Fraunhofer Institute for Wind Energy Systems
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GiCCS: A German in-Context Conversational Similarity Benchmark
No full textThe Semantic textual similarity (STS) task is commonly used to evaluate the semantic representations that language models (LMs) learn from texts, under the assumption that good-quality representations will yield accurate similarity estimates. When it comes to estimating the similarity of two utterances in a dialogue, however, the conversational context plays a particularly important role. We argue for the need of benchmarks specifically created using conversational data in order to evaluate conversational LMs in the STS task.
We introduce GiCCS, a first conversational STS evaluation benchmark for German. We collected the similarity annotations for GiCCS using best-worst scaling and presenting the target items in context, in order to obtain highly-reliable context-dependent similarity scores.
We present benchmarking experiments for evaluating LMs on capturing the similarity of utterances. Results suggest that pretraining LMs on conversational data and providing conversational context can be useful for capturing similarity of utterances in dialogues. GiCCS will be publicly available to encourage benchmarking of conversational LMs
Interprofessional evaluation of the training of paramedic trainees and participants of the advanced training in emergency care in extended reality
No full textThe research data refer to a formative project evaluation in the BMBF-funded project ViTAWiN. In addition to socio-demographic data, presence (Igroup Presence Questionnaire, Schubert et al.), simulator sickness (Sim. Sickn. Quest. Kennedy), task and cognitive load (NASA TLX, Hart & Staveland), situational motivation (Situational Motivation Scale), Training Evaluation (Training Evaluation Inventory, Ritzmann et al.) and usability (System Usability Scale according to Brooke) are recorded.Included are two files in CSV format that can be opened by the usual evaluation programmes
Raw Data: Gas phase lubrication study with an organic friction modifier
No full textXPS / tribometer / mass spectrometer raw data supporting the publication "Gas phase lubrication study with an organic friction modifier
Digital Ecosystem Service Classification
No full textApplication of digital ecosystem criteria on digital ecosystem service candidates, to classify these candidates as digital ecosystem services or non digital ecosystem services.Note: In case one criterium for a digital ecosystem service candidate is not fulfilled, the remaining ones have not been checked, indicated through "n/a".Semicolon-delimited CSV file with UTF-8 encodin
CovidRestrict: mobility restrictions in German Federal States in 2020
Get PDFThe CovidRestrict project collected data on mobility-related restrictions that were adopted by the governments of six German Federal States in the first wave of the COVID-19 pandemic (01 January - 31 July 2020). The data also discerns between stringency levels of these regulations
Coefficient of thermal expansion (CTE) of C12A7:e- ceramic
No full textThe compound [Ca24Al28O64]4+(4e-) named as C12A7:e- has outstanding properties because of a low work function (2.4-2.6 eV) and high electron conductivity due to a cage-like crystal structure. For the application of the material as hollow cathodes in small satellite propulsion systems, its preparation as sinterable glass-ceramics via the powder route is purposeful. The coefficient of thermal expansion CTE is
between 4.2 and 6.0 × 10−6 K−1 (RT-1000°C).This work was supported by the European Union’s Horizon 2020 research and innovation program under grant agreement No 828902 (E.T. PACK project) and grant agreement No 870336 (iFACT project)
Supplementary data
No full textThe data includes SO2 measurements, which were carried out under laboratory conditions. Furthermore, the data includes the measured values on a container ship field test. In addition, the python script, which was used to analyse the gathered data, can be found
Künstliche Intelligenz, Emotionen und Gemeinwesen
No full textIn der Umfrage geht es um emotionale Assoziationen zu automatisierten Entscheidungen durch Maschinen im Vergleich zu Entscheidungen durch Menschen ebenso wie um die Bewertung des Einsatzes von Künstlicher Intelligenz im Gemeinwesen hinsichtlich positiver oder negativer Effekte auf die Gesellschaft.
Für die Umfrage werden neben den Datensätze (sowohl mit Werten als auch mit Label) die Fragebögen mit dem Codeschema bereitgestellt. Zusätzlich werden die von GMS verfügbaren Angaben zu den Methoden der Datenvalidierung, Bereinigung und Gewichtung der Datensätze bereitgestellt.Die Daten werden jeweils in zwei Formaten zugänglich gemacht. Formate für Daten (mit Werten und mit Label): .csv und .xlsx Formate für Fragebogen: .docx und .odt Formate für Methodenberichte: .pd
Multi-professional evaluation of the training of paramedic trainees and participants of the advanced training in emergency care in extended reality
No full textThe research data refer to a formative project evaluation in the BMBF-funded project ViTAWiN.In addition to socio-demographic data from 41 participants, presence (Igroup Presence Questionnaire, Schubert et al.), simulator sickness (Sim. Sickn. Quest., Kennedy), situational motivation (Situational Motivation Scale, Guay) and usability (System Usability Scale, Brook) are recorded.It is a raw data file in CSV format that can be opened by the common evaluation programmes
CROWDSS: A crowdsourced, ecologically-valid dialogue dataset for German
No full textThe CROWDSS dataset (Crowdsourced Wizard of Oz Dialogue dataset based on Situated Scenarios) contains 113 German dialogues collected in a Wizard-of-Oz fashion (i.e., simulating human-machine interaction).
To refer to CROWDSS in any publication, please cite the following paper:
Frommherz, Y. and Zarcone, A. (2021). Crowdsourcing ecologically-valid dialogue data for German. In Frontiers in Computer Science, Vol 3, doi: 10.3389/fcomp.2021.686050The dataset is structured as follows: Each dialogue is saved as a dictionary (with the dialogue id as key) containing 1) the scenario which was used for eliciting the corresponding dialogue and 2) the log.
The log is a list of turns made by user and assistant, where each turn again is a dictionary containing the actual turn ("text"), who uttered it ("role") as well as the corresponding dialogue act annotations, following the scheme in Pareti and Lando (2019) but with some modifications (see annotation guidelines). The dialogue acts are saved as a list with the label as well as the start and end indices in the text.
The dialogues were collected on a turn-by-turn basis and using a one-to-many ratio (see paper). The dialogue ids consist of numbers separated by dots. The first number corresponds to the the 30 dialogue beginnings that where collected in batch 1 (see paper). Since we assigned each of these dialogues to multiple participants in batch 2, dialogues sharing the first number in their id share both the same scenario and the first turn, etc