Reviewerly/exHarmony huggingface dataset:
Constraining the geotherm beneath the British Isles from Bayesian inversion of Curie depth: Integrated modelling of magnetic, geothermal and seismic data

Abstract. Curie depth offers a valuable constraint on the thermal structure of the lithosphere, based on its interpretation as the depth to 580 °C, but current methods underestimate the range of uncertainty. We formulate the estimation of Curie depth within a Bayesian framework to quantify its uncertainty across the British Isles. Uncertainty increases exponentially with Curie depth but this can be moderated by increasing the size of the spatial window taken from the magnetic anomaly. The choice of window size needed to resolve the magnetic thickness is often ambiguous, but based on our chosen spectral method, we determine that significant gains in precision can be obtained with windows sizes 15–30 times larger than the deepest magnetic source. Our Curie depth map of the British Isles includes a combination of window sizes: smaller windows are used where the magnetic base is shallow to resolve small-scale features, and larger window sizes are used where the magnetic base is deep in order to improve precision. On average, the Curie depth increases from Laurentian crust (22.2 ± 5.3 km) to Avalonian crust (31.2 ± 9.2 km). The temperature distribution in the crust, and associated uncertainty, was simulated from the ensemble of Curie depth realisations assigned to a lower thermal boundary condition of a crustal model (sedimentary thickness, Moho depth, heat production, thermal conductivity), constructed from various geophysical and geochemical data sets. The uncertainty of the simulated heat flow field substantially increases from ± 10 mW m−2 for shallow Curie depths ~ 15 km to ± 80 mW m−2 for Curie depths &gt; 40 km. Surface heat flow observations are concordant with the simulated heat flow field except in regions that contain igneous bodies. Heat flow data within large batholiths in the British Isles exceed the simulated heat flow by ∼ 25 mW m−2 as a result of their high rates of heat production (4–6 μW m−3). Conversely, heat refraction around thermally resistive mafic volcanics and thick sedimentary layers induce a negative heat flow misfit of a similar magnitude. A northward thinning of the lithosphere is supported by shallower Curie depths on the northern side of the Iapetus Suture, which separates Laurentian and Avalonian terranes. Cenozoic volcanism in Northern Britain and Ireland has previously been attributed to a lateral branch of the proto-Icelandic mantle plume. Our results show that high surface heat flow (&gt; 90 mW m−2) and shallow Curie depth (∼ 15 km) occur within the same region, which supports the hypothesis that lithospheric thinning occurred due to the influence of a mantle plume. That the uncertainty is only ± 3–8 km in this region, demonstrates that Curie depths are more reliable in hotter regions of the crust where the magnetic base is shallow.
Automated Seismic Source Characterisation Using Deep Graph Neural Networks

We propose a deep learning approach for automated earthquake location and magnitude estimation based on Graph Neural Network theory• This new approach processes multi-station waveforms and incorporates station locations explicitly• Including station locations improves the accuracy of epicentre estimation compared to models that are location-agnostic
CERTAIN INTEGRAL TRANSFORMS OF THE GENERALIZED K-STRUVE FUNCTION

The aim of this paper is to the study of Struve type functions.Using k-Struve functions, we derive various integral transforms, including Euler transform, Laplace transform, Whittakar transform, K-transform and Fractional Fourier transform.The transform images are expressed in terms of the generalized Wright function.Interesting special cases of the main result are also considered.
Teaching-Learning Based Task Scheduling Optimization in Cloud Computing Environments

Generating optimal task scheduling plans in cloud environments is a tedious task as it is a np-hard problem. The optimal resource allocation in cloud environments involves more search space and time consuming. Therefore, recent researchers are focused on implementation of artificial intelligence to solve task scheduling problem. In this paper, a new and efficient evolutionary algorithm named teaching-learning based algorithm has been implemented first time to solve the task scheduling problem in cloud environments. The current research work considers the task scheduling problem as a multi-objective optimization problem. The proposed algorithm finds the best solution by minimizing the execution time and response time while maximizing the throughput of all resources to complete the assigned tasks.
Indexed Crime Data Visualization Utilizing Self-Organizing Map Algorithm

Visualization is the transformation of symbolic representation to geometric representation.The goal of visualization is to analyze, explore, discover, illustrate, and communicate information in well understandable form.Visualization of crime data provides sufficient input to the authorities on the trends of crimes in the locality.This study clustered the indexed crime data of Misamis Occidental Philippines.Further, the study utilized Self-Organizing Map (SOM) to visualize the data.Results showed that five indexed crime data were clustered together, and three crime data were on the same cluster.
19. The data epic : Visualization practices for narrating life and death at a distance

This chapter proposes the notion of the 'data epic', which is examined through two works of 'cinematic data visualization': The Fallen of World War II and The Shadow Peace: The Nuclear Threat.These pieces mobilize an aesthetics of distance to narrate life and death at scale, in past and possible global conflicts.While previous studies of quantification emphasize the function of distance in relation to aspirations of objectivity, this chapter explores other narrative and affective capacities of distance in the context of 'public data culture'.The data epic can thus enrich understanding of how data are rendered meaningful for various publics, as well as the entanglement of data aesthetics and data politics involved in visualization practices for picturing collective life.
Home / Archives / Vol 4 No 2 (2020): December 2020 / Articles Ecolinguistic study of the writing works of achieving students Bali State Polytechnic in 2020

Bali State Polytechnic (PNB) has used the label "green", such as the "green campus" and "green tourism", as a flagship program to show its concern for ecological awareness. Ecolinguistics is a combination of linguistics and ecology studying the relationship between the use of language in discourse related to the environment. This study aims to see how the ecological insights possessed by outstanding students are revealed through their written works (languages). From the primary data source in the form of 10 written works of achieving students in 2020, ecological insights will be identified in which they are revealed in the choice of words containing ecological content. The results show that the average ratio of ecological word used and the number of words in writing is 0.84%, while the average vocabulary, 9 types of ecological words for each writing contain an average of 2065 words. With the assumption based on the cognitive linguistic theory that thoughts are expressed through language, it can be concluded that students' ecological insight and awareness have not developed optimally as far as it is expressed through word choice (diction). It suggests that the PNB students need to broaden their ecological insights through language learning, especially reading texts based on ecolinguistics.
Digitalization of university management in Russia and foreign countries as a necessary measure to ensure their economic security

This publication is devoted to the problems of digitalization of university management, which the authors consider as a universal trend aimed at improving the management system in order to improve the quality of educational services and ensure dynamically sustainable and balanced development of universities. Research objectives: to determine the impact of digitalization (digitalization) on the development of the education management system; to identify the factors hindering the development of the education management system. Methodology: Based on a systemic and structural approach, as well as on key paradigms describing promising management transformations under the influence of digitalization, the authors studied the current state and potential of digitalization of management of universities in Russia, Argentina, Chile and Brazil (Latin American countries), and compared the indicators with similar data on the control group of countries (USA, China and Germany). Assessment and analysis were carried out using the expert method and the method of cluster analysis; the leading experts involved in the problems of public administration and digitalization of management of universities in Russia acted as experts. Results: It was found that digitalization of management has a significant potential for a positive impact on the competitiveness of educational services, on the quality of its pro-vision, on the sustainable, dynamic and balanced development of universities. The study revealed that there are cases of obvious breakthroughs in terms of digitalization of management of individual universities – and on their basis it is possible to build effective digitalization projects that can be adopted locally or replicated in the interests of intensive development and increasing the competitiveness of national higher education systems. The situation with the digitalization of university management in Russia and in the countries of Latin America selected for study is characterized by the presence of numerous common aspects, which suggests the possibility and validity of the implementation of universal solutions suitable for replication. The research materials were supplemented with the results of questionnaires and interviews of administrative managers working at universities. Conclusions. The analytical data obtained indicates that the universities of Russia and selected countries of Latin America remain catching up in the trends of global development of digitalization of management of higher education institutions, while universities in Russia, Chile, Brazil have a high potential for digitalization of management, primarily technological and personnel, and the assessment of the corresponding potential in the universities of Argentina is generally positive. Guided by the relevant data, as well as the materials of generalization of the digitalization practice of individual universities, it seems possible to significantly expand the horizons set by this study.
A Pointer Generator Network Model to Automatic Text Summarization and Headline Generation

In a world where information is growing rapidly every single day, we need tools to generate summary and headlines from text which is accurate as well as short and precise. In this paper, we have described a method for generating headlines from article. This is done by using hybrid pointer-generator network with attention distribution and coverage mechanism on article which generates abstractive summarization followed by the application of encoder-decoder recurrent neural network with LSTM unit to generate headlines from the summary. Hybrid pointer generator model helps in removing inaccuracy as well as repetitions. We have used CNN / Daily Mail as our dataset.
Security Enhanced Model for Cloud Data Based on Dynamic Data Fragmentation and Replication (DDFR)

Nowadays cloud computing is utilized in several IT capabilities like smart industry with IoTs, Mobile computing, etc., It is happen through outsourcing data to a third-party administrative control which is great application of cloud. But this leads to data leakage through attacks. A high level of data security is required on the data stored in cloud nodes. This paper enhances the security and data availability by dynamic fragmentation and replication process. The fragmentation is performed in runtime to create the fragments according to the available virtual machine. The replication aims to enhance the better load balance with less number of replicas. Bee colony algorithm is used for finding the best node in replication. The AES encryption approach is used for encrypting the fragments. This approach does not provides the original data if any attacks happened successfully.
Spam Account Detection on Social Media using Machine Learning Methods: A Survey

Internet has become a world of social media and communication.In our daily life usually every person is using a social media like a Facebook, twitter and LinkedIn for communication and knowledge widening.These social media sites are misused for communication by creating fake accounts.These accounts do not have genuine data.Fake accounts mean some people are using other's personal data for creating fake profile.Fake accounts frequently spam legitimate users, posting inappropriate or illegal content.These social media sites are being used to spy, send malicious links and carry out financial frauds.Platforms of social media are more effectual for conman since they give permission to striker to build a bond with their targets.We are using several new features, which are more effective and robust compared to existing features like selection, generalize batter, interpretable.In our proposed system, we are using classification and regression method for detecting fake accounts on social media which are more efficient and accurate as compared to the existing ones.
A Fuzzy Based Classification – An Experimental Analysis

Soft Computing has become popular in developing systems that encloses human expertise. Imaging technologies and clinical cytology has improved in disease diagnosis. Exact detection is extremely important for proper treatment and cure of disease. Two soft computing technique Neural Network and Support Vector Machine are used for classification of Caridotocography data set. This paper clearly explains the advantages of hybrid technique, when Fuzzy is combined with Neural Network and Support Vector Machine it is clearly noticed that there is an increase in accuracy of classification rate.
PENINGKATAN KEMAMPUAN MENULIS TEKS FABEL MENGGUNAKAN METODE DISCOVERY LEARNING SMP NEGERI 8 PONTIANAK

AbstractThis research is motivated by the result of observation which states the lack of writing ability in writing aspect. This is due to several factors one of which is the lack of student interest in writing skills, making it difficult to pour ideas of students. The problem of this study is how the process of learning, activity and motivation of students, and the results of learning discovery learning in students of class VII F SMP Negeri 8 Pontianak?This study was conducted in two cycles. Each cycle consists of planning, acting, observation, and reflecting. The research method used is descriptive method, the research form is qualitative, the research procedure is classroom action research (PTK). Data were taken using test and non test instrument. The data collection tools used are the observation sheet, motivation questionnaire, and the learning result test.The results showed that the discovery learning method can improve the learning result of writing fable text on the students of class VII F SMP Negeri 8 Pontianak. It was found that: (1) learning process of writing fable text using discovery learning method has increased. (2) activity and motivation of student in learning to write text of fable by using discovery learning method each cycle better. (3) learning outcomes are also increasing. This is seen in the average of each cycle, in cycle 1 average 73.96, cycle 2 increased to 85.68. (4) students' attitudes following learning improved greatly. Keywords: Writing skill, Text Fable, Discovery Learning Method.
Methods and Algorithms for Knowledge Reuse in Multiagent Reinforcement Learning

Reinforcement Learning (RL) is a powerful tool that has been used to solve increasingly complex tasks. RL operates through repeated interactions of the learning agent with the environment, via trial and error. However, this learning process is extremely slow, requiring many interactions. In this thesis, we leverage previous knowledge so as to accelerate learning in multiagent RL problems. We propose knowledge reuse both from previous tasks and from other agents. Several flexible methods are introduced so that each of these two types of knowledge reuse is possible. This thesis adds important steps towards more flexible and broadly applicable multiagent transfer learning methods.
Mass Media Exposure and use of family planning in Ethiopia: Evidence from EDHS 2016

Abstract Background Mass media is a significant force in the digital divide era in which communities and individuals are engaged constantly with messages from a crowd of information sources. As family planning is influenced by mass media, there is a need to study the impact of mass media exposure on family planning utilization.
Exergaming With Beat Saber: An Investigation of Virtual Reality Aftereffects

Background: Virtual reality (VR) exergaming has the potential to target sedentary behavior. Immersive environments can distract users from the physical exertion of exercise and can motivate them to continue exergaming. Despite the recent surge in VR popularity, numerous users still experience VR sickness from using head-mounted displays (HMDs). Apart from the commonly assessed self-reported symptoms, depth perception and cognition may also be affected when users experience VR sickness. Considering the potential benefits of VR exergaming, it is crucial to identify the adverse effects limiting its potential and continued uptake.Objective: This study aims to investigate the consequences of playing one of the most popular VR exergames for 10 and 50 min on aspects of vision, cognition, and self-reported VR sickness.Methods: A total of 36 participants played a popular VR exergame, called Beat Saber, using an HMD. A repeated measures within-subject design was conducted to assess changes in vision, cognition, and well-being after short (10 min) and long (50 min) durations of VR exposure. We measured accommodation, convergence, decision speed, movement speed, and self-reported sickness at 3 test periods—before VR, immediately after VR, and 40 min after VR (late).Results: Beat Saber was well tolerated, as there were no dropouts due to sickness. Overall, for most participants, any immediate aftereffects were short-lived and returned to baseline levels after 40 min of exiting VR. For both short and long exposures, there were changes in accommodation (F1,35=8.424; P=.006) and convergence (F1,35=7.826; P=.008); however, in the late test period, participants returned to baseline levels. Measures on cognition revealed no concern. The total simulator sickness questionnaire (SSQ) scores increased immediately after VR (F1,35=26.515; P&amp;lt;.001) and were significantly higher for long compared with short exposures (t35=2.807; P=.032), but there were no differences in exposure duration in the late test period, with scores returning to baseline levels. Although at a group level, participants’ sickness levels returned to baseline 40 min after VR exposure, approximately 14% of the participants still reported high levels of sickness in the late test period after playing 50 min of Beat Saber. We also showed that the participants who experienced a high level of sickness after a short exposure were almost certain to experience a high level of symptoms after a longer exposure.Conclusions: Irrespective of the duration of exposure, this study found no strong evidence for adverse symptoms 40 min after exiting VR; however, some individuals still reported high levels of VR sickness at this stage. We recommend that users commit to a waiting period after exiting VR to ensure that any aftereffects have deteriorated. Exergames in HMDs have the potential to encourage people to exercise but are understudied, and the aftereffects of exergaming need to be closely monitored to ensure that VR exergames can reach their full potential.
Privacy Preserving Outsourced Calculations With Symmetric Fully Homomorphic Encryption

Cloud computing is a new paradigm which provides cloud storage service to manage, maintain and back up private data remotely. For privacy concerns the data is kept encrypted and made available to users on demand through cloud service provider over the internet. The legacy encryption techniques rely on sharing of keys, so service providers and end users of the cloud have exclusive rights on the data thus the secrecy may loss. Homomorphic Encryption is a significant encryption technique which allows users to perform limited arithmetic on the enciphered data without loss of privacy and security. This paper addresses a new simple and non-bootstrappable Fully Homomorphic Encryption Scheme based on matrices as symmetric keys with access control.
Evaluation of Clusters based on Systems on a Chip for High-Performance Computing: A Review

High-performance computing systems are the maximum expression in the field of processing for large amounts of data. However, their energy consumption is an aspect of great importance, which was not considered decades ago. Hence, software developers and hardware providers are obligated to approach new challenges to address energy consumption, and costs. Constructing a computational cluster with a large amount of systems on a chip can result in a powerful, ecologic platform, with the capacity to offer sufficient performance for different applications, as long as low costs and minimum energy consumption can be maintained. As a result, energy efficient hardware has an opportunity to impact upon the area of high-performance computing. This article presents a systematic review of the evaluations conducted on clusters of ystems on a Chip for High-Performance computing in the research setting.
Students’ choice of digital educational resource: Psychological aspect

The issue of psychological feasibility of distance learning is understudied and remains relevant both in theoretical and practical terms. The purpose of the study viewed in the article is to identify the connection between students’ preferences to study using the Internet and electronic textbooks with their personal psychological traits. Presumably, confident, self-organized students have a positive attitude towards their use, while anxious, emotionally unstable students have a negative attitude. The study involves 1st – 4th year students (N = 270, aged from 17 to 21 years; 61.3% women) students, who specialize in Economics and Agricultural Industry in Izhevsk State Agricultural Academy. We used the following techniques: questionnaire developed by O. N. Malakhova, O. A. Zhuchenko, aimed at studying students’ preferences and Cattell’s Personality Factor Questionnaire (16-PF), form C, which has the goal to study personality traits that are significant for the research. It was found that there was a direct interconnection between students’ preference to use digital educational resources with their personal psychological traits. The study revealed that students with self-discipline and analytical mindset have a negative attitude to the use of electronic textbooks and distance learning. It was found that among the main personal psychological characteristics of students, which influence their preferences, are sensitivity and radicalism, anxiety and lack of self-discipline. We did not confirm the hypothesis put forward in the study. The applied aspect of the problem under study can be implemented in educational practice to increase the efficiency of educational process and in the development of digital training courses. The research perspectives are related to the study of teacher preferences.