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

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.

Breaking the exascale barrier for the electronic structure problem in ab-initio molecular dynamics - Schade, Robert; Kenter, Tobias; Elgabarty, Hossam; Lass, Michael; Kühne, Thomas; Plessl, Christian (The International Journal of High Performance Computing Applications)
The non-orthogonal local submatrix method applied to electronic structure–based molecular dynamics simulations is shown to exceed 1.1 EFLOP/s in FP16/FP32-mixed floating-point arithmetic when using 4400 NVIDIA A100 GPUs of the Perlmutter system. This is enabled by a modification of the original method that pushes the sustained fraction of the peak performance to about 80%. Example calculations are performed for SARS-CoV-2 spike proteins with up to 83 million atoms.
Keywords: Hardware and Architecture; Theoretical Computer Science; Software

Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions - Tornede, Alexander

IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran - Gusmita, Ria Hari; Firmansyah, Asep Fajar; Moussallem, Diego; Ngonga Ngomo, Axel-Cyrille (Natural Language Processing and Information Systems)
Indonesian is classified as underrepresented in the Natural Language Processing (NLP) field, despite being the tenth most spoken language in the world with 198 million speakers. The paucity of datasets is recognized as the main reason for the slow advancements in NLP research for underrepresented languages. Significant attempts were made in 2020 to address this drawback for Indonesian. The Indonesian Natural Language Understanding (IndoNLU) benchmark was introduced alongside IndoBERT pre-trained language model. The second benchmark, Indonesian Language Evaluation Montage (IndoLEM), was presented in the same year. These benchmarks support several tasks, including Named Entity Recognition (NER). However, all NER datasets are in the public domain and do not contain domain-specific datasets. To alleviate this drawback, we introduce IndQNER, a manually annotated NER benchmark dataset in the religious domain that adheres to a meticulously designed annotation guideline. Since Indonesia has the world’s largest Muslim population, we build the dataset from the Indonesian translation of the Quran. The dataset includes 2475 named entities representing 18 different classes. To assess the annotation quality of IndQNER, we perform experiments with BiLSTM and CRF-based NER, as well as IndoBERT fine-tuning. The results reveal that the first model outperforms the second model achieving 0.98 F1 points. This outcome indicates that IndQNER may be an acceptable evaluation metric for Indonesian NER tasks in the aforementioned domain, widening the research’s domain range.

Partial observations, coarse graining and equivariance in Koopman operator theory for large-scale dynamical systems - Peitz, Sebastian; Harder, Hans; Nüske, Feliks; Philipp, Friedrich; Schaller, Manuel; Worthmann, Karl (arXiv:2307.15325)
The Koopman operator has become an essential tool for data-driven analysis, prediction and control of complex systems, the main reason being the enormous potential of identifying linear function space representations of nonlinear dynamics from measurements. Until now, the situation where for large-scale systems, we (i) only have access to partial observations (i.e., measurements, as is very common for experimental data) or (ii) deliberately perform coarse graining (for efficiency reasons) has not been treated to its full extent. In this paper, we address the pitfall associated with this situation, that the classical EDMD algorithm does not automatically provide a Koopman operator approximation for the underlying system if we do not carefully select the number of observables. Moreover, we show that symmetries in the system dynamics can be carried over to the Koopman operator, which allows us to massively increase the model efficiency. We also briefly draw a connection to domain decomposition techniques for partial differential equations and present numerical evidence using the Kuramoto--Sivashinsky equation.

Finite-data error bounds for Koopman-based prediction and control - Nüske, Feliks; Peitz, Sebastian; Philipp, Friedrich; Schaller, Manuel; Worthmann, Karl (Journal of Nonlinear Science)
The Koopman operator has become an essential tool for data-driven approximation of dynamical (control) systems in recent years, e.g., via extended dynamic mode decomposition. Despite its popularity, convergence results and, in particular, error bounds are still quite scarce. In this paper, we derive probabilistic bounds for the approximation error and the prediction error depending on the number of training data points; for both ordinary and stochastic differential equations. Moreover, we extend our analysis to nonlinear control-affine systems using either ergodic trajectories or i.i.d. samples. Here, we exploit the linearity of the Koopman generator to obtain a bilinear system and, thus, circumvent the curse of dimensionality since we do not autonomize the system by augmenting the state by the control inputs. To the best of our knowledge, this is the first finite-data error analysis in the stochastic and/or control setting. Finally, we demonstrate the effectiveness of the proposed approach by comparing it with state-of-the-art techniques showing its superiority whenever state and control are coupled.

Multi-Objective Optimization for Sparse Deep Neural Network Training - Hotegni, Sedjro Salomon; Peitz, Sebastian; Berkemeier, Manuel Bastian (arXiv:2308.12243)
Different conflicting optimization criteria arise naturally in various Deep Learning scenarios. These can address different main tasks (i.e., in the setting of Multi-Task Learning), but also main and secondary tasks such as loss minimization versus sparsity. The usual approach is a simple weighting of the criteria, which formally only works in the convex setting. In this paper, we present a Multi-Objective Optimization algorithm using a modified Weighted Chebyshev scalarization for training Deep Neural Networks (DNNs) with respect to several tasks. By employing this scalarization technique, the algorithm can identify all optimal solutions of the original problem while reducing its complexity to a sequence of single-objective problems. The simplified problems are then solved using an Augmented Lagrangian method, enabling the use of popular optimization techniques such as Adam and Stochastic Gradient Descent, while efficaciously handling constraints. Our work aims to address the (economical and also ecological) sustainability issue of DNN models, with a particular focus on Deep Multi-Task models, which are typically designed with a very large number of weights to perform equally well on multiple tasks. Through experiments conducted on two Machine Learning datasets, we demonstrate the possibility of adaptively sparsifying the model during training without significantly impacting its performance, if we are willing to apply task-specific adaptations to the network weights. Code is available at https://github.com/salomonhotegni/MDMTN.

Efficient time stepping for numerical integration using reinforcement learning - Dellnitz, Michael; Hüllermeier, Eyke; Lücke, Marvin; Ober-Blöbaum, Sina; Offen, Christian; Peitz, Sebastian; Pfannschmidt, Karlson (SIAM Journal on Scientific Computing)
Many problems in science and engineering require an efficient numerical approximation of integrals or solutions to differential equations. For systems with rapidly changing dynamics, an equidistant discretization is often inadvisable as it results in prohibitively large errors or computational effort. To this end, adaptive schemes, such as solvers based on Runge–Kutta pairs, have been developed which adapt the step size based on local error estimations at each step. While the classical schemes apply very generally and are highly efficient on regular systems, they can behave suboptimally when an inefficient step rejection mechanism is triggered by structurally complex systems such as chaotic systems. To overcome these issues, we propose a method to tailor numerical schemes to the problem class at hand. This is achieved by combining simple, classical quadrature rules or ODE solvers with data-driven time-stepping controllers. Compared with learning solution operators to ODEs directly, it generalizes better to unseen initial data as our approach employs classical numerical schemes as base methods. At the same time it can make use of identified structures of a problem class and, therefore, outperforms state-of-the-art adaptive schemes. Several examples demonstrate superior efficiency. Source code is available at https://github.com/lueckem/quadrature-ML.

Mit dem Leben Schritt halten - Eine Analyse des Wearable-Dispositivs - Schloots, Franziska Margarete
„Lerne deinen Körper besser kennen“, „Das Beste für deine Gesundheit“ und „Ihre Transformation beginnt jetzt“ - mit Versprechen wie diesen vermitteln die Produkttexte von Wearables wie Fitnesstracker und Smartwatches ein ganz bestimmtes Bild ihrer vorgesehenen Nutzer*innen und deren Nutzung. Verbunden mit den kleinen, am Handgelenk getragenen Geräten sind Fragen nach Erkenntnisgewinn und Kontrollverlust, Selbstoptimierung und Quantifizierungslogiken, Eigenverantwortung und Fremdsteuerung. Die vorliegende Arbeit widmet sich diesem komplexen Spannungsfeld und verfolgt dabei einen multiperspektivischen Ansatz: im Rahmen einer Dispositivanalyse werden die einzelnen Elemente des Wearable-Dispositivs als eigenständige, empirisch zu untersuchende Analysegegenstände betrachtet, um so das Zusammenwirken und die komplexe Beziehung von Diskursen, Gegenständen, Nutzung, Subjekten und Gesellschaft zu erforschen. Ein besonderes Erkenntnisinteresse liegt dabei auf dem Wissen, was sich über Wearables etabliert hat und sich in den Alltagspraktiken der Nutzer*innen widerspiegelt sowie bei der Frage nach den möglichen Funktionen und Auswirkungen des Wearable-Dispositivs.
Keywords: Selbstvermessung; Dispositivanalyse; Gesundheitsdiskurs; Quantifizierungsgesellschaft; Wearables; Selbstoptimierung

ElectricGrid.jl - A Julia-based modeling and simulationtool for power electronics-driven electric energy grids - Wallscheid, Oliver; Peitz, Sebastian; Stenner, Jan; Weber, Daniel; Boshoff, Septimus; Meyer, Marvin; Chidananda, Vikas; Schweins, Oliver (Journal of Open Source Software)
Keywords: General Earth and Planetary Sciences; General Environmental Science

Paradigmenwechsel vom klassischen zum datengetriebenen Problemlösen im Informatikunterricht - Höper, Lukas; Schulte, Carsten (MNU journal)

On the impossibility of surviving (iterated) deletion of weakly dominated strategies in rational MPC - Blömer, Johannes; Bobolz, Jan; Bröcher, Henrik

A Generic Construction of an Anonymous Reputation System and Instantiations from Lattices - Blömer, Johannes; Bobolz, Jan; Porzenheim, Laurens Alexander

Minimizing Eye Movements and Distractions in Head-Mounted Augmented Reality through Eye-Gaze Adaptiveness - Wecker, Daniel; Yigitbas, Enes (Proceedings of the ACM Symposium on Spatial User Interaction (SUI 2023))

GaMoVR: Gamification-Based UML Learning Environment in Virtual Reality - Yigitbas, Enes; Schmidt, Maximilian; Bucchiarone, Antonio; Gottschalk, Sebastian; Engels, Gregor (Science of Computer Programming)

Transitional Cross Reality Interfaces for Spatially Demanding Search and Collect Tasks - Schmidt, Leonard; Yigitbas, Enes (Proceedings of the 27th International Workshop on Personalization and Recommendation)

ABIS 2023 – 27th International Workshop on Personalization and Recommendation - Neumayr, Thomas; Yigitbas, Enes; Augstein, Mirjam; Herder, Eelco (Proceedings of the Mensch & Computer (2023))

Routing in Hypergraphs - Deppe, Volker

ARIadne – An Explanation Model for Digital Artefacts - Winkelnkemper, Felix; Höper, Lukas; Schulte, Carsten (Informatics in Education)
When it comes to mastering the digital world, the education system is more and more facing the task of making students competent and self-determined agents when interacting with digital artefacts. This task often falls to computing education. In the traditional fields of computing education, a plethora of models, guidelines, and principles exist, which help scholars and teachers identify what the relevant aspects are and which of them one should cover in the classroom. When it comes to explaining the world of digital artefacts, however, there is hardly any such guiding model. The ARIadne model introduced in this paper provides a means of explanation and exploration of digital artefacts which help teachers and students to do a subject analysis of digital artefacts by scrutinizing them from several perspectives. Instead of artificially separating aspects which target the same phenomena within different areas of education (like computing, ICT or media education), the model integrates technological aspects of digital artefacts and the relevant societal discourses of their usage, their impacts and the reasons behind their development into a coherent explanation model.
Keywords: Computer Science Applications; Communication; Education; General Engineering