Reviewerly/exHarmony huggingface dataset:
ENHANCING EDUCATIONAL EFFORTS BY IMPLEMENTING A STUDENT-CENTERED WEBSITE FOR AN ENGINEERING DEPARTMENT

This paper describes the methods used to further enhance our educational efforts by implementing a student-centered website for our department. The main objective of the work is to describe the process and survey carried in order to achieve a good web presence. Our structured survey questionnaire helped us prove our hypothesis. The survey hypothesis was that well-formated and mobile friendly websites encourage students to do better reaserch, to check webpages more often and share information more. Our survey objective is to prove that multi-device accessibility to websites aids students in their research. Another goal is to find if students check the site more often if its responsive. The third research point underlines if students tend to share the information more if its on a well-formated and mobile-friendly website. The targeted survey population are students enrolled in engineering degrees beginning with first year students and up top PhDs. The survey demographics includes young adults in their twenties located in South-East Europe from Romania. Our results show that an increasing number of students are looking for information on their university website and efforts need to be made to provide availability and screen-friendly web-applications. Our research also shows an important increase in screen time versus indoor library reading time amoung our students. The survey questionnaire is structured so that we can better understand our students' needs when they log on to our web application. This work was looking for student feedback in order to modify the parts of the websites to suit our students' needs.
AUTOMATED MODELING OF ROMANIAN LITERARY TRENDS IN HISTORY USING TOPICS OVER TIME AND CO-OCCURENCES

The Chronology of Romanian Literary Life (also referred to as CVLR) is an ambitious effort conducted by the "G. C?linescu" Institute of Literary History and Theory to present the most important literature-related events from Romania, covering a period of 50 years, starting with World War II up until the beginning of the 3rd century. Our work aims to capture the evolution of trends in the Romanian literary space using a custom LDA topic model, focused also on the changes in the structure of topics across time, as well as the relations between the topics. In the Trends Over Time approach, the discovery of topics is related not only to the co-occurrences of words, but also to the timestamps associated to events (temporal information). Our results depicting the dynamics of trends over time are interesting both from an e-learning perspective, but also from literary and historical perspectives. Patterns of co-occurrences are discovered and visualizations of the topics' evolution in time are introduced to provide valuable insights from CVLR. Moreover, topic entropy is used to assess the diversity of ideas covered by a topic. The obtained results are promising, reflecting the most important historical periods and the evolution of trends in a coherent manner in relation to the historical events. By showing trends dynamics over time is interesting both from an eLearning perspective and from Literary and Historical perspective. Our study represents a solid foundation for further analyses on Romanian literary events by relying on automated models grounded in advanced Natural Language Processing techniques.
BLOCKCHAIN IN ELEARNING. PERSPECTIVES FOR MILITARY LEARNING ARCHITECTURES.

Digitization is the process of converting information into a digital format. The economy that is based on digital computing technologies is known as Digital Economy. The digital economy is also sometimes called the Internet Economy or Web Economy. Right now, in most of the countries, inside the people's wallet, they probably have a couple of credit cards, an identification card, automatic teller machine cards (ATM card) and maybe a few other plastic cards. Without realizing it, these plastic cards have become a very important part of their life. Currently smart cards can be seen in the transportation, telecommunication and retail sectors. In this paper we are providing the combination of digitization and digital economy and aim to propose designing and implementation of a Student Card System for higher educational institutes using smart card technology. Smart card is a card that contains a barcode which is nothing but a unique card that is assigned to the student. A barcode is a series of alternating dark and light stripes that are read by an optical scanner. It is an automatic identification technology. A barcode is an optical, machine-readable, representation of data the data usually describes something about the object that carries the barcode. The student smart card can be used to ease the work of students. This card is useful for the students in places like library, canteen, stationary shops and online storage of important documents. From there we can see the potential and power of smart cards their versatility and usability.
Employing machine learning pickers for routine global earthquake monitoring with SeisComP: What are the benefits and how can we quantify the uncertainty of picks?

Recent years have seen the development of several very powerful machine learning pickers for P and S waves. The recent development of the SeisBench platform (https://github.com/seisbench/seisbench) in combination with mixed regional teleseismic benchmark datasets published by the NEIC (USGS) and GEOFON (GFZ Potsdam) enabled the retraining of the most popular picker neural network models (PhaseNet and EQTransformer) optimised for global monitoring applications in the benchmark study of M&amp;#252;nchmeyer et al (2021, J. Geophys. Res.). &amp;#160;In this contribution we introduce a module scdlpicker, which connects SeisBench to SeisComP (https://www.seiscomp.de/) &amp;#160;through a client submodule, which listens to new event detections from the regular SeisComP automatic detection system and triggers repicking of those events with any picker implemented in SeisBench, using the improved picks to trigger a relocation. The machine learning picks are subsequently available within the SeisComP GUI in case further manual refinement or checking is desired.&amp;#160;We demonstrate application of this system with the GEOFON global earthquake monitoring service (https://geofon.gfz-potsdam.de/eqexplorer), evaluating the benefits of using the machine learning picker with respect to the conventional workflow relying on traditional pickers with respect to timeliness of reporting earthquakes and reduction of manual work load, and improvement in the number of high quality picks available for each event.&amp;#160;The quantification of the uncertainty of machine learning picks is important when weighing the contribution of different picks in many location algorithms, yet this information is not readily available from machine learning pickers. They do return, however, a characteristic function (nominally the confidence in the pick), whose properties might correlate with the uncertainty of the pick. We will show whether and how the picking uncertainty correlates with properties of the characteristic function.&amp;#160;
Benchmarking Study of EQTransformer Autopicker for Seismic Phase Identification in Northern Chile

One of the vital open questions in seismology is rapid, high quality phase identification and picking. Measurements of earthquake arrival time or phase picking are often done by expert judgment with many years of experience. Due to advances in technology and seismometer deployment, the amount of recorded data has increased dramatically in the previous decade, leading up to a point, where it has become almost impossible for humans to deal with this amount of data flow. Therefore, automatic picking algorithms are being used. &amp;#160;In recent years multiple machine learning algorithms have been introduced that bear the potential to combine both, high picking accuracy and the capability of processing large amounts of data.&amp;#160;In this contribution, we demonstrate the performance of the EQTransformer autopicker, when applied to continuous seismic data from the Northern Chilean subduction zone. To test this deep neural network, we chose a random day and carefully hand picked the continuous data on 18 IPOC stations, selecting only combinations of picks which should lead to locatable events (e.g. with at least five picks). This results in the identification of &amp;#160;3040 P and 2310 S picks. We compare the results of two different training versions of EQTransformer with hand-picked data and with the IPOC seismicity catalog. As it turns out, the comparison is not straightforward, because the evaluation of the picks is highly complicated, given that the true number and type of phase arrivals is and remains unknown. However, the autopicker is able to detect most of the hand-picked phases and arrival times. It outperforms the IPOC catalog by a factor of about 10-15 and thusly it appears to be a valid alternative for advanced seismic catalog construction.
Incorporating Training Data Uncertainty in Machine Learning Models for Satellite Imagery

Supervised machine learning (ML) models rely on labels in the training data to learn the patterns of interest. In Earth science applications, these labels are usually collected by humans either as labels annotated on imagery (such as land cover class) or as in situ measurements (such as soil moisture). Both annotations and in situ measurements contain uncertainties resulting from factors such as class misinterpretation and device error. These training data uncertainties propagate through the ML model training and result in uncertainties in the model outputs. Therefore, it is essential to quantify these uncertainties and incorporate them in the model [1].In this research, we will present results of inputting semantic segmentation label uncertainties into the model training and show that it improves model performance. The experiment is run using the LandCoverNet training dataset which contains global land cover labels based on time-series of Sentinel-2 multispectral imagery [2]. These labels are human annotations derived using a consensus algorithm based on the input labels from three independent annotators. The training dataset contains the consensus label and consensus score, and we treat the latter as a measure of uncertainty for each labeled pixel in the data. Our model architecture is a Convolutional Neural Network (CNN) trained on a subset of LandCoverNet with the rest of the dataset used for validation. We compare the results of this experiment with the same model trained on the dataset without the uncertainty information and show the improvement in the accuracy of the model.&amp;#160;[1] Elmes, A., Alemohammad, H., Avery, R., Caylor, K., Eastman, J., Fishgold, L., Friedl, M., Jain, M., Kohli, D., Laso Bayas, J., Lunga, D., McCarty, J., Pontius, R., Reinmann, A., Rogan, J., Song, L., Stoynova, H., Ye, S., Yi, Z.-F., Estes, L. (2020). Accounting for Training Data Error in Machine Learning Applied to Earth Observations. Remote Sensing, 12(6), 1034. https://doi.org/10.3390/rs12061034[2] Alemohammad, H., Booth, K. (2020). LandCoverNet: A global benchmark land cover classification training dataset. NeurIPS 2020 Workshop on AI for Earth Sciences. http://arxiv.org/abs/2012.03
EarthQA: A Question Answering Engine for Earth Observation Data Archives

The standard way for earth observation experts or users to retrieve images from image archives (e.g., ESA's Copernicus Open Access Hub) is to use a graphical user interface, where they can select the geographical area of the image they are interested in and additionally they can specify some other metadata, such as sensing period, satellite platform and cloud cover. In this work, we are developing the question-answering engine EarthQA that takes as input a question expressed in natural language (English) that asks for satellite images satisfying certain criteria and returns links to such datasets, which can be then downloaded from the CREODIAS cloud platform. To answer user questions, EarthQA queries two interlinked knowledge graphs: a knowledge graph encoding metadata of satellite images from the CREODIAS cloud platform (the SPARQL endpoint of CREODIAS) and the well-known knowledge graph DBpedia. Hence, the questions can refer to image metadata (e.g., satellite platform, sensing period, cloud cover), but also to more generic entities appearing in DBpedia knowledge graph (e.g., lake, Greece). In this way, the users can ask questions like &amp;#8220;Find all Sentinel-1 GRD images taken during October 2021 that show large lakes in Greece having an area greater than 100 square kilometers&amp;#8221;. EarthQA follows a template-based approach to translate natural language questions into formal queries (SPARQL). Initially, it decomposes the user question by generating its dependency parse tree and then automatically disambiguates the components appearing in the question to elements of the two knowledge graphs.&amp;#160; In particular, it automatically identifies the spatial or temporal entities (e.g., &amp;#8220;Greece&amp;#8221;, &amp;#8220;October 2021&amp;#8221;), concepts (e.g., &amp;#8220;lake&amp;#8221;), spatial or temporal relations (e.g., &amp;#8220;in&amp;#8221;, &amp;#8220;during&amp;#8221;), properties (e.g., &amp;#8220;area&amp;#8221;) and product types (e.g., &amp;#8220;Sentinel-1 GRD&amp;#8221;) and other metadata (e.g., &amp;#8220;cloud cover below 10%&amp;#8221;) mentioned in the question and maps them to the respective elements appearing in the two knowledge graphs (dbr:Greece, dbo:Lake, dbp:area, etc). After this, the SPARQL query is automatically generated.
The scaling properties of fault networks and their relationship with the size distribution of orogen-internal seismicity

Understanding orogen-internal seismic deformation in regions with diffuse spatial earthquake occurrence is challenging. To gain deeper insights into the processes driving seismic fault reactivation, it is crucial to obtain information on the ubiquitous pre-existing fracture patterns. In orogens with long tectonic histories &amp;#8211; such as the Alps &amp;#8211; such patterns can be complex, and information on their appearance is mainly limited to observations of faults at the surface, while the detailed patterns at depth remain mostly unknown. Moreover, the link between such surface-based fault observations and active seismicity is often ambiguous. However, it has been shown that both earthquake magnitudes (Gutenberg-Richter law) and various fault properties (e.g., length, displacement) follow power-law distributions.In this work, we aim to investigate the potential relationship between the scaling properties of faults and earthquakes, which has been little explored. To this end, we use statistical tools based on field data collected with remote sensing techniques at different scales to quantitatively characterize the length distributions of exposed fault networks at different study sites in the southwestern Swiss Alps. Due to the good outcrop conditions at high elevations, the dense seismic monitoring network, and the enhanced earthquake activity, this region provides an ideal natural laboratory for the study of orogen-internal seismicity. By combining fault trace maps from three different scales, we are able to derive power law parameters and decipher similarities in scaling exponents for the different sites studied. Assuming that the fault networks exist in a similar form at depth and form the pre-existing discontinuities along which recent earthquakes develop, we compare the derived scaling laws with the frequency-magnitude distribution of local seismicity over the past 15 years. Here we find similar scaling properties between the seismicity and fracture networks only at depths below 3 km. However, in shallower regions, the large discrepancy between the scaling laws suggests that partial seismic ruptures of individual fault segments are more common than at greater depths. Such a statistical comparison of fault and earthquake scaling laws provides interesting insights into orogen-internal seismic deformation and fault reactivation processes that also have implications for regional seismic hazard.
Did the input data inflation via regression-based levelling of data from various analytical protocols affect the performance of geochemical predictive models?

The geochemical predictive models based on environmental correlations were proved to return reliable predictions when the covariates&amp;#8217; feature space is representatively covered by samples. It may be efficient to combine the data from different sampling campaigns. Classical analytical methods of wet mineralisation of trace elements with spectral termination are both cost- and time-consuming. For trace elements, the most common routine is the usage of aqua regia together with &lt; 2 mm sieved samples. Nevertheless, in the Czech Republic, the common usage of partial extraction using cold 2 mol/L nitric acid had preceded the recent practise of aqua regia mineralisation and had left rich legacy datasets from long-term soil monitoring. We tested several models (i.e. simple linear models, step-wise multiple linear models and generalised additive models) to provide a reliable and effective (parsimonious) model for data recalculation between various extractions based on parallel soil analysis of 6,000 representative soil samples. Since all the regression models left highly spatially autocorrelated residuals, we also tested several spatial auto-regressive models among which the geographically weighted regression was found useful. Finally, we tested the predictive models using a quantile regression forest model where the environmental covariates for lithological sources (parent material classification combined with airborne geophysical data) and human-induced sources (night-time lights data, density of mining dumps, density of traffic routes, elements&amp;#8217; deposition rates) were combined with data from remotely sensed surface characterisation (Sentinel-2), multiscale representation of terrain (Gaussian pyramids), and the spatial autoregressive structure of target features (quantile-based buffer distances). We trained several QRF models in high resolution (20 x 20 m) where we researched the effects of using true measured data (3,300 samples) and dataset inflated with regression-recalculated data (11,000 recalculated samples from global and local regression-based levelling) and the potential effects of using the goodness of fit criteria from various regression-based recalculations between methods as weights for final QRF predictive models. The research has been supported by the Technology Agency of the Czech Republic under the research project No. SS03010364.
Integration between space- and ground-based observations in areas prone to volcanic hazard: the experience of Mt. Etna Supersite

The Geohazard Supersites and Natural Laboratories (GSNL) is an initiative of the Group of Earth Observation (GEO) that has started in 2007 with the Frascati declaration, in which the GeoHazards Community of Practice recommended to: &amp;#8220;... stimulate international and intergovernmental effort to monitor and study selected reference (geologic hazards) sites, by establishing open access to relevant datasets according to GEO principles, to foster collaboration between various partners and end users&amp;#8221;. Since the beginning the main idea has been the improvement of the hazard assessment by combining space- and ground-based datasets provided by the Space Agencies and the research institutions managing the in-situ observation systems, respectively.According to the definition of Supersite, since the early stage of the GSLN initiative, Mt. Etna has been identified as one of the Supersites due to its almost continuous eruptive activity, the great amount of satellite and in-situ data available, and the advanced in-situ multi-parametric observing systems. Officially, Mt. Etna is a Permanent Supersite since 2014. The Space Agencies provide quotas of SAR and high-spatial resolution optical multispectral satellite data and INGV offers geophysical, geochemical, and volcanological data. The data are accessible via an open access platform implemented in the framework of the EC FP7 MED-SUV project, and is going to be integrated in the EPOS research infrastructure.During the past few decades, Mt. Etna has erupted almost every year offering the optimal conditions to apply the Supersite concept; thus here we report some relevant examples of the integrated use of the space-and ground-based data applied to Mt. Etna&amp;#8217;s eruptions, highlighting how such complementarity improved the monitoring of the eruptive events and the assessment of the associated hazards.
Developing the SwarmPAL Python package

SwarmPAL is a new Python package under development with support from Swarm DISC (Data, Innovation, and Science Cluster). This project aims to provide the research community with a suite of tools to rapidly access, analyse, and visualise data from Swarm (and related data sources). By relying on the VirES system [1] for data access and other utilties, this greatly reduces the complexity required within SwarmPAL. By making use of HAPI [2], we can also connect to many other data sources to retrieve data from beyond Swarm. This is part of an overall strategy for integrating with the wider Python (and Jupyter) ecosystem, while being cognisant of the particular scientific landscape occupied by Swarm [3].SwarmPAL is developed by researchers and research software engineers: this ensures a close fit between the development process and the needs of researchers, as well as fostering software skills within the research community. Through several other DISC activities we bring different research teams together, working on different areas of Swarm science, to collaboratively work on the SwarmPAL system. Namely, these activities currently include the TFA (time frequency analysis), and DSECS (dipolar spherical elementary current systems) toolboxes. These toolboxes provide tools to rapidly and configurably apply analyses to Swarm data, while the SwarmPAL package provides the home for these, together with all the maintenance and documentation that this implies. The development process is made with a strong focus on sustainability and open source [4].[1] https://vires.services[2] https://hapi-server.org[3] https://doi.org/10.3389/fspas.2022.1002697[4] https://github.com/Swarm-DISC/SwarmPAL