Reviewerly/exHarmony huggingface dataset:
DIGITAL PLATFORM FOR THE PERFORMANCE GROWTH OF EDUCATION IN GEODETIC ENGINEERING FIELD

Current accelerated digitalization process of society and technological developments in all technical areas have determined the academic institutions to adapt their education system to these challenges. Numerous studies have shown evidence of increased interest to learn when digital devices are incorporated into the learning environment. Therefore, in this paper we proposed a functional model of digital platform to be used by the geodetic students. In order to improve the university education process and performance, the digital platform should include certain elements such as: study field presentation, ability to adapt to latest technological developments, attracting of private sector in educational process through different methods and standardization of evaluation process. A digital platform must provide answers to essentials questions or requirements of students. The proposed digital platform is based on two framework schemes which refer to the promotion faculty, admission and educational process content. For admission, the university should take account three candidate categories (decided on certain study field, those who want an engineering specialization and those undecided).Hence, for all three categories the digital platform should provide the certain aspects as following: perspective on labour market of chosen specialization and information regarding the study field (courses name, brief description of topics courses, competences etc.).The educational process content is addressed to the students attended to each study program and must be conceived and presented based on the professional and transversal competences set. The information on the digital platform must be organized in a logical sequence and must allow to users to get all information they needed.
AN EVALUATION OF BLOCKCHAIN-BASED REPUTATION SYSTEMS. CASE STUDY: THE EERIS RESEARCH NETWORK

The paper realizes an evaluation of Blockchain-based reputation systems used in education and research, starting from the definition of reputation in different contexts, then justifying the advantages of using the Blockchain technology for implementing reputation systems. A number of criteria are proposed for analyzing Blockchain-based reputation systems, selected from a review of literature presenting such projects. The results of the evaluation are used to fundament and conceive the design and implementation of a reputation research network, called EERIS - European Research Infrastructures System, which are presented in the second part of the article. Because the events for adding information that affect the reputation happen less often, it is more efficient to query the reputation which is already stored and calculated on the Blockchain whenever it is needed. At the same time, all the information which compose the reputation are stored on the Blockchain, which makes other organizations handle their own reputation equation which can consist of different data which suits its needs. This being said, in order to populate the Blockchain with the data the platform already contains, it is needed to do a migration. Hyperledger Fabric, because of the programming languages which can be used to write chaincode, is more powerful and allows integrating libraries or using the core methods, for making API calls, which in this case is considered to be use to initialize the chaincode upon creation. The conclusions could be used for researchers focusing on different Blockchain educational use-cases, and also for those designing reputation systems for educational actors and entities.
AN EXPLORATORY RESEARCH ON THE IMPACT OF DIGITAL ASSISTANTS IN EDUCATION

Digital assistants are currently expanding, and they are integrated into the daily lives of many users. In the implementation of digital assistants, various technologies are used, such as Artificial Intelligence, Big Data, Machine Learning, Natural Language Processing (NLP), etc. This article is an exploratory research in digital assistants used mainly for educational purposes. The progress of the EdTech sector has brought significant changes in the educational landscape. In this context, digital assistants are one of the technological advancements that could have a high impact on education. It is very likely to see more institutions using virtual assistants in education over the next few years, given the advancements in virtual assistants and their impact in the process during the COVID-19 pandemic. Several schools and universities have already started to use Amazon's Alexa for support issues like answering administrative questions or guiding students around the campus. Although for the moment they have rather basic functionality, it is obvious that virtual assistants can already be used in education and have the potential to boost students' performance, assist teachers' activity and support administrative tasks. This paper will focus on providing a state of the art of current initiatives and discuss the perspectives on how digital assistants can shift the education industry. It will also discuss future development possibilities and use case scenarios. The progress of artificial intelligence and its integration with virtual assistants shows that there are many opportunities ahead for using digital assistants in education, but there is still a long to go to realize their full potential. But for sure, they will be used in the future in more complex contexts.
NEW TRENDS IN MANAGING OUR FOREIGN LANGUAGE (VIRTUAL) CLASSROOM IN PANDEMIC TIMES

Teaching languages in pandemic times takes on both pedagogical and technological challenges. This is the moment where teachers and students explore and adapt to a new environment. This is a place (shall we call it a "cyberspace"?) where tradition has to be reconsidered more closely and facilitate significant innovations. The year 2020 will be remembered as a turnaround point in human modern history for many scientific domains. Teaching languages in schools is facing big challenges alongside with other teaching subjects (arts, humanities, natural sciences). Nonetheless, (foreign) language teaching gives teachers even in a restricted teaching environment (confinement restrictions, enforced lockdowns of varying degrees) a certain flexibility in managing the ,,virtual classroom". Students can acquire in times of restrictions languages, as long as they have an electronic device that enables them to get in touch with a teacher. This also implies the fact that teachers are able to facilitate and control communication within the classroom. Moreover, beyond their (traditional) teaching skills they should also be able to develop a basic competence in the software system. Although online teaching has gained more and more attention in the last decades, it was first in 2020 - as pandemic restrictions had been imposed - that teachers were directly confronted with the need of teaching in front of a personal computer for their students. The object of this paper is therefore to examine online language teaching and set a basic framework of the so called virtual classroom. This examination relies on the fact that there is indeed a gap between traditional face-to-face classroom teaching and online teaching (Compton, 2009). Whereas the teacher is supposed to acquire additional teaching skills in order to face online challenges. Furthermore, the virtual classroom must be restructured, in the sense that traditional classroom ceases to exist once the teacher jumps into the cyberspace. Another interesting issue is cyberspace itself. This should be a space where both teacher and student have profiles and behaviours that stand in clear contrast to their traditional counterparts. A significant feature is the fact that online teaching must reshape traditional teacher-centred strategies and develop a group-centred method, whereas the general frontal view teaching should shift into a more personalised approach. Teachers must be fully aware of the importance of these acts by overcoming their fears and engaging in a constructive dialogue with their students.
IMPROVING PATIENT EDUCATION AND THE TRANSITION PROCESS USING VIRTUAL REALITY

For those diagnosed with a chronic condition at an early age, such as Cystic Fibrosis, transition from a paediatric care facility to an adult orientated facility is often inevitable. Patients can feel anxious and nervous as they are unfamiliar with their new surroundings and healthcare team. Research has shown that these symptoms would be reduced if patients were given the opportunity to meet their new healthcare team and become familiar with the new process and surroundings prior to transitioning. This research explores the possibility of using Virtual Reality as a means of complementing the transition process. By allowing patients to explore and learn of their new surroundings and meet their healthcare team virtually prior to transitioning, it is anticipated that their will be an improvement on patient education and experience. A 3D model of the Adult CF Day Unit, Specialist Inpatient Unit, and other common testing labs is created for exploration in the Unreal Engine 4. This 3D environment is then prepared for use in a HTC Vive. The environment also includes interest points, audio, and welcome video introducing the adult CF healthcare team. Throughout the development process this system is tested with five technical researchers. The feedback received from these evaluators primarily focused on the use of a UI and other features such as a bread-crumb trail and audio to assist in the educational content at interest points. This feedback has now been implemented in preparation for patient testing. It is anticipated that this will enhance the patients experience and educate them on the transition process.
Anorthosite and associated gabbronorite plutons of Barabar hills in Chotanagpur granite gneiss complex (CGGC), eastern India: Estimation of parental melt for gabbronorite using equilibrium distribution method (EDM)

The small plutons of anorthosite and associated gabbronorite exposed near Barabar hills form a component of Chotanagpur granite gneiss complex (CGGC) in eastern India. Plagioclase (&gt;90 vol %) make up the majority of anorthosite rock with minor mafic minerals (amphibole, mica), while orthopyroxene (&gt;40 vol %), plagioclase (40-50 vol %) and clinopyroxene (&gt;20 vol %) make up the associated gabbronorite. These are cumulate rocks with anorthosite and gabbronorite showing adcumulate and mesocumulate textures, respectively. Compositionally, plagioclase ranges from anorthite to labradorite (An60-96) in anorthosite and from oligoclase to bytownite (An50-70) in gabbronorite. In gabbronorite, the clinopyroxene composition ranges from diopside to augite (En36-43 Fs12-15 Wo43-47), and the orthopyroxenes are hypersthene (Wo39-40 En46-50 Fe10&amp;#8211;21).Anorthosite show enrichment of LILE (Rb, Ba, Sr, Th, Pb) with respect to the HFSE (Zr, Ti, Nb and display enrichment in LREE ((La/Yb) N = 2.78-15.29) with positive Eu anomaly (Eu/Eu* = 1.29-3.45) and variable MREE. A flat to depleted trend for HREE ((Sm/Yb) N = 1.02-2.95) is observed for anorthosites. Associated gabbronorites show enrichment of LREE ((La/Yb) N=1.99-4.93), depleted HREE ((Sm/Yb) N = 0.88-3.24) with negative to positive Eu anomaly (Eu/Eu* = 0.78-2.95). Also, the gabbronorite shows enrichment of LILE (Rb, Ba, Sr, Th, Pb) compared to HFSE (Zr, Ti, Nb). Clinopyroxenes of gabbronorite have low REE abundances (53.29-60.29 ppm). Clinopyroxenes are depleted in light rare earth elements (LREEs) (La/Yb) N = 0.75&amp;#8211;0.80 and depleted in LILEs such as Ba, Sr. and also exhibit negative anomalies in Zr and Ti.REE composition of gabbronorite clinopyroxene is constrained between TMF = 15-30% calculated using the equilibrium distribution method (EDM). This is substantiated by whole rock parental melt REE composition calculated using the concentration ratio approach (Nernst equation), the result of which is consistent with those made using EDM. In chondrite normalized plot, the estimated parental melt display (1) near-horizontal trend from Lu to Gd at rock/chondrite = ~100, (2) negative anomaly at Eu, (3) gradual rise from Sm to Ce and (4) slight dip from Ce to La.
Time-dependent Seismic Hazard Parameters Evaluation with SHAPE MATLAB Package

Seismic processes can be often time dependent at different time scales. Earthquake interactions (e.g., static and dynamic stress changes), anthropogenic activities (e.g., mining, fluid injection, hydrocarbon exploitation) fluid dynamics (e.g., in geothermal fields and volcanic areas) and periodic phenomena (e.g., earth and ocean tides) result to changes in frequency and magnitude distribution of earthquakes. These changes apply in a wide range of time scales from seconds to decades and their evaluation is vital, for seismic hazard assessment in the vicinity of urban and industrial areas. In addition, such estimates can be used in industrial sites to facilitate production optimization, and they also may offer better insights for the underlying physical mechanisms of seismogenesis (e.g., stress transfer, fluid migration pathways and pore pressure, chemical alteration and frictional properties in depth).SHAPE (Seismic HAzard Parameters Evaluation), is an open source toolbox, based on MATLAB, developed within the SERA (Seismology and Earthquake Engineering Research Infrastructure Alliance for Europe) Project, and is available for use by seismologists and other scientists and engineers in related fields. SHAPE probabilistically estimates the time-dependent, source components of seismic hazard, namely the magnitude distribution and the seismic activity rates, expressed jointly as changes in the exceedance probability of a given magnitude within a predefined period. Alternatively, the changes of the mean return period of a given magnitude is evaluated in moving time windows. Four different magnitude distribution models are included (unbounded and truncated Gutenberg-Richter law and non-parametric kernel). Interactive parameter selection and data filtering routines are also incorporated in the package.The presentation will cover the capabilities of SHAPE and a demonstration of selected examples from published and ongoing case studies:Mining induced seismicity at Rudna Mine, Poland. Seismicity triggered by water reservoir impoundment in Song Trahn 2 artificial lake, Vietnam. Tidal triggering of microseismicity recorded by an ocean bottom seismometer network in the equatorial mid-Atlantic ridge. The SHAPE package is developed in two standalone versions (an interactive Graphical User Interface version and a function) as well an online version, integrated in the Thematic Core Service Anthropogenic Hazards (TCS-AH) of the European Plate Observing System (EPOS). The standalone versions can be downloaded for free from a public repository.
Seismic and infrasound monitoring of military conflicts using machine learning

The real-time seismo-acoustic monitoring of military conflicts can provide a unique alternative to conventional ground reports and sparse satellite coverage. The pressure waves generated by an explosion travel through the atmosphere and subsurface as sound and seismic waves, and their signature can be recorded by arrays of seismometers for ground motion or microbarometers for sound propagation. However, standard monitoring techniques can be both computationally expensive when localizing signals over large regions and/or prone to false detections when signals have low amplitudes. In this contribution we propose a Machine-Learning (ML) based solution to detect seismic and infrasound arrivals and locate sources close to real time. To validate our model we leverage the seismic data collected during the Russia-Ukraine conflict started in February 2022 using the Ukrainian primary station of the International Monitoring System (IMS), the Malin array (AKSAG). We test both the accuracy and computational efficiency of our approach against a threshold-based migration stacking model developed for near-real time monitoring in Ukraine. We hope that this first-ever ML detector of both seismic and acoustic phases could be employed for real-time monitoring of conflicts around the world across different network geometries and noise conditions.
PrESENCE : a participative citizen seismic network.

The SeismoCitizen (SismoCitoyen) project presents and tests a new paradigm of collaborative monitoring of geohazards in urban and peri-urban environments. Seismological observations are obtained using a large number of low cost internet-connected equipment (Raspberry Shake seismic sensors and associated open access data). The breakthrough strategy of the project relies on the deployment of the sensors in residences or administrative buildings of non-seismologist voluntary citizens or authorities. The aim is to use those stations to densify the french permanent seismic network, and to improve the detection and location of seismic events, in particularly small ones. The volunteers take part in a sociological survey to estimate the impact of that participative project on their perception of science. Candidates are primarily chosen according to the seismic interest of their location and for some of them to represent the social variability of the population.&amp;#160;&amp;#160;Since the &amp;#8220;Sismocitoyen&amp;#8221; project was launched in 2018 by BCSF-R&amp;#233;nass and EOST (CNRS and Strasbourg University), sixty sensors have been deployed and are currently hosted by voluntary citizens in the region of the Upper Rhine Graben, in the area of Strasbourg, Mulhouse and alongside Vosges mountains. They were able to strongly improve our monitoring of the seismic events induced by a deep geothermal project close to Strasbourg where several events have been largely felt (2019-2022). The topic is becoming a major issue in the development of renewable energies that involve the subsurface as seismic hazards are of significant public concern and can have major socio-economic impacts.&amp;#160;&amp;#160;With the new PrESENCE ANR project (2022-2025) we focus on seismic hazards induced by deep geothermal operations in northern Alsace and their associated societal perception.&amp;#160; Seventy Raspberry Shake seismic stations are being deployed since the end of 2022 and installations will continue in 2023. We will use our previous experience to improve, refine and develop all aspects such as site selection, protection of privacy and confidentiality of volunteers data and information, station calibration before deployment, data transmission and protocol to minimize data losses, stations monitoring and data analysis.&amp;#160;During the project, interactions with the station hosts will be reinforced, in particular with convivial meetings (Stammtisch) to answer questions, present the use of the data and the results obtained.
Best Machine Learning Model For Face Recognition in Home Security Application

Particularly since the COVID-19 outbreak, Indonesia has seen an annual surge in criminal prosecutions. To increase home security, many technological advances have been made. Face recognition served as the main form of security for almost all of them. Face detection, face segmentation, and face recognition are the three steps in the face recognition process. To avoid misclassification and increase system dependability, accurate recognition of faces becomes crucial in security systems. The optimization tool Grid Search CV produces using a number of machine learning methods that are proposed. Each machine learning has been created using its best model and has attained accuracy levels of at least 90%. The most effective strategy is SVM, which has 100% accuracy rates. A technique for choosing the best model is an alternative. The computation time will be compared to that of more complex systems before these results are eventually communicated to the real system
Seismic imaging of shallow magma bodies at Krafla, Iceland.

Imaging the subsurface geology at volcanoes is crucial for understanding their structure and dynamics. Knowledge about the existence of magma chambers, fault and fluid systems improves natural hazard assessment and geothermal energy exploitation. However, the highly heterogeneous subsurface at volcanoes complicates the identification of geologic layers and objects. A strongly scattered seismic wavefield is typically recorded that masks coherent energy reflected at interfaces of interest. In addition, small geologic features such as magma bodies are often smeared out by tomographic techniques. A well-known example that highlights this problem is Krafla, a volcano caldera in the north-east of Iceland. In 2009, a magma body was unexpectedly found at a depth of 2.1km during drilling for geothermal purposes. Even though Krafla is one of the best-studied volcanoes worldwide, the shallow magma body remained undetected. In the summer of 2022, we conducted a six weeks long field experiment at Krafla as part of the IMPROVE project. We deployed densely spaced short seismic profiles comprising 114 seismometers in a passive experiment. Our goal is to image the magma body at 2.1 km depth. At first, we carry out a comprehensive data characterization in order to better understand the seismic wavefield and the influence of source, propagation, and near-station effects. In a subsequent step we apply targeted imaging methods including local earthquakes and high-frequency industrial noise. Krafla provides an optimal setting to test and calibrate seismic imaging, because the location of the magma body is known (through drilling). By combining different methods, we seek to improve seismic imaging techniques in order to obtain a high-resolution image of the subsurface in complex media.
An Approach for the Reduction of Unwanted Edges in Contour Detection Based on Local Filtering

In this paper, an approach for the reduction of unwanted edges in contour detection based on local filtering is presented. Our approach can be used as a preprocessing step before contour detection. Also our approach is useful for object recognition based on feature extraction tasks, because many contour detection methods can’t delete all unwanted edges carefully. Our method consists of a computational algorithm that has 7 steps. Including smoothing, edge detection, smoothing, decreasing of pixels, thresholding, local filtering, and mask creation respectively. We use smoothing for adhering neighbor edge pixels and weakening alone edge pixels. So we can amplify the correct edge pixels and attenuate unwanted edge pixels by smoothing the edge image. In local filtering, we use a proposed casual template that determines noisy regions and correct regions and therefore can create a mask matrix that its elements related to mentioned regions. Finally we can use the "mask matrix" for improving contours by using a “And” operator and we ensure final contour that has a few context effect.
SAMMY Modernization Efforts [Slides]

This presentation discusses SAMMY releases and states that all of its dependencies are freely available. It also discusses version control and confirms that versions will be tagged as needed. Additionally, SAMMY program flow is discussed and states that the program flow was improved to allow for easier external access to SAMMY routines. Modernization of SAMMY is continued with the Doppler and Resolution broadening modules. The presentation concludes by stating that documentation updates are in progress.