Reviewerly/exHarmony huggingface dataset:
CalxPy: a software for the calibration of geophysical systems against a reference&amp;#160;

&amp;lt;p&amp;gt;The International Monitoring Systems (IMS) operational manuals for waveform stations require that IMS stations be calibrated regularly. Since 2012, the Provisional Technical Secretariat (PTS) had relied mostly on electrical calibration to meet that requirement. However electrical calibration has inherent challenges (no traceability, integration and sustainment issues, high operating costs&amp;amp;#8230;).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;A part of the geophysical community, including Station Operators, has started performing regular calibrations by comparison against a co-located reference. This method allows a more systematic and centralized approach to calibration. Over the past few years, it has been increasingly used at IMS stations, particularly infrasound ones. In this context, the PTS is developing tools to support this alternative approach.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We present CalxPy, a web-application developed at the PTS for the calibration of geophysical systems by comparison. With CalxPy, one can calculate, store, and display the response of a system for a given period, or track the evolution of the response against time or environmental variables. CalxPy also allows the refinement and evaluation of the measured response against a baseline, and the reporting of calibration results.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;CalxPy supports the Initial calibration and on-site yearly calibration processes, as well as data quality control. CalxPy can be deployed in the IDC pipeline and in NDC-in-a-box.&amp;lt;/p&amp;gt;
Modernization of Natural Science Education in the Context of Teacher Training

The article states about the personal readiness of future psychologists to use the latest interactive and information technologies in order to improve natural science training in the framework of the integrative acquisition of professional competencies. For this purpose, the expediency of post-non-classical integration of the humanitarian and natural components in the field of psychology was investigated, the relevance of Coworking, BarCamp, and Workshop technologies was reasoned. The readiness of future psychologists to use these technologies after passing advanced training of scientific and scientific pedagogical personnel abroad has been studied using sociological methods. The purpose of the article is to clarify the presence of personal motivation for innovative, integrative, interactive and informational improvement of professional natural science competencies aiming to improve the transformational processes of higher education in Ukraine. A theoretical analysis of the modern natural-scientific training of future psychologists with the identification of promising aspects and a sociological analysis (survey) were chosen as research methods. The analysis showed the need for personal awareness and interactive and information-oriented education. The result of sociological research is the manifestation of divergence between the understanding of the need to use information and communication technologies and mainly personal obstacles to their use. The international significance of the article lies in the fact that its conclusions can be used in modeling innovative conditions for the advanced training of teachers of natural sciences in the countries of “late democracies”.
Geochemical investigations of 100 superficial soils observed by Sentinel 2 and PRISMA

&amp;lt;p&amp;gt;Geochemical investigations of agricultural soils are fundamental to characterize pedosphere dynamics that sustain ecosystem services linked with agriculture. Parameters like soil moisture, soil organic matter (SOM), and soil organic carbon (SOC) are strong instruments to evaluate carbon sink potential.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Satellite Earth Observation is a significant source of free data that can be linked to soil characteristics and dynamics and employed to produce temporal series. Access to these data is nowadays facilitated by platforms such as ADAM (https://adamplatform.eu), which allow users to quickly search for, visualize and subset data products, greatly reducing the volume of data that end users must handle.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this work we demonstrate the usefulness of such systems by carrying out a geochemical investigation of 100 superficial (0-15 cm) soil samples collected in the province of Ferrara (North-Eastern Italy) and using the ADAM platform to associate to each a time series of Sentinel 2 data.&amp;amp;#160;The samples were collected in October 2021 in fields that were ploughed or mono-cultivated at maize, soybean, rice, and winter vegetables. To obtain the average soil properties over a spatial scale larger than the satellite sensor resolution, we adopted a composite sampling strategy, merging 5 sub-samples collected at the vertexes and at the center of a 30x30 m&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; area.&amp;amp;#160;Soil granulometry was recognized from clay to medium sand, with exception of peat deposits.&amp;amp;#160;Soil moisture, and SOM, contents were estimated by loss on ignition (LOI), respectively at 105&amp;amp;#176;C (values from 0.3 to 7.4 wt%), and 550&amp;amp;#176;C (values from 2.1 to 21.0 wt%). SOC contents (values from 0.7 to 9.3 wt%) were determined through DIN19539 analysis performed with an Elementar soliTOC Cube.&amp;amp;#160;Using the ADAM platform, we associated a temporal series from 2016 to 2021 of the Sentinel 2 NDVI data product to each sampling location, using a cloud coverage mask to eliminate values taken on cloudy days. Localized phenological cycles for each year are recognizable in the remotely-sensed data. Hence, our database describes for each parcel, geochemical parameters and vegetative temporal series.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In a separate study, we also attempted to train a neural network to predict geochemical properties from the soil spectrum measured by the hyperspectral satellite PRISMA. We used the geochemical properties of our 100 samples as training data, associated with the PRISMA spectra of the sampling locations measured on April 7 2020, when, according to our NDVI data, none was covered in vegetation.&amp;lt;/p&amp;gt;
Self-Supervised Graph Representation Learning via Information Bottleneck

Graph representation learning has become a mainstream method for processing network structured data, and most graph representation learning methods rely heavily on labeling information for downstream tasks. Since labeled information is rare in the real world, adopting self-supervised learning to solve the graph neural network problem is a significant challenge. Currently, existing graph neural network approaches attempt to maximize mutual information for self-supervised learning, which leads to a large amount of redundant information in the graph representation and thus affects the performance of downstream tasks. Therefore, the self-supervised graph information bottleneck (SGIB) proposed in this paper uses the symmetry and asymmetry of graphs to establish comparative learning and introduces the information bottleneck theory as a loss training model. This model extracts the common features of both views and the independent features of each view by maximizing the mutual information estimation between the local high-level representation of one view and the global summary vector of the other view. It also removes redundant information not relevant to the target task by minimizing the mutual information between the local high-level representations of the two views. Based on the extensive experimental results of three public datasets and two large-scale datasets, it has been shown that the SGIB model can learn higher quality node representations and that several classical network analysis experiments such as node classification and node clustering can be improved compared to existing models in an unsupervised environment. In addition, an in-depth network experiment is designed for in-depth analysis, and the results show that the SGIB model can also alleviate the over-smoothing problem to a certain extent. Therefore, we can infer from different network analysis experiments that it would be an effective improvement of the performance of downstream tasks through introducing information bottleneck theory to remove redundant information.
Videogame as Means of Communication and Education: Philosophical Analysis

The study is devoted to the philosophical consideration of specific features of communication and education through the use of video games. The purpose of the research was to consider the specific features of communication in the process of interaction within video games, to reveal their educational potential and the difference in their use for educational purposes. The analysis of videogame definitions has allowed focusing on their specific features, namely: interactive, rule-based nature and the need of the specific hardware. As a result, the possible types of dialogue within video games have been considered and, on their basis, the main types of interaction have been formulated for analyzing their use in education: interaction with no active player, player-videogame interaction in case of one-player videogames, player-videogame-player interaction and player-community-videogame interaction. In conclusion, the similarity of videogame playing and the learning process has been delineated in relation to the analyzed types of interaction. The authors state the further need for a comprehensive study of the specific features of each of described types due to the significant differences in the dialogue and educational potential of videogames belonging to them.
Adversarially robust water quality assessment associated with power plants

Power production is responsible for the introduction of eutrophication into surface water bodies via multiple routes. Therefore, water quality assessment is quite necessary for the resource protection of surface water bodies near power plants. At present there is an urgent need to develop automated methods for fast and accurate water quality assessment. In this paper three CNN-based classifiers are constructed aiming at the classification of surface water images. With respect to the threat of adversarial attacks, three typical attacking methods and a JPEG compression-based countermeasure are chosen for observing the impact of adversarial attacks. The evaluation of edge inference is made on two edge inference engines (platform-independent and platform-dependent) respectively. The experimental results show that the classifiers own excellent performance all with accuracy above 98%. They are able to be conveniently deployed on edge devices without performance degradation for at most 80 FPS. It is also shown that adversarial attacks will reduce classification accuracy markedly while JPEG compression can mitigate the threat as a simple and practical mechanism.
Collaborative Filtering on Bipartite Graphs using Graph Convolutional Networks

Existing graph neural networks are not suitable to handle bipartite graphs, and existing graph-based collaborative filtering methods cannot model user-item interactions. To over-come these problems, we propose a novel collaborative filtering method named Graph Convolutional Collaborative Filtering (GCCF). Our GCCF aggregates neighbor nodes information with a novel Graph Convolutional Network (GCN) style node aggregation function improved for bipartite graphs. In addition, GCCF models user-item interactions using the user-item crossing layer. We conduct experiments to compare our GCCF with the existing state-of-the-art methods. Experimental results show that GCCF achieved or matched state-of-the-art results over five collaborative filtering datasets without any side information.
Temporal information extraction with the scalable cross-sentence context for electronic health records

Temporal information is essential for accurate understanding of medical information hidden in electronic health record texts. In the absence of temporal information, it is even impossible to distinguish whether the mentioned symptom is a current condition or past medical history. Hence, identifying the relationship between medical events and document creation time (DCT) is a critical component for medical language comprehension, which can link the mentioned medical information to the time dimension by marking temporal tags. Existing natural language processing (NLP) systems are typically based on the sentence where the medical event is located to extract the DCT relationship. Inevitably, the limited textual context can be insufficient as it is difficult to contain adequate document information. Introducing the surrounding sentences into models is a fitting way to enrich the information. However, in addition to document information, the added context can also bring noise to confuse the models. For effective utilization of the context, we design the DCDR (Dynamic Context and Dynamic Representation) model. Our model consists of two modules, i.e. the dynamic context mechanism and dynamic representation mechanism. The dynamic context mechanism is employed to bring the related texts into our model via the sliding windows and a scoring calculation. For the dynamic representation mechanism, a modified dynamic routing algorithm is adopted to filter the noise and generate an integrated representation for the whole context. Besides, the mentioned medical information is led into the routing process to enhance the dynamic representation module. The experiments show that our proposed model achieves improvement over existing models and achieves an F-score of 85.7% on the commonly used THYME corpus.
Sequential Alignment Methods for Ensemble Part-of-Speech Tagging

Part-of-speech tagging is a fundamental task that provides the elemental structure and content information for additional natural language processing. Although Part-of-speech tagging problems have traditionally been formulated as sequential labeling tasks, none of the proposed ensemble approaches have focused on sequence alignment during post-processing. Herein, we present a weighted ensemble technique using a sequence alignment approach for a Part-of-speech tagger. Through this technique, we introduce a simple but powerful post-processor, which is a sub-sequence selector using a similarity score calculated through sequence alignment methods. Such methods are based on an existing DNA alignment approach applied toward natural language. Experiments were conducted using an ensemble of sequence alignment methods with three different sub-sequence units, i.e., the sequence, word, and character span. Experiments on English and Korean datasets show that our sequence alignment ensemble technique outperforms a basic hard voting method. Most of the results of the ensemble sequence alignment approach with various sub-sequence units showed an increase in F1-score over hard voting. F1-score increased up to 0.36 for the general hard voting method on the test dataset.
Comparison of web application performance on the example of Laravel and Vaadin frameworks

The article presents the result of comparing the performance of web applications written with the use of Laravel and Vaadin frameworks. The aim of the study was to identify which framework offers better performance in creating web applications and to verify the thesis presented in the article. A research method based on a comparative analysis was used to conduct the study. The study was carried out using two applications created for the purpose of the study and tools such as ApacheBench and Chrome DevTools were used for the study. Tests were carried out according to 13 research scenarios and the obtained results allowed to clearly verify the thesis. Finally, it was concluded that Vaadin is a much more efficient framework than Laravel.
Creating a Remote Choir Performance Recording Based on an Ambisonic Approach

The aim of this paper is three-fold. First, the basics of binaural and ambisonic techniques are briefly presented. Then, details related to audio-visual recordings of a remote performance of the Academic Choir of the Gdansk University of Technology are shown. Due to the COVID-19 pandemic, artists had a choice, namely, to stay at home and not perform or stay at home and perform. In fact, staying at home brought in the possibility of creating and developing art at home while working online. During the first months of lock-down, the audience was satisfied with music performances that were fairly far from the typical experience of a real concert hall. Then, more advanced technology was brought to facilitate joint rehearsal and performance of better quality, including multichannel sound and spatialization. At the same time, spatial music productions benefited from the disadvantage of remote rehearsal by creating immersive experiences for the audience based on ambisonic and binaural techniques. Finally, subjective tests were prepared and performed to observe performers’ attention behavior divided between the conductor and music notation in the network-like environment. To this end, eye-tracking technology was employed. This aspect is related to the quality of experience (QoE), which in the performance area–and especially in remote mode–is essential.
Analyzing the accuracy of detecting steganograms formed by adaptive steganographic methods when using artificial neural networks

This paper reports a comparative analysis of accuracy in the detection of steganograms formed according to adaptive steganographic methods, using steganography detectors based on common and specialized types of artificial neural networks. The results of the review of modern convolutional neural networks applied for the tasks of digital image stegoanalysis have established that the accuracy of operating the steganography detectors based on these networks is significantly compromised when processing image packets characterized by a significant variability of statistical parameters. The performance accuracy of steganography detectors based on the modern statistical model of container images maxSRMd2 has been investigated, as well as on the latest convolutional and «hybrid» artificial neural networks, in particular, GB-Ras and ASSAF networks, when detecting steganograms formed according to the adaptive steganographic methods HUGO and MiPOD. It was established that the use of the statistical model maxSRMd2 makes it possible to significantly (up to 30 %) improve the accuracy of steganogram detection in the case of analyzing those images that are characterized by a high level of natural noise. It was found that the use of the ASSAF network makes it possible to significantly (up to 35 %) reduce an error of steganogram detection compared to current steganography detectors based on the GB-Ras network and the maxSRMd2 statistical model. It was determined that the high accuracy of the ASSAF network-based steganography detector is maintained even in the most difficult case of image processing with high noise and poor filling of the container image with stegodata (less than 10 %). The results reported here are of theoretical interest for designing high-precision steganography detectors capable of working under conditions of high variability in image parameters.
CROSS-CULTURAL COMMUNICATION DEVELOPMENT OF UNIVERSITY STUDENTS IN ONLINE CLASSROOM

The process of globalization and academic mobility actualizes the formation and development of cross-cultural communication as an important factor in university education. The paper considers the value of cross-cultural communication development in a multicultural university environment. Such qualities as tolerance, empathy, assertiveness, and patriotism are considered to be important in the sphere of cross-cultural communication. Cross-cultural communication development is viewed as a meaningful part of the professional competence of a future Higher School graduate. The purpose of the research is to define the qualities necessary for successful cross-cultural communication between students and to find the main problems that prevent students from the improvement of the process of cross-cultural communication in online classroom. The study was conducted on the basis of the developed elective online course aimed at students’ cross-cultural competence development. The research involved 90 first-year students of different majors at Southern Federal University, Russia. The levels of students’ cross-cultural communication development were characterized and evaluated during the study. The paper takes into account the student’s opinion on the definition of the observed qualities of cross-cultural communication and considers the problems that students are facing in the process of cross-cultural development.
Educational digital inclusion: a conceptual mapping

The objective of this documentary review was to analyze the notion of educational digital inclusión and establish the boundaries of the concept in relation to other terms. The conceptual mapping method was chosen (Tobón, 2004), wich consists of four phases: 1) search for documents relevant to the research problem, 2) definition of inclusion and exclusion criteria, 3) data analysis based on eight axes, and 4) interpretation of the results. A total of 40 documents were reviewed from the databases Dialnet and SciELO, as well as from the Fundación Ceibal repository. The results obtained make it possible to establish that it is often difficult to relate the term with educational inclusion (higher category) given that the latter is oriented towards equalize the conditions. In this sense, the link between both concepts has permeated a vision focused on the implementation of actions and strategies that, for the most part, do not consider the particularities of the institutions to which they are directed. Likewise, its association with the term innovation has weighed on the figure of the teacher, since it is assumed that the insertion of technological artifacts is enough to achieve innovative uses, without considering that the inclusion of these tools must be accompanied by the corresponding literacy processes.
Knowledge Management Approach for Memory Components Based on User-friendly Conversational System

Due to the recent technological development and the growth of computing resources, there are various studies that apply large-scale language models in the field of natural language processing such as conversational systems. Also, there are researches that attempt to maintain a conversation flow and naturally lead a dialogue by treating the contextual information exchanged with users from the perspective of knowledge. In this paper, we propose a method for managing various contextual information such as chat history, situations, and preferred topics based on a knowledge base and generating a conversation customized for the specific user. We design a schema of memory components to deal with the user's contextual information so that implement a Web-based conversational system, which is friendly come to those users. It is expected that these systems using the user-specific memory components will be helpful in such domains like education or customer consultation.