Reviewerly/exHarmony huggingface dataset:
Remote learning and examination based on augmented reality

Remote Learning and Examination based on Augmented Reality (RELAR) is a European Erasmus+ project (2020-1-NL01-KA226-VET-083043) that aims to create a crisis-proof resilient education environment, enabling remote coaching and digital skills training based on AR. RELAR integrates seven European partners –Vocational Training Institutions and Higher Education Institutions– all linked to the maritime industry. The industry itself is also represented. With the help of a reference group, a set of learning outcomes has been defined to develop three demo scenarios to test and demonstrate the RELAR system, which is based on the RealWear HMT-1 assisted reality hands-free computer. All scenarios are scaffolded on the same framework that integrates active learning pedagogy, curriculum requirements and technological integration. This digital active learning process pedagogy incorporates two processes for instruction: a remote instruction process called ‘Expert Coaching’ that gives the students the possibility of receiving instant feedback while taking actions and decisions; and a remote assessment process named ‘Digital Workflow’ that incorporates formative assessment to consolidate learning. The curricular aspect focuses on the professional competencies students will acquire, the expected learning outcomes, the required knowledge, and the transferable skills required by students to perform professionally. Finally, technological integration describes how and when the assisted reality system should be incorporated to add value to the learning process. This paper describes the work in the learning spaces currently under development by the partnership based on the same methodological and pedagogical foundations.
Synergies of Soft Computing and M&amp;S

This chapter of the SCS M&S Body of Knowledge starts with section on fuzzy logic and simulation, neural networks, and artificial intelligence. It then provides a detailed look at the agent metaphor and agent-based simulation.
Predicting the frequency of seismic events for subsurface engineering projects usingbackground microseismicity data

The general public is concerned about subsurface energy projects and their link to seismic activity.However, in order to meet the Net Zero requirements, low carbon technologies involving fluidinjection into the subsurface, such as geothermal, Carbon Capture and Storage (CCS) and gasstorage, must be developed and implemented. In an effort to enhance their confidence, it isimportant to comprehend the seismogenic effects and the triggering mechanism that suchtechnologies could have in a region.The frequency of occurrence of microearthquakes (earthquakes with magnitude &amp;lt; ML0 in theGutenberg &amp;#8211; Richter scale) in natural faults can provide crucial details about the evolution ofseismicity of a region and the geological structures that support this. The permanent seismicmonitoring networks, due to their detection threshold on small magnitude earthquakes (&amp;lt; ML1), donot enable for the long &amp;#8211; distance recording of all microearthquakes. These events have higherfrequencies that attenuate fast, making detection at stations 10s or more kilometres away difficult,if not impossible.In order to determine whether microearthquakes share the same characteristics as largerearthquakes and whether they can be used as a precursor to the occurrence of larger earthquakes,this research focuses on the study of the characteristics of natural microseismicity (frequency ofoccurrence, magnitude distribution, depth of hypocentres etc.). In July 2022, we deployed atemporary microseismic monitoring network in the southern Heraklion prefecture (Crete, Greece) incollaboration with the Hellenic Seismological Network of Crete with the aim of recording very smallin magnitude earthquakes (within the range of -0.5 &amp;lt; M &amp;lt; 3, i.e. two orders of magnitude below thecurrent completeness magnitude of M c 2.0). The network consisted of seven short periodseismometers, one of which was placed in the centre of a nearly circular geometry with theremaining six distributed around it at a radius of approximately 6.1km. This geometry provided agood azimuthal coverage for determining the hypocentres.In this work, we present analysis and results of the microseismic data collected from our localmonitoring network. We find a significantly larger number of microseismic events than that reportedfor the same time period within 50 km distance from the central station of our local network in thepublished seismic catalogue by the National Observatory of Athens. The number of detected eventswe report here refers to only those events that can be visually observed in the recordings. The realnumber of smaller in magnitude events is larger but obscured by noise. We use the detectedmicroseismic events to populate the Gutenberg Richter magnitude distribution for lower magnitudeevents and discuss the implications on the existing Gutenberg Richter magnitude distribution for theregion as derived from events recorded by the permanent seismic network in Crete.
Standards in Russian book publishing: Features of application and implementation

The article examines modern publishing standards in force on the territory of the Russian Federation, provides the list of the most important GOST standards for employees of the publishing sector, names the causes of non-compliance by Russian publishers with the standards in the preparation of book products, and shows ways to resolve the current situation. The conditions for the putting of GOST standards into effect, the complexity of their application, and the contradictions between them are analyzed. The article raises the problem of searching for updated GOST standards and/or those put into effect instead of outdated ones, and the question about the recommendatory nature of the standards, which allows unscrupulous publishers to violate GOST standards with impunit. The authors believe that, in order to speak about the high culture of editions and publications in Russia, it is necessary to regard the SIBID standards as mandatory requirements. On the basis of the undertaken analysis of the practice of applying the standards in the Russian book publishing, the expediency of applying the existing standards, adapting them to the modern publishing environment, etc., options for interaction between GOST developers and practical publishers, ways to consolidate the professional publishing community on the basis of the RBC platform in order to discuss topical issues of book publishing, testing new and updated GOST standards, refining of the existing standards by practical publishers are proposed. The authors of the article believe that the possibility of such a dialogue will improve the situation with Russian book publishing and will quickly remove contradictions in the GOSTs because only the compliance with publishing standards will allow publishers to maintain a high publication culture and book quality, and therefore will contribute to the creation of the professional image in the publishing industry and the publishing house reputation as a whole. The authors are convinced that it is necessary to have an open access to information about the introduced GOST standards at the stage of their development/implementation/testing, to indicate the developers of the standards, and to give an opportunity of giving a feedback to them (which means the creation of a centralized system of interaction within the professional community (e.g., on the RBC website)); that the changes in the standards should become predictable, expected and systematic, allowing to gradually introduce new standards and promptly make corrections to GOST before it is put into effect. All the theoretical provisions of the article are supported by the real examples from publishing practice. Also, as a part of the undertaken research, a survey was conducted among the students of the Ural Federal University (Yekaterinburg) related by the nature of their future activities to the SIBID standards. The survey has shown that there is currently an urgent need to involve practitioners in the development of new GOST standards. The authors declare no conflicts of interests.
Surface solar radiation trends over Europe assessed from ground-based measurements and satellite imagery and their comparison with climate models

Downward surface solar radiation (SSR) is the main component in the surface energy balance and the climate system, as well as being the fundamental source of energy in various forms of solar and photovoltaic technologies. It is therefore of great importance to know in detail the spatio-temporal variation of SSR, as well as its long-term trends. Scientific evidence has shown that the amount of solar radiation incident on the Earth&amp;#8217;s surface is not stable over the years, but undergoes significant variations every decade. Until recently, ground-based observations have been the most reliable data source for SSR monitoring. Nevertheless, satellite-derived SSR measurements have a better spatial and temporal coverage, though the scientific literature on the use of satellite imagery for the study of SSR is still limited.This study covers several purposes. First, a direct comparison between ground-based observations and satellite-derived estimates has been carried out, to determine the capability of the latter to reproduce measurements from surface observations. Monthly averaged time series of 108 land stations from GEBA (Global Energy Balance Archive) dataset (ground observations) have been compared to those estimated from satellite imagery by the Solargis model over the same locations. Solargis is a company based in Bratislava, dedicated to the assessment of the solar resource worldwide, using GIS (Geographic Information Systems). SSR anomalies measured at the surface and estimated from satellite images have been compared over Europe for the period 1994-2019. Second, multiannual SSR trends have also been calculated in detail (station-averaged and station-separated) for both ground-based and satellite-derived datasets, in the period of study. Finally, SSR time series have been compared to several CMIP6 (Coupled Model Intercomparison Project Phase 6 ) climate models runs.The results show that the method of estimating SSR from satellite images is able to reproduce around 94% of the variability of the SSR measured by ground-based methods in Europe. In addition, trend analysis shows a general increase of SSR over the continent in the period of this study, with an average trend of 3.5 Wm-2decade-1for the observational data and 1.7 Wm-2decade-1for the satellite estimations. This increase in SSR may be associated with changes in the transmission of the atmosphere due to variations in cloud properties and aerosols. Finally, CMIP6 time series average over all models for RCP8.5 scenario shows exactly the same trend as the satellite-derived dataset, which suggests there are still some variables not considered by satellite imagery methods and climate models.
Automatic Picking of Teleseismic SH- and SV-Phases using an Autoregressive and Singal Analytic Approach

In the recent decade, the amount of available seismological broadband data has increased steeply. Picking later arriving phases such as S-phases is difficult, and the number of manual picks available for these phases&amp;#160; is low compared to first arriving P-Phases. Moreover, data sets of manual picks can also be problematic, since phase arrival picks are sensitive to the parameters of the filtering, which are often unknown, and also depend on the individual picking behavior of the analysts. However, accurate arrival times, especially for these phases, could be used to improve the accuracy of velocity models obtained from seismic tomography. This necessitates the adoption of automatic techniques for determining teleseismic phase arrival times consistently over a large data set.In this work, a robust automatic picking algorithm based on multi-component, multiple-sample forward autoregressive prediction is examined with regards to its accuracy and applicability to massive data sets. The phase is identified using the Akaike criterion and the onset time is found evaluating discontinuities in the instantaneous period of the signal. This signal analytic approach is tested using synthetic waveforms as well as real data in conjunction with manual picks obtained from the reviewed ISC-catalog.Picking errors are estimated by comparing the automatic picks with manual picks, automatic picks at the neighboring stations as well as statistical methods. The quality evaluations suggest potential of using these automatically determined phase arrival times for SH and SV travel time tomography.
Linking Aftershock-free significant earthquakes to the dynamics of volcanic arcs; and linking aftershock-rich significant earthquakes to devolitization.

Earthquakes are typically followed by a series of aftershocks. Deeply trapped and internally generated high-pressure fluid diffuses along permeable paths and subsequently reactivates faults that drive thousands of seismic events. The thermal decomposition of CO2 in the carbonate regime in the central Apennines contributes significantly to seismogenesis and provides substantial quantities of internally derived high-pressure fluids. We develop a 3-dimensional model of non-linear diffusion with a source term that diffuses along faults and to the surroundings, triggering seismicity along the flow paths, and compare model results with the spatial and temporal observations from the 2009 L'Aquila (Mw 6.3) and the 2016 Amatrice-Visso-Norcia (Mw 6.5) earthquake sequences. The model mimics the generation of additional fluid by thermal decomposition and shows solid correlations in space by comparing the calculated fluid pressure field and the locations of over 50,000 well-constrained hypocenters.In contrast, other earthquakes result in only a small number or even no aftershocks. These include the Peru earthquake (Sep. 25, 2013 -Mw 7.1), the Mexiko earthquake (Sep. 19, 2017 - Mw 7.1), and the Crete earthquake (Oct. 12, 2021). Additionally, great earthquakes in Pakistan (Jan. 18, 2011 - Mw 7.2) and Iran ( Apr. 16, 2013 -Mw 7.7) also spawned no aftershocks despite the high magnitudes. These phenomena can be linked to the dynamics of volcanic arcs.
Analyzing geological maps at the continental scale

Geological maps are essential products of geological work that display the results of generations of field geologists&amp;#8217; work. Most original geological maps are generated and utilized at local scales. At regional scales, geological maps have gained practical significance ever since William Smith&amp;#8217;s 1815 geological map of England exemplified the robust nature of mapping and correlating strata beyond local scales. However, by comparison, geological maps compiled at continental scales appear to be of limited use outside of geological circles. They are often oversized, inhibiting their practical use, so they decorate our geoscience hallways and lecture halls with their beautiful colors and general esthetic appearance. Few outsiders can even read these maps. Their unique color-coding, the multiple non-diverging color schemes, and their complex legends further inhibit non-geologists from being able to recognize the enormous knowledge stored in these maps. I present an analysis of continent-scale geological maps by visualizing time not represented by the rock record (hiatus) and examining the dimensions of hiatal surfaces at interregional scales. The maps yield significant variability in sizes and space-time patterns of hiatal surfaces, a behavior expected in light of interregional-scale processes induced by both the plate and the plume mode of mantle convection. However, to rigorously test models of mantle convection, the temporal resolution of continent-scale maps must be increased to stages level, i.e., the temporal scale at which tectonic processes occur. In addition, synthesis of geological data on continent-scales requires the development and application of event-based stratigraphic-framework mapping.
Sentiment Analysis on Food-Reviews Dataset

There has been a vast development in the field of the internet, it has led people to express their opinions. It is very important to understand the customer for the successful businesses. Customers express their thoughts in the form of reviews, which can be positive or negative. For successful businesses, it is essential for them to understand a customer and their behavior because it will help them to grow their business more successfully. In this paper, we have proposed sentiment analysis of restaurant review datasets using multinomial naive bayes and logistic regression. This program will help owners quickly determine the customer's sentiments.
Secure Online Voting System Using Blockchain

In large democracy like India with about 83.4 crore voters it is estimated that about 50,000 crore rupees is spent on election and it takes about 6 weeks to conduct election. So the approach of election followed in India is costly both in terms of time and money. In order to reduce the cost of time and money, in last few decades numerous methods have already been developed and one of them is to use online voting system that providers the comfort of voting from remote location. But traditional online system has its own drawbacks like data tampering, privacy breach, etc. Blockchain technology is proposed, that incorporates cryptographic properties. Blockchain technology is simply defined as a decentralized, distributed ledger that maintains the provenance of a digital asset and is considered to be utmost secure. To avoid single point of failure, two step authentication process is used which include OTP verification and Biometric authentication. To ensure the anonymity of the votes, Identity-based ring signature scheme is used.
Detection of Bike Riders with no Helmet

Machine literacy refers to the creation of digital mechanisms which could make significant contributions without formal training by interpreting as well as extrapolating from attack patterns using mathematical techniques from various models. It's research wherein the system can improve itself autonomously as a consequence of the interaction and data. It's an example of artificial intelligence in action. In this study, we'll look at recognizing bike riders wearing or not wearing helmets in a video. To get the movies back into circulation, we'll have used the OpenCV Software. The YOLO and CNN models, that are utilized for enabling real-time license plate retrieval for a non-helmeted rider, the above two models are indeed the prescribed strategy for our device. The technology in our approach looks to see if the user who is riding the bike is wearing a helmet. If the rider is not wearing the required protective gear, the license plate is nonetheless extracted so that it could be supplied to activity recognition technology for evaluating and giving the fines to the prosecutor. For this design, we have a truly vast compass. It can be used to detect bikers who are not wearing a helmet. If the individual in the video is not wearing a helmet, the system is designed to detect the license plate of the bike and send an appropriate alert message to the appropriate person. This system is found to be more robust and effective than other algorithms.
Two-Step Text Recognition and Summarization of Scanned Documents

With the explosion of unstructured textual data circulating the digital space in present times, there has been an increase in the necessity of developing tools that can perform automatic text summarization to allow people to get insights from them easily and extract significant and essential data using Automatic Text Summarizers. The readability of documents can be improved and the time spent on researching for information can be improved by the implementation of text summarization tools. In this project, extractive summarization will be performed on text recognized from scanned documents via Optical Character Recognition (OCR), using the TextRank algorithm which is an unsupervised text summarization technique for performing extractive text summarization.
Face Mask and Social Distancing Detection Using Faster Regional Convolutional Neural Network

Face masks and social distancing is essential for many infectious diseases that spread through micro-droplets. According to WHO, the preventive measure for COVID-19 is to follow social distancing. Face Detection has expanded as a widespread concern in Computer Vision and Image processing. Many unique algorithms are developed using Convolutional Architectures to curate the algorithm as accurately as possible. First, the person in the video frames is pinpointed with the aid of Deep Learning (DL). The second step is to calculate the span between any two individuals through approaches of image processing. We aim to use a binary face classifier which will help us analyze the frame and help in detecting any face present irrespective of its alignment. Our proposed technique helps in generating accurate face segmentation masks from any random-sized input picture. We start by using an RGB image of any size, the approach uses Predefined Training for feature extraction. Faster Regional Convolutional Neural Networks are used for training. Here we segment out the faces present in the given image or frame semantically. Faster Regional Convolutional Neural Network (FRCNN) processes the given data faster with higher accuracy. The precision and decision-making are very elevated in Faster RCNN compared to others.
PENERAPAN BUSINESS INTELLIGENCE PADA MASKAPAI PENERBANGAN

Satisfying service is a priority for airlines so airlines compete to create comfort. In addition, flights also affect external matters because the airline needs to minimize errors which often occur. To overcome this, it is necessary to monitor the performance of airlines. One method used is the application of a business intelligence system which can provide reporting dashboards that can provide interactive data analysis information. The amount of data from flight traffic stored in the data warehouse becomes very useful if managed properly to get various kinds of hidden insights. With the implementation of business intelligence, the data can be utilized so it becomes a tool in making decisions for the development and improvement of air traffic and the impact of air traffic. The object of this research used raw data from public data. This research method used the Business Intelligence Roadmap (BIR). The results of the study made 3 dashboards: the first dashboard showed a pattern which was almost the same as the total delay data in 3 consecutive years. This was due to a high proportion of weather conditions in America. The second dashboard depicted a positive correlation between airplane visits and economic growth. The third dashboard indicated the number of flights that were frequently used by passengers as a standard reference that the need for and comfort for aircraft facilities because the quality of service provided by the airline was very important for consumers who used flight services.
The Use of Social Media Platforms in Language Learning: A Critical Study

Social media has changed the world of communication, information sharing, and entertainment. As schools continue to search for effective and efficient ways to teach students, this article reviews the usefulness of social media as a pedagogical tool to help learners acquire language. It concentrates on how social media can be used to improve language learning. Social media is mainly used to communicate and to encourage other people to learn more about the world. Writing skills and vocabulary are among the most common skills that students acquire when they use social media platforms like Facebook. Participants in the study showed that they are supportive of the incorporation of social media into language learning. It is evident that social media apps and sites are powerful in influencing the way children learn foreign languages in formal education as well as the ways they are used to help children learn a language outside of formal schools.