Towards reliable data-based optimal and predictive control using extended DMD - Schaller, Manuel; Worthmann, Karl; Philipp, Friedrich; Peitz, Sebastian; Nüske, Feliks (IFAC-PapersOnLine)
We present an approach for guaranteed constraint satisfaction by means of data-based optimal control, where the model is unknown and has to be obtained from measurement data. To this end, we utilize the Koopman framework and an eDMD-based bilinear surrogate modeling approach for control systems to show an error bound on predicted observables, i.e., functions of the state. This result is then applied to the constraints of the optimal control problem to show that satisfaction of tightened constraints in the purely data-based surrogate model implies constraint satisfaction for the original system.

Online load balancing on uniform machines with limited migration - Maack, Marten (Operations Research Letters)
Keywords: Applied Mathematics; Industrial and Manufacturing Engineering; Management Science and Operations Research; Software

A Formal Comparison of Advanced Digital Signature Primitives - Schürmann, Patrick

The Complexity of Translationally Invariant Problems beyond Ground State Energies - Gharibian, Sevag; Watson, James; Bausch, Johannes (Proceedings of the 40th International Symposium on Theoretical Aspects of Computer Science (STACS))

MAAS: Hiding Trojans in Approximate Circuits - Ahmed, Qazi Arbab; Awais, Muhammad; Platzner, Marco (The 24th International Symposium on Quality Electronic Design (ISQED'23), San Francisco, Califorina USA)

Minimum Edge Cuts in Overlay Networks - Schweichhart, Jonas

Unifying Gathering Protocols for Swarms of Mobile Robots - Castenow, Jannik; Harbig, Jonas; Meyer auf der Heide, Friedhelm (Lecture Notes in Computer Science)

Model-driven Collaborative Design of Professional Education Programmes With Extended Online Whiteboards - Wolters, Dennis; Engels, Gregor (MODELSWARD'23)

Cooperative Android App Analysis - Pauck, Felix

Routing Schemes for Hybrid Communication Networks - Werthmann, Julian; Scheideler, Christian; Coy, Sam; Czumaj, Artur; Schneider, Philipp

Designing Business Reputation Ecosystems — A Method for Issuing and Trading Monetary Ratings on a Blockchain - Hemmrich, Simon; Bobolz, Jan; Beverungen, Daniel; Blömer, Johannes (ECIS 2023 Research Papers)
Market transactions are subject to information asymmetry about the delivered value proposition, causing transaction costs and adverse market effects among buyers and sellers. Information systems research has investigated how review systems can reduce information asymmetry in business-to-consumer markets. However, these systems cannot be readily applied to business-to-business markets, are vulnerable to manipulation, and suffer from conceptual weak spots since they use textual data or star ratings. Building on design science research, we conceptualize a new class of reputation systems based on monetary-based payments as quantitative ratings for each transaction stored on a blockchain. Using cryptography, we show that our system assures content confidentiality so that buyers can share and sell their ratings selectively, establishing a reputation ecosystem. Our prescriptive insights advance the design of reputation systems and offer new paths to understanding the antecedents, dynamics, and consequences to reduce information asymmetry in B2B transactions.

Development and Evaluation of a Model-Based UI Prototyping Experimentation Approach - N., N.

Two Sparsification Strategies for Accelerating Demand-Driven Pointer Analysis - Karakaya, Kadiray; Bodden, Eric (2023 IEEE Conference on Software Testing, Verification and Validation (ICST))

Privacy-Preserving Collection and Evaluation of Log Files - Koch, Angelina

IngridKG: A FAIR Knowledge Graph of Graffiti - Sherif, Mohamed Ahmed; da Silva, Ana Alexandra Morim; Pestryakova, Svetlana; Ahmed, Abdullah Fathi; Niemann, Sven; Ngomo, Axel-Cyrille Ngonga (Scientific Data)
Abstract Graffiti is an urban phenomenon that is increasingly attracting the interest of the sciences. To the best of our knowledge, no suitable data corpora are available for systematic research until now. The Information System Graffiti in Germany project ( Ingrid ) closes this gap by dealing with graffiti image collections that have been made available to the project for public use. Within Ingrid , the graffiti images are collected, digitized and annotated. With this work, we aim to support the rapid access to a comprehensive data source on Ingrid targeted especially by researchers. In particular, we present Ingrid KG, an RDF knowledge graph of annotated graffiti, abides by the Linked Data and FAIR principles. We weekly update Ingrid KG by augmenting the new annotated graffiti to our knowledge graph. Our generation pipeline applies RDF data conversion, link discovery and data fusion approaches to the original data. The current version of Ingrid KG contains 460,640,154 triples and is linked to 3 other knowledge graphs by over 200,000 links. In our use case studies, we demonstrate the usefulness of our knowledge graph for different applications.
Keywords: Library and Information Sciences; Statistics; Probability and Uncertainty; Computer Science Applications; Education; Information Systems; Statistics and Probability

Local Protocols for Contracting and Expanding Robot Formation Problems - Castenow, Jannik

Online Algorithms for Allocating Heterogeneous Resources - Knollmann, Till

Approximation Algorithms for Fair Range Clustering - Hotegni, Sedjro Salomon; Mahabadi, Sepideh; Vakilian, Ali (Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023.)
Keywords: Fair range clustering

We Really Need to Talk About Session Tickets: A Large-Scale Analysis of Cryptographic Dangers with TLS Session Tickets - Hebrok, Sven Niclas; Nachtigall, Simon; Maehren, Marcel; Erinola, Nurullah; Merget, Robert; Somorovsky, Juraj; Schwenk, Jörg (32nd USENIX Security Symposium)

Data-Driven Product Management: A Practitioner-Driven Research Agenda - Grigoryan, Khoren; Fichtler, Timm; Schreiner, Nick; Rabe, Martin; Panzner, Melina; Kühn, Arno; Dumitrescu, Roman; Koldewey, Christian (Procedia CIRP 33)
The global megatrends of digitization and sustainability lead to new challenges for the design and management of technical products in industrial companies. Product management - as the bridge between market and company - has the task to absorb and combine the manifold requirements and make the right product-related decisions. In the process, product management is confronted with heterogeneous information, rapidly changing portfolio components, as well as increasing product, and organizational complexity. Combining and utilizing data from different sources, e.g., product usage data and social media data leads to promising potentials to improve the quality of product-related decisions. In this paper, we reinforce the need for data-driven product management as an interdisciplinary field of action. The state of data-driven product management in practice was analyzed by conducting workshops with six manufacturing companies and hosting a focus group meeting with experts from different industries. We investigate the expectations and derive requirements leading us to open research questions, a vision for data-driven product management, and a research agenda to shape future research efforts.
Keywords: Product Management; Data Analytics; Data-Driven Design; Product-related data; Lifecycle Data; Tool-support

Interview Study on Strategy Options for Platform Operation in B2B Markets - Özcan, Leon; Fichtler, Timm; Kasten, Benjamin; Koldewey, Christian; Dumitrescu, Roman
Keywords: Digital Platform; Platform Strategy; Strategic Management; Platform Life Cycle; Interview Study; Business Model; Business-to-Business; Two-sided Market; Multi-sided Market

Neural Class Expression Synthesis - KOUAGOU, N'Dah Jean; Heindorf, Stefan; Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023))
Many applications require explainable node classification in knowledge graphs. Towards this end, a popular ``white-box'' approach is class expression learning: Given sets of positive and negative nodes, class expressions in description logics are learned that separate positive from negative nodes. Most existing approaches are search-based approaches generating many candidate class expressions and selecting the best one. However, they often take a long time to find suitable class expressions. In this paper, we cast class expression learning as a translation problem and propose a new family of class expression learning approaches which we dub neural class expression synthesizers. Training examples are ``translated'' into class expressions in a fashion akin to machine translation. Consequently, our synthesizers are not subject to the runtime limitations of search-based approaches. We study three instances of this novel family of approaches based on LSTMs, GRUs, and set transformers, respectively. An evaluation of our approach on four benchmark datasets suggests that it can effectively synthesize high-quality class expressions with respect to the input examples in approximately one second on average. Moreover, a comparison to state-of-the-art approaches suggests that we achieve better F-measures on large datasets. For reproducibility purposes, we provide our implementation as well as pretrained models in our public GitHub repository at https://github.com/dice-group/NeuralClassExpressionSynthesis
Keywords: Neural network; Concept learning; Description logics

Counterfactual Explanations for Concepts in ELH - Sieger, Leonie Nora; Heindorf, Stefan; Blübaum, Lukas; Ngonga Ngomo, Axel-Cyrille (arXiv:2301.05109)
Knowledge bases are widely used for information management on the web, enabling high-impact applications such as web search, question answering, and natural language processing. They also serve as the backbone for automatic decision systems, e.g. for medical diagnostics and credit scoring. As stakeholders affected by these decisions would like to understand their situation and verify fair decisions, a number of explanation approaches have been proposed using concepts in description logics. However, the learned concepts can become long and difficult to fathom for non-experts, even when verbalized. Moreover, long concepts do not immediately provide a clear path of action to change one's situation. Counterfactuals answering the question "How must feature values be changed to obtain a different classification?" have been proposed as short, human-friendly explanations for tabular data. In this paper, we transfer the notion of counterfactuals to description logics and propose the first algorithm for generating counterfactual explanations in the description logic $\mathcal{ELH}$. Counterfactual candidates are generated from concepts and the candidates with fewest feature changes are selected as counterfactuals. In case of multiple counterfactuals, we rank them according to the likeliness of their feature combinations. For evaluation, we conduct a user survey to investigate which of the generated counterfactual candidates are preferred for explanation by participants. In a second study, we explore possible use cases for counterfactual explanations.

Identifizierung quantifizierbarer Bewertungsinhalte und -kategorien mittels Text Mining - Kersting, Joschka
Reading between the lines has so far been reserved for humans. The present dissertation addresses this research gap using machine learning methods. Implicit expressions are not comprehensible by computers and cannot be localized in the text. However, many texts arise on interpersonal topics that, unlike commercial evaluation texts, often imply information only by means of longer phrases. Examples are the kindness and the attentiveness of a doctor, which are only paraphrased (“he didn’t even look me in the eye”). The analysis of such data, especially the identification and localization of implicit statements, is a research gap (1). This work uses so-called Aspect-based Sentiment Analysis as a method for this purpose. It remains open how the aspect categories to be extracted can be discovered and thematically delineated based on the data (2). Furthermore, it is not yet explored how a collection of tools should look like, with which implicit phrases can be identified and thus made explicit (3). Last, it is an open question how to correlate the identified phrases from the text data with other data, including the investigation of the relationship between quantitative scores (e.g., school grades) and the thematically related text (4). Based on these research gaps, the research question is posed as follows: Using text mining methods, how can implicit rating content be properly interpreted and thus made explicit before it is automatically categorized and quantified? The uniqueness of this dissertation is based on the automated recognition of implicit linguistic statements alongside explicit statements. These are identified in unstructured text data so that features expressed only in the text can later be compared across data sources, even though they were not included in rating categories such as stars or school grades. German-language physician ratings from websites in three countries serve as the sample domain. The solution approach consists of data creation, a pipeline for text processing and analyses based on this. In the data creation, aspect classes are identified and delineated across platforms and marked in text data. This results in six datasets with over 70,000 annotated sentences and detailed guidelines. The models that were created based on the training data extract and categorize the aspects. In addition, the sentiment polarity and the evaluation weight, i. e., the importance of each phrase, are determined. The models, which are combined in a pipeline, are used in a prototype in the form of a web application. The analyses built on the pipeline quantify the rating contents by linking the obtained information with further data, thus allowing new insights. As a result, a toolbox is provided to identify quantifiable rating content and categories using text mining for a sample domain. This is used to evaluate the approach, which in principle can also be adapted to any other domain.

Capabilities and Limitations of Local Strategies in Dynamic Networks - Götte, Thorsten; Knollmann, Till; Meyer auf der Heide, Friedhelm; Scheideler, Christian; Werthmann, Julian (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

On-The-Fly Compute Centers II: Execution of Composed Services in Configurable Compute Centers - Karl, Holger; Maack, Marten; Meyer auf der Heide, Friedhelm; Pukrop, Simon; Redder, Adrian (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

Practical Cryptograhic Techniques for Secure and Privacy-Preserving Customer Loyalty Systems - Blömer, Johannes; Bobolz, Jan; Eidens, Fabian; Jager, Tibor; Kramer, Paul (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

Architectural Management of OTF Computing Markets - Gottschalk, Sebastian; Vorbohle, Christian; Kundisch, Dennis; Engels, Gregor; Wünderlich, Nacy V. (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

Robustness and Security - Blömer, Johannes; Eidens, Fabian; Jager, Tibor; Niehues, David; Scheideler, Christian (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

Dialogue-based Requirement Compensation and Style-adjusted Data-to-text Generation - Bäumer, Frederik Simon; Chen, Wei-Fan; Geierhos, Michaela; Kersting, Joschka; Wachsmuth, Henning (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

Configuration and Evaluation - Hanselle, Jonas Manuel; Hüllermeier, Eyke; Mohr, Felix; Ngonga Ngomo, Axel-Cyrille; Sherif, Mohamed; Tornede, Alexander; Wever, Marcel Dominik (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

The Market for Services: Incentives, Algorithms, Implementation - Haake, Claus-Jochen; Hehenkamp, Burkhard; Polevoy, Gleb (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

Composition Analysis in Unknown Contexts - Wehrheim, Heike; Hüllermeier, Eyke; Becker, Steffen; Becker, Matthias; Richter, Cedric; Sharma, Arnab (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

Reconfigurable Random Forest Implementation on FPGA - Raeisi Nafchi, Masood

Data Analytics for Predictive Maintenance of Time Series Data - Yadalam Murali Kumar, Nihal

The Optimal Depth of Variational Quantum Algorithms Is QCMA-Hard to Approximate - Bittel, Lennart; Gharibian, Sevag; Kliesch, Martin (Proceedings of the 38th Computational Complexity Conference (CCC))
Variational Quantum Algorithms (VQAs), such as the Quantum Approximate Optimization Algorithm (QAOA) of [Farhi, Goldstone, Gutmann, 2014], have seen intense study towards near-term applications on quantum hardware. A crucial parameter for VQAs is the depth of the variational ansatz used - the smaller the depth, the more amenable the ansatz is to near-term quantum hardware in that it gives the circuit a chance to be fully executed before the system decoheres. This potential for depth reduction has made VQAs a staple of Noisy Intermediate-Scale Quantum (NISQ)-era research. In this work, we show that approximating the optimal depth for a given VQA ansatz is intractable. Formally, we show that for any constant $ε>0$, it is QCMA-hard to approximate the optimal depth of a VQA ansatz within multiplicative factor $N^{1-ε}$, for $N$ denoting the encoding size of the VQA instance. (Here, Quantum Classical Merlin-Arthur (QCMA) is a quantum generalization of NP.) We then show that this hardness persists even in the "simpler" setting of QAOAs. To our knowledge, this yields the first natural QCMA-hard-to-approximate problems. To achieve these results, we bypass the need for a PCP theorem for QCMA by appealing to the disperser-based NP-hardness of approximation construction of [Umans, FOCS 1999].

On Cloud Assisted, Restricted, and Reosurce Constrained Scheduling - Pukrop, Simon

Utilizing Redundancy in Distributed Heterogeneous Storage - Schneider, Fabian

Graph Neural Network-based Anomaly Detection in Smart Grid Energy Consumption - Ranade, Amruta

Highly accurate deep compressed facial recognition - Ali, Osama

Virtual On-Demand Volunteer System Based on Delaunay Triangulation - Ashri, Nivedita

Multi-FPGA Designs and Scaling of HPC Challenge Benchmarks via MPI and Circuit-Switched Inter-FPGA Networks - Meyer, Marius; Kenter, Tobias; Plessl, Christian (ACM Transactions on Reconfigurable Technology and Systems)
While FPGA accelerator boards and their respective high-level design tools are maturing, there is still a lack of multi-FPGA applications, libraries, and not least, benchmarks and reference implementations towards sustained HPC usage of these devices. As in the early days of GPUs in HPC, for workloads that can reasonably be decoupled into loosely coupled working sets, multi-accelerator support can be achieved by using standard communication interfaces like MPI on the host side. However, for performance and productivity, some applications can profit from a tighter coupling of the accelerators. FPGAs offer unique opportunities here when extending the dataflow characteristics to their communication interfaces. In this work, we extend the HPCC FPGA benchmark suite by multi-FPGA support and three missing benchmarks that particularly characterize or stress inter-device communication: b_eff, PTRANS, and LINPACK. With all benchmarks implemented for current boards with Intel and Xilinx FPGAs, we established a baseline for multi-FPGA performance. Additionally, for the communication-centric benchmarks, we explored the potential of direct FPGA-to-FPGA communication with a circuit-switched inter-FPGA network that is currently only available for one of the boards. The evaluation with parallel execution on up to 26 FPGA boards makes use of one of the largest academic FPGA installations.
Keywords: General Computer Science

Improving the End-of-Line Test of Custom-Built Geared Motors using Clustering based on Neural Networks - N., N.

Neuro-Symbolic Class Expression Learning - Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (International Joint Conference on Artificial Intelligence)

Computing and Compressing Electron Repulsion Integrals on FPGAs - Wu, Xin; Kenter, Tobias; Schade, Robert; Kühne, Thomas; Plessl, Christian (2023 IEEE 31st Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM))
The computation of electron repulsion integrals (ERIs) over Gaussian-type orbitals (GTOs) is a challenging problem in quantum-mechanics-based atomistic simulations. In practical simulations, several trillions of ERIs may have to be computed for every time step. In this work, we investigate FPGAs as accelerators for the ERI computation. We use template parameters, here within the Intel oneAPI tool flow, to create customized designs for 256 different ERI quartet classes, based on their orbitals. To maximize data reuse, all intermediates are buffered in FPGA on-chip memory with customized layout. The pre-calculation of intermediates also helps to overcome data dependencies caused by multi-dimensional recurrence relations. The involved loop structures are partially or even fully unrolled for high throughput of FPGA kernels. Furthermore, a lossy compression algorithm utilizing arbitrary bitwidth integers is integrated in the FPGA kernels. To our best knowledge, this is the first work on ERI computation on FPGAs that supports more than just the single most basic quartet class. Also, the integration of ERI computation and compression it a novelty that is not even covered by CPU or GPU libraries so far. Our evaluation shows that using 16-bit integer for the ERI compression, the fastest FPGA kernels exceed the performance of 10 GERIS ($10 × 10^9$ ERIs per second) on one Intel Stratix 10 GX 2800 FPGA, with maximum absolute errors around $10^{-7}$ - $10^{-5}$ Hartree. The measured throughput can be accurately explained by a performance model. The FPGA kernels deployed on 2 FPGAs outperform similar computations using the widely used libint reference on a two-socket server with 40 Xeon Gold 6148 CPU cores of the same process technology by factors up to 6.0x and on a new two-socket server with 128 EPYC 7713 CPU cores by up to 1.9x.