Reviewerly/exHarmony huggingface dataset:
A dataset of Earth Observation Data for Lithological Mapping using Machine Learning

Machine Learning (ML) algorithms had successfully contributed in the creation of automated methods of recognizing patterns in high-dimensional data. Remote sensing data&amp;#160; covers&amp;#160; wide&amp;#160; geographical areas and could be used to solve the problem of the demand of various&amp;#160; in-situ data.&amp;#160; Lithologicall mapping using remotely sensed data&amp;#160; is one of the most challenging&amp;#160; applications of ML algorithms. In the framework of the &amp;#8220;AI for Geoapplications&amp;#8221; project , ML and especially Deep Learning (DL) methodologies are investigated&amp;#160; for&amp;#160; the identification and characterization of the lithology based on remote sensing data in various&amp;#160; pilot areas&amp;#160; in Greece.&amp;#160; In order to train and test the various ML algorithms, a dataset consisting of&amp;#160; 30 ROIs selected&amp;#160; mainly&amp;#160; from low -vegetated areas,&amp;#160; that cover 2% of the total&amp;#160; area of Greece was created . For each&amp;#160; ROI&amp;#160; the corresponding shape file&amp;#160; with the lithological units the corresponding&amp;#160; Sentinel2 (10 bands)&amp;#160; and/or Aster (14 bands) images are provided The dataset is&amp;#160; being publicly&amp;#160; available in the cloud along with the necessary code for visualization and processing.
Interactive understanding of groundwater hydrology and hydrogeology – the iNUX project

Groundwater represents more than 97% of the globally available freshwater resources. Groundwater is situated in geological structures in the subsurface and is therefore not visible, difficult to characterize, and manage. As a consequence, it is often not adequately considered by authorities, the general public &amp;#8211; and in education. However, teaching and learning Hydrogeology and Groundwater Management at universities, but also as a part of continuing education training for professionals, is essential to deal with future challenges. For this reason, it is important to use suitable teaching material to improve the understanding of the complex topic of groundwater among these target groups. The recent challenge of the COVID-19 pandemic has increased the demand for digital and remote teaching. An ongoing Erasmus+ cooperation project named iNUX &amp;#8211; interactive understanding of groundwater hydrogeology aims to address the need for digital teaching material. The project aims to achieve an interactive and digital learning environment in hydrogeology and groundwater management with a European but also global target of teachers and students. Existing experience in teaching relevant groundwater subjects from highly reputable European universities will be used to develop interactive and digital teaching material focusing on basic and applied hydrogeology. The teaching material will cover basic theory in combination with field and laboratory applications in different European environments (Northern Europe, Central Europe, and the Mediterranean). The teaching material will comprise (1) various types of videos (e.g., field experiments, lab experiments, screencasts of calculations and software use), (2) interactive Jupyter notebooks that combine explanation with live code (e.g., based on Python), (3) various types of questions and problems that allow different assessments to enhance self-controlled learning of students. All materials are intended as open source and publicly available. The iNUX activities also comprise initiatives to establish interest groups to combine efforts towards larger pools of commonly developed digital teaching material (e.g., question pools) and to link with other activities like the 'Groundwater project' (https://gw-project.org/).
Global thermal spring distribution and relationship to endogenous and exogenous factors

In the last decades, the enormous potential for direct geothermal heat from aquifers attracted special attention, particularly toward those thermal springs indicating areas in which exploitation of geothermal energy might be economically feasible for indirect uses such as electrical power production. The availability of geochemical data besides the location of thermal spring areas assumes particular importance, especially in the first stages of a geothermal exploration program. In this work, we present a digitised format of the literature review of Gerald Ashley Waring on the geothermal springs of the world. This unprecedented dataset contains geographical coordinates (from georeferentiation) of ~6,000 geothermal spring areas, including complementary data such as temperatures, flow rates, total dissolved solids content (TDS, expressed in ppm), and quantitative chemical analysis of major elements (only for a few hundred sites). Using temperature and flow rate, we derive the heat discharged from 1483 thermal spring areas (between ~10-5 and ~103 MW, with a median value of ~0.5 MW and ~8300 MW in total). We complement this information with geological data sets currently available in the literature and analyse them using statistical and geospatial tools and a supervised machine-learning algorithm. We show that terrestrial heat flow, topography, volcanism, and extensional tectonic play a key role in the occurrence of thermal waters around the globe. These results can also be beneficial to address the geothermal interest towards specific and less studied areas and significantly drive the first steps of the geothermal surveys and detailed investigations. Finally, this data set in electronic format will be beneficial for future research on the spatial distribution of thermalism at a small scale and the variation of temperature and flow rate of several thermal springs in the last decades in certain regions.
User-friendly data pipeline for FAIRification of environmental sensordata

In recent years, the requirements for data from earth system sciences have increased massively. Data from observation systems needs to be transferred into larger research data infrastructures, evaluated and flagged via well-defined quality checks, enriched with standardized metadata and finally made available to the public via standard interfaces. And in order to fulfill the FAIR principles, we have to ensure transparency and reproducibility of all these steps. Moreover, the rising demand of near-real-time (NRT) data requires the whole data pipeline to run operationally with minimal manual effort.However, in many cases, there are still heterogenous data landscapes to be found without centralized control of data, data processing, version control and QA/QC. This is often aggravated by to inconvenient, outdated and isolated tools and software solutions.Therefore, we develop and implement an adaptable automated pipeline, which combines the assurance of data consistency, QA/QC (Quality Assurance / Quality Control), graphically supported validation and unified persistence and publication of data. User friendliness is achieved by making the system configurable and trackable through lightweight user interfaces over the complete data lifecycle. By only using open-source software solutions and applying community standards for data formats and interfaces, a high level of sustainability and independence can be ensured.In this presentation, we hence want to demonstrate such an end-to-end data pipeline that finally allows for the FAIRification of typical environmental sensor data.
Deep learning approach for detecting low frequency events on DAS data at Vulcano Island, Italy

Since September 2021, signs of unrest at Vulcano Island have been noticed after four years of quiescence, along with CO2 degassing and the occurrence of long-period and very long-period events. With the intention of improving the monitoring activities, a submarine fiber optic telecommunications cable linking Vulcano Island to Sicily was interrogated from 15 January to 14 February 2022. Of particular interest has been the recording of 1488 events with wide range of waveforms made up of two main frequency bands (from 3 to 5 Hz and from 0.1 to 0.2 Hz).With the aim of the automatic detection of seismic-volcanic events, different approaches were explored, particularly investigating whether the application of machine learning could provide the same performance as conventional techniques. Unlike many traditional algorithms, deep learning manages to guarantee a generalized approach by automatically and hierarchically extracting the relevant features from the raw data. Due to their spatio-temporal density, the data acquired by the DAS can be assimilated to a sequence of images; this property has been exploited by re-designing deep learning techniques for image processing, specifically employing Convolutional Neural Networks.The results demonstrate that deep learning not only achives good performance but that it even outperforms classical algorithms. Despite providing a generalized approach, Convolutional Neural Networks have been shown to be more effective than traditional tecniques in expoiting the high spatial and temporal sampling of the acquired data.&amp;#160;
Conscious and unconscious processing of ensemble statistics oppositely modulate perceptual decision-making.

Our visual system possesses a remarkable ability to extract summary statistical information from groups of similar objects, known as ensemble perception. It remains elusive whether the processing of ensemble statistics exerts influences on our perceptual decision-making and what roles consciousness and attention play in this process. In a series of experiments, we demonstrated that the processing of ensemble statistics can exert significant modulation effects on our perceptual decision-making, which is independent of consciousness but relies on attentional resources. More intriguingly, the conscious and unconscious ensemble representations respectively induce repulsive and attractive modulation effects, with the unconscious effect susceptible to the temporal separation and the distinction between the inducers and the targets. These results not only suggest that the conscious and unconscious ensemble representations engage different visual processing mechanisms but also highlight the distinct roles of consciousness and attention in ensemble perception. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
X‐ray spectral characteristics of typical clays in China and their application in clay classification

Abstract The variety and content of minerals in clay determine its quality and use. The x‐ray fluorescence characteristics of seven elements, that is, Mg, Al, Si, K, Ca, Ti, and Fe, in clay samples were studied using the energy‐dispersive x‐ray fluorescence (EDXRF) method. The application of wavelet transform was used to analyze the overlapping peaks of the FeK α and MnK β and the CaK α and KK β spectral lines and to measure the spectral peak count rate and the content of seven elements. The weight of classified elements in clay was calculated through principal component analysis, and the clay samples from four clay‐producing areas were classified using the K‐means clustering method. The results showed that the accuracy of classification using the count rate of seven elements reached 97.73%. The average accuracy of the classification results of the content of seven elements reached 80.68%. A comparison of the results showed that the EDXRF spectral peak information provides a reliable scientific method for the identification and tracing of clay and its products. In addition, tracing the origin of clay and its products can also indirectly assess the radiation level and its impact on the radiation environment based on the radiation index test results.
Prospects and Challenges of Equipping Mathematics Tutoring Systems with Personalized Learning Strategies

Equipping mathematics tutoring systems with per-sonalized learning strategies is a crucial task in providing personalized learning service. The advance of intelligent educational technology sheds a touchable prospect for practicing personalized learning model. The cloud-based education systems have already provided the platform that can support the scale personalized service. The solving algorithms in mathematics is going to support the personalized learning for mathematics. The educational robots have potential to provide the personalized interactions with learners. However, we still face the challenges in building personalized learning strategies for mathematics. The challenges lie in that we still have difficulty in acquiring the trust learner profile, building strategies of learning mathematics, and finding the relations between profiles and strategies.
Evaluating the Performance of WRF-Solar Model for 72-Hour Ahead Global Horizontal Irradiance Forecasting in West Africa: A Case Study of Ghana

Accurate forecasting of solar irradiance is crucial for the integration of solar energy into thepower grid, power system planning, and the operation of solar power plants. The WeatherResearch and Forecasting (WRF) model, with its solar radiation (WRF-Solar) extension, hasbeen used to forecast solar irradiance in various regions worldwide. However, the applicationof the WRF-Solar model for global horizontal irradiance (GHI) forecasting in West Africa,specifically in Ghana, has not been studied. This study aims to evaluate the performance ofthe WRF-Solar model for GHI forecasting in Ghana, focusing on 3 health centers (Kologo,Kumasi and Akwatia) for the year 2021. We applied a two one-way nested domain (D1=15km and D2=3 km) to investigate the ability of the WRF solar model to forecast GHI up to 72hours in advance under different atmospheric conditions. The initial and lateral boundaryconditions were taken from the ECMWF operational forecasts. In addition, the optical aerosoldepth (AOD) data at 550 nm from the Copernicus Atmosphere Monitoring Service (CAMS)were considered. The study uses statistical metrics such as mean bias error (MBE), root meansquare error (RMSE), to evaluate the performance of the WRF-Solar model with theobservational data obtained from automatic weather stations in the three health centers inGhana. The results of this study will contribute to the understanding of the capabilities andlimitations of the WRF-Solar model for forecasting GHI in West Africa, particularly inGhana, and provide valuable information for stakeholders involved in solar energy generationand grid integration towards optimized management of in the region.Keywords: WRF-Solar; Global horizontal irradiance; Forecasting; West Africa; Ghana
Road User Position and Speed Estimation via Deep Learning from Calibrated Fisheye Videos

In this paper, we present a deep learning processing flow aimed at Advanced Driving Assistance Systems (ADASs) for urban road users. We use a fine analysis of the optical setup of a fisheye camera and present a detailed procedure to obtain Global Navigation Satellite System (GNSS) coordinates along with the speed of the moving objects. The camera to world transform incorporates the lens distortion function. YOLOv4, re-trained with ortho-photographic fisheye images, provides road user detection. All the information extracted from the image by our system represents a small payload and can easily be broadcast to the road users. The results show that our system is able to properly classify and localize the detected objects in real time, even in low-light-illumination conditions. For an effective observation area of 20 m × 50 m, the error of the localization is in the order of one meter. Although an estimation of the velocities of the detected objects is carried out by offline processing with the FlowNet2 algorithm, the accuracy is quite good, with an error below one meter per second for urban speed range (0 to 15 m/s). Moreover, the almost ortho-photographic configuration of the imaging system ensures that the anonymity of all street users is guaranteed.
Benchmark for Evaluation of Danish Clinical Word Embeddings

In natural language processing, benchmarks are used to track progress and identify useful models. Currently, no benchmark for Danish clinical word embeddings exists. This paper describes the development of a Danish benchmark for clinical word embeddings. The clinical benchmark consists of ten datasets: eight intrinsic and two extrinsic. Moreover, we evaluate word embeddings trained on text from the clinical domain, general practitioner domain and general domain on the established benchmark. All the intrinsic tasks of the benchmark are publicly available.
AGILE ROLES ADOPTED FOR LARGE-SCALE AND DISTRIBUTED TEAMS: A SYSTEMATIC LITERATURE REVIEW

Companies have concentrated on the challenge of developing agile methods and establishing large-scale and global software development (GSD) teams. Research is scarce on which agile roles have been adapted. In this paper, we sought to identify which roles for large-scale and GSD teams had been adapted to agile methods and how the adaptations could be represented to assist researchers and practitioners in analyzing the adapted agile roles. We conducted a systematic review of the initial 1,520 studies, and 81 primary studies were chosen to be analyzed and evaluated for quality. The findings present a synthesis of 17 distinct relevant agile roles that organizations can adopt, such as ``System Managers'', ``cultural ambassadors'', and ``proxy product owners''.
The Ideological Aspect of the New Poetics of Cinema: The Interpretation of Action as an Expression of the Spirit of Modernity in the Philosophy of G. Deleuze

The study characterises the central thesis of G. Deleuze’s book about cinema, i.e. a thesis about the crisis of the image-action and the emergence of the image-time, which is proved with the help of philosophy. It is determined that according to G. Deleuze’s point of view, the protagonist of Western cinema is primarily an acting person. In Russian culture, the protagonist is both an acting person and a contemplative, reflecting person. It is concluded that the ideas of modernity have become accepted in Russia. Modernity in Russia appeared in the image of Marxism and also awakened the archetypal ideas about a just social order. At the same time, modernity in Russia should be understood more broadly than just the artistic avant-garde, which was no less active here than in the West. The paper also attaches great importance to considering the question of the understanding of action in the culture of different peoples.
Gradient Aligned Domain Generalization with a Mutual Teaching Teacher-Student Network for Intelligent Fault Diagnosis

Domain generalization for intelligent fault diagnosis handles multiple source domains and generalizes learned knowledge to unseen target domains. Recent efforts actively advocate for learning domain invariances that retain only domain-invariant features and reduce domain-specific ones. However, most of their invariance criteria might not be effective when compared with simple domain averages. Besides, pursuing a similar training procedure has shown the potential to promote domain invariances, but it involves gradient alignments that are quite hard to manipulate and intractable with restricted adjustments. To address these issues, a Gradient Aligned Domain Generalization method (GADG) with a mutual teaching teacher-student network is proposed. Specifically, domain-invariant features are first thoroughly identified using gradients derived from multiple task-specific classifiers, which also intend to offer structure benefits by taking disentangled features for respective tasks. Then, to capture highly expressive domain invariances in the gradient space, a granular gradient alignment is initiated to emphasize high-order domain consistencies, which collaborate with low-order invariant distributional statistics to guarantee safe gradient alignments. Finally, to satisfy consistent gradient alignments, a mutual teaching teacher-student network is proposed, in which modules are assigned with respective tasks and recursively trained. Comprehensive experiments on various generalization tasks validate the efficacy of GADG.
InterREC: An Interpretable Method for Referring Expression Comprehension

Referring Expression Comprehension (REC) aims to locate the target object in the image according to a referring expression. This is a challenging task owing to the need for understanding both natural language and visual information and interpretable reasoning between them. Most existing implicit reasoning-based REC methods lack interpretability, while explicit reasoning-based REC methods have lower accuracy. To achieve competitive accuracy while providing adequate interpretability, in this work, we propose a novel explicit reasoning-based method named InterREC. First, in order to address the challenge of multi-modal understanding, we design two neural network modules based on text-image representation learning: a Text-Region Matching Module to align objects in the image and noun phrases in the expression, and a Text-Relation Matching Module to align relations between objects in the image and relational phrases in the expression. Additionally, we design a Reasoning Order Tree for handling complex expressions, which can reduce complex expressions to multiple object-relation-object triplets and therefore identify the inference order and reduce the difficulty of reasoning. At the same time, to achieve an interpretable reasoning step, we design a Bayesian Network-based explicit reasoning method. Based on the comparative evaluation on various datasets, our method achieves higher accuracy than existing explicit reasoning-based REC methods, and the visualization results demonstrate the method's high interpretability.
A Novel Virtual Reality (VR) based Intelligent Guiding System

A novel VR intelligent guiding system with image processing and programming methods is designed in this study. The fundamental goal of VR technology is to achieve realistic experiences and also natural technology-based human-computer interaction. human-computer interaction based on the natural technologies, so a system that achieves or partially achieves such a goal can be referred to as virtual reality systems. In the designed system, the novel contains the image processing and programming methods. (1) The novel image virtualization method is designed to provide the novel idea of the image pre-processing. (2) The suitable programming framework is designed to make the system efficient. Through the experiment on the UI and the systematic implementation, the performance is tested.
Ultra-Short-Term Photovoltaic Power Prediction Based on Improved Kmeans Algorithm and VMD-SVR-LSTM Model

As the proportion of photovoltaic connected to the power grid increases year by year, the safe and stable operation of the power grid is facing new challenges. Thus accurate prediction of photovoltaic power generation is of great significance to the economic dispatch of the power grid and photovoltaic consumption. In this paper, historical data are clustered based on Kmeans++ algorithm and then decomposed by variational mode decomposition algorithm, which reduces the volatility of data and fully excavates the characteristics of data used for prediction. After the combined prediction model based on support vector regression and long short-term memory network is established, the model with better prediction effect is selected for the prediction of each component obtained by decomposition, which improves the prediction accuracy of single modal component, and then improves the final prediction accuracy after reconstruction. Simulation results show that the proposed hybrid model can give full play to the advantages of each algorithm, and prediction effect is better than traditional single prediction model.