Combining NLP, speech recognition, and indexing. An AI-based learning assistant for higher education

This paper presents the ongoing development of HAnS (Hochschul-Assistenz-System), an Intelligent Tutoring System (ITS) designed to support self-directed digital learning in higher education. Initiated by twelve collaborating German universities and research institutes, HAnS is developed 2021–2025 with the goal of utilizing artificial intelligence (AI) and Big Data in academic settings to enhance technology-based learning. The system employs AI for speech recognition and the indexing of existing learning resources, enabling users to search and compile these materials based on various parameters. Here, we provide an overview of the project, showcasing how iterative design and development processes contribute to innovative educational research in the evolving field of AI-based ITS in higher education. Notwithstanding the potential of HAnS, we also deliberate upon the challenges associated with ensuring a suitable dataset for training the AI, refining complex algorithms for personalization, and maintaining data privacy. (DIPF/Orig.)In diesem Beitrag wird die laufende Entwicklung von HAnS (Hochschul-Assistenz-System) vorgestellt, einem Intelligenten Tutoring-System (ITS), das selbstgesteuertes digitales Lernen in der Hochschulbildung unterstützen soll. HAnS wurde von zwölf deutschen Hochschulen und Forschungsinstituten initiiert und wird 2021-2025 mit dem Ziel entwickelt, künstliche Intelligenz (KI) und Big Data im akademischen Umfeld zu nutzen, um technologiebasiertes Lernen zu verbessern. Das System nutzt KI für die Spracherkennung und die Indizierung vorhandener Lernressourcen und ermöglicht es den Nutzern, diese Materialien auf der Grundlage verschiedener Parameter zu suchen und zusammenzustellen. Hier geben wir einen Überblick über das Projekt und zeigen, wie iterative Design- und Entwicklungsprozesse zu innovativer Bildungsforschung auf dem sich entwickelnden Gebiet der KI-basierten ITS in der Hochschulbildung beitragen. Ungeachtet des Potenzials von HAnS gehen wir auch auf die Herausforderungen ein, die mit der Sicherstellung eines geeigneten Datensatzes für das Training der KI, der Verfeinerung komplexer Algorithmen für die Personalisierung und der Wahrung des Datenschutzes verbunden sind. (Autor

Tricyanidoferrates(−IV) and ruthenates(−IV) with non‐innocent cyanido ligands

Exceptionally electron-rich, nearly trigonal-planar tricyanidometalate anions [Fe(CN)(3)](7-) and [Ru(CN)(3)](7-) were stabilized in LiSr3[Fe(CN)(3)] and AE(3.5)[M(CN)(3)] (AE=Sr, Ba; M=Fe, Ru). They are the first examples of group 8 elements with the oxidation state of -IV. Microcrystalline powders were obtained by a solid-state route, single crystals from alkali metal flux. While LiSr3[Fe(CN)(3)] crystallizes in P6(3)/m, the polar space group P6(3) with three-fold cell volume for AE(3.5)[M(CN)(3)] is confirmed by second harmonic generation. X-ray diffraction, IR and Raman spectroscopy reveal longer C-N distances (124-128 pm) and much lower stretching frequencies (1484-1634 cm(-1)) than in classical cyanidometalates. Weak C-N bonds in combination with strong M-C pi-bonding is a scheme also known for carbonylmetalates. Instead of the formal notation [Fe-IV(CN-)(3)](7-), quantum chemical calculations reveal non-innocent intermediate-valent CN1.67- ligands and a closed-shell d(10) configuration for Fe, that is, Fe2-

Cavity-enhanced spectroscopy of a few-ion ensemble in Eu3+:Y2O3

We report on the coupling of the emission from a single europium-doped nanocrystal to a fiber-based microcavity under cryogenic conditions. As a first step, we study the properties of nanocrystals that are relevant for cavity experiments and show that embedding them in a dielectric thin film can significantly reduce scattering loss and increase the light-matter coupling strength for dopant ions. The latter is supported by the observation of a fluorescence lifetime reduction, which is explained by an increased local field strength. We then couple an isolated nanocrystal to an optical microcavity, determine its size and ion number, and perform cavity-enhanced spectroscopy by resonantly coupling a cavity mode to a selected transition. We measure the inhomogeneous linewidth of the coherent D-5(0)-F-7(0) transition and find a value that agrees with the linewidth in bulk crystals, evidencing a high crystal quality. We detect the fluorescence from an ensemble of few ions in the regime of power broadening and observe an increased fluorescence rate consistent with Purcell enhancement. The results represent an important step towards the efficient readout of single rare earth ions with excellent optical and spin coherence properties, which is promising for applications in quantum communication and distributed quantum computation

Predicting Wind Comfort in an Urban Area : A Comparison of a Regression- with a Classification-CNN for General Wind Rose Statistics

Wind comfort is an important factor when new buildings in existing urban areas are planned. It is common practice to use computational fluid dynamics (CFD) simulations to model wind comfort. These simulations are usually time-consuming, making it impossible to explore a high number of different design choices for a new urban development with wind simulations. Data-driven approaches based on simulations have shown great promise, and have recently been used to predict wind comfort in urban areas. These surrogate models could be used in generative design software and would enable the planner to explore a large number of options for a new design. In this paper, we propose a novel machine learning workflow (MLW) for direct wind comfort prediction. The MLW incorporates a regression and a classification U-Net, trained based on CFD simulations. Furthermore, we present an augmentation strategy focusing on generating more training data independent of the underlying wind statistics needed to calculate the wind comfort criterion. We train the models based on different sets of training data and compare the results. All trained models (regression and classification) yield an (Formula presented.) -score greater than 80% and can be combined with any wind rose statistic.This work is part of the Digital Twin Cities Centre supported by Sweden’s Innovation Agency Vinnova under Grant No. 2019-00041. This work was further supported by the Swedish Research Council for Sustainable Development Formas under the grants 2019-01169 and 2019-01885. The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at HPC2N partially funded by the Swedish Research Council through grant agreement no. 2018-05973.</p

Predicting Wind Comfort in an Urban Area : A Comparison of a Regression- with a Classification-CNN for General Wind Rose Statistics

Wind comfort is an important factor when new buildings in existing urban areas are planned. It is common practice to use computational fluid dynamics (CFD) simulations to model wind comfort. These simulations are usually time-consuming, making it impossible to explore a high number of different design choices for a new urban development with wind simulations. Data-driven approaches based on simulations have shown great promise, and have recently been used to predict wind comfort in urban areas. These surrogate models could be used in generative design software and would enable the planner to explore a large number of options for a new design. In this paper, we propose a novel machine learning workflow (MLW) for direct wind comfort prediction. The MLW incorporates a regression and a classification U-Net, trained based on CFD simulations. Furthermore, we present an augmentation strategy focusing on generating more training data independent of the underlying wind statistics needed to calculate the wind comfort criterion. We train the models based on different sets of training data and compare the results. All trained models (regression and classification) yield an (Formula presented.) -score greater than 80% and can be combined with any wind rose statistic.This work is part of the Digital Twin Cities Centre supported by Sweden’s Innovation Agency Vinnova under Grant No. 2019-00041. This work was further supported by the Swedish Research Council for Sustainable Development Formas under the grants 2019-01169 and 2019-01885. The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at HPC2N partially funded by the Swedish Research Council through grant agreement no. 2018-05973.</p

Addressing wind comfort in an urban area using an immersed boundary framework

Considering wind, air and heat comfort in designing new urban areas is still a challenge for city planners. Urban heat islands, or the phenomena of locally increased temperatures in urban areas compared to their rural surroundings, are becoming increasingly problematic with global warming and the rise of urbanization. Therefore, new areas must be planned considering appropriate ventilation to mitigate these high-temperature regions and cooling strategies, such as green infrastructures, must be considered. Typically, most of the comfort criteria are evaluated and assessed in the final stages of urban planning when further strategic interventions are no longer possible. Here, a numerical framework is tested that urban planners can use as a future tool to analyze complex fluid dynamics and heat transfer in the early stages of urban planning. The framework solves the RANS equations using an immersed boundary approach to discretize the complex urban topography in a cartesian octree grid. The grid is automatically generated, eliminating the complex pre-processing of urban topographies and making the framework accessible to all users. The results are validated against experimental data from wind tunnel measurements of wind-driven ventilation in street canyons. After validation, we will apply the numerical framework to estimate the wind comfort in an idealized urban area. Finally, guidelines will be provided on the choice of minimum grid sizes required to capture the relevant flow structures inside a canyon accurately