Research on curriculum resources in mathematics education: a survey of the field - Rezat, Sebastian (ZDM – Mathematics Education)

Digitalisierung als Chance für alle Prinzipien guten Unterrichts - Barzel, Bärbel; Greefrath, Gilbert; Nagel, Mareike; Hoffmann, Max (mathematik lehren)
athematische Kompetenzen digital zu fördern und digitale Kompetenzen mathematisch zu fördern – dies ist eine Forderung der neuen Bildungsstandards mit Blick auf eine Bildung in der digitalen Welt. Gerade das Potenzial digitaler Medien für das fachliche Lernen wurde in vielen Studien bestätigt. Eine sinnvoll gestaltete Einbettung digitaler Medien bietet die Chance, allen fünf Prinzipien eines guten Unterrichts gerecht zu werden: Verstehensorientierung, Durchgängigkeit, kognitive Aktivierung, Lernendenorientierung & Adaptivität und Kommunikationsförderung. Die flächendeckende Nutzung digitaler Medien etabliert sich bislang nur zögerlich. Aber wie können wir Lehrkräfte stärken, digitale Medien sinnvoll einzusetzen? Wir möchten hier die Bandbreite der Möglichkeiten an Beispielen verdeutlichen, ihren Einsatz motivieren und Wege für einen guten Unterricht aufzeigen.

31 Sprachbildung im berufsbezogenen Mathematikunterricht. - Prediger, Susanne; Wessel, Lena (Berufs-und Fachsprache Deutsch in Wissenschaft und Praxis)

Implementation of Different Communication Structures for a Rocket Chip Based RISC-V Grid of Processing Cells - Luchterhandt, Lars; Nellius, Tom; Beck, Robert; Dömer, Rainer; Kneuper, Pascal; Müller, Wolfgang; Sadiye, Babak (MBMV 2024 - 27. Workshop Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen“)
RISC-V has received worldwide acceptance in the industry and by the academic community. As of today, multiple RISC-V applications and variants are under investigation for embedded IoT systems, from resource-limited single-core processors up to multi-core systems for High-Performance Computing (HPC). Recently, the Grid of Processing Cells (GPC) platform has been proposed as a scalable parallel grid-oriented network of processor cores with local memories. This paper describes a prototype design of the GPC platform for hardware implementation at Register-Transfer Level (RTL) based on modified RISC-V Rocket processors with scratchpad memories. It introduces a scalable Chisel-based implementation of the modified Rocket cores with RTL generation and a functional test using Verilator simulation. This work also includes the adaptation of the Chipyard software toolchain to extend the compiler to multi-core grids with different local address spaces.

A Scalable RISC-V Hardware Platform for Intelligent Sensor Processing - Palomero Bernardo, Paul; Schmid, Patrick; Bringmann, Oliver; Iftekhar, Mohammed; Sadiye, Babak; Müller, Wolfgang; Koch, Andreas; Jentsch, Eyck; Sauer, Axel; Feldner, Ingo; Ecker, Wolfgang (DATE 24 - Design Automation and Test in Europe)

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

Geodesic Interpolation of Frame-Wise Speaker Embeddings for the Diarization of Meeting Scenarios - Cord-Landwehr, Tobias; Boeddeker, Christoph; Zorilă, Cătălin; Doddipatla, Rama; Haeb-Umbach, Reinhold (ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

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

Visual Tracking Nonlinear Model Predictive Control Method for Autonomous Wind Turbine Inspection - Amer, Abdelhakim; Mehndiratta, Mohit; le Fevre Sejersen, Jonas; Pham, Huy Xuan; Kayacan, Erdal (2023 21st International Conference on Advanced Robotics (ICAR))

Analysis and Simulation of a Photonic Multiband FMCW Radar Sensor System using Nyquist Pulses - Kruse, Stephan; Schwabe, Tobias; Kneuper, Pascal; Kurz, Heiko G.; Meinecke, March-Michael; Scheytt, Christoph (German Microwave Conference (GeMiC))

Phase Noise Analysis of Photonic Radar Systems with Optical LO Distribution - Kruse, Stephan; Kneuper, Pascal; Schwabe, Tobias; Kurz, Heiko G.; Meinecke, Marc-Michael; Gonzalez-Huici, María A.; Scheytt, J. Christoph (nternational Conference on Microwaves for Intelligent Mobility (ICMIM))

Doppler Analysis of a Lidar-Photonic Radar Combined Sensor System - Kruse, Stephan; Brockmeier, Jan; Kneuper, Pascal; Schwabe, Tobias; Kurz, Heiko G.; Meinecke, Marc Michael; Scheytt, J. Christoph (International Radar Symposium (IRS))

Machine learning of continuous and discrete variational ODEs with convergence guarantee and uncertainty quantification - Offen, Christian
The article introduces a method to learn dynamical systems that are governed by Euler--Lagrange equations from data. The method is based on Gaussian process regression and identifies continuous or discrete Lagrangians and is, therefore, structure preserving by design. A rigorous proof of convergence as the distance between observation data points converges to zero is provided. Next to convergence guarantees, the method allows for quantification of model uncertainty, which can provide a basis of adaptive sampling techniques. We provide efficient uncertainty quantification of any observable that is linear in the Lagrangian, including of Hamiltonian functions (energy) and symplectic structures, which is of interest in the context of system identification. The article overcomes major practical and theoretical difficulties related to the ill-posedness of the identification task of (discrete) Lagrangians through a careful design of geometric regularisation strategies and through an exploit of a relation to convex minimisation problems in reproducing kernel Hilbert spaces.

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))