Reviewerly/exHarmony huggingface dataset:
LITHOGEOCHEMISTRY OF THE HICKS DOME CRITICAL MINERAL RESOURCE, ILLINOIS-KENTUCKY FLUORSPAR DISTRICT, USA: IMPLICATIONS FOR GENESIS

Hicks Dome is a proposed source of fluorine in coarse crystalline fluorite (~7 Mt) produced from MVT deposits of the IL-KY fluorspar district. Recent work by Hicks Dome LLC has defined a 65.8 Mt area-wide, critical mineral resource with 15.8% fluorite, 4.8% barite, 1.3% TiO2, 0.3% REY oxide, 0.2% Nb2O5, and 0.16% BeO, at a fluorite cut-off grade of 10%. Fine-grained, purple fluorite mineralization occurs in rock-flour-matrix, solution-collapse breccias in Ordovician to Silurian platform carbonate rocks at depths of 700-1000 m. The critical mineral resource is associated with 271 Ma lamprophyre dikes and diatremes, a Th radiometric anomaly, and an upper crustal magnetic anomaly. Multielement analyses of drill core (N=3,176 samples; 6,700 m) and micro-XRF maps of 3 high-grade samples were used to identify element associations, characterize mass transfer, and investigate ore-gangue relationships. In comparison to average upper crust, Hicks Dome ore is enriched in Be, Ba, Th, U, Nb, Ti, Sr, Pb, Zn, HREE, F, P, Mg, and Ca and depleted in Mn, Fe, Al, and Si. Chondrite-normalized REE patterns mimic patterns typical for the main REE mineral, xenotime, with patterns decreasing from La to Nd and elevated concave patterns from Sm to Lu. Compared to lamprophyre dikes, the REE patterns of ore are enriched in Sm to Lu. Element associations relate to lithology and mineralization as follows: carbonate rocks (Ca, Mg, Sr), terrigenous detritus (Si, Al, K, Ti), lamprophyre (Si, Al, Fe, Mg), critical minerals (F, Ba, Sr, Ti, Nb, Th, Y, REE, and Be), and sulfides (Fe, S, As, Zn, Pb). Our hypothesis is that more extensive mafic, alkaline magmas were emplaced beneath the Paleozoic stratigraphy and that degassing and condensation of acid volatiles into a basinal brine caused dissolution of carbonate rocks and formation of caverns and collapse breccias. Repetitive input of magmatic fluids containing critical elements preferentially replaced rock-flour-matrix breccias to form high-grade ore. The critical element assemblage may be related to a highly fractionated alkaline intrusive phase or carbonatite with further hydrothermal enrichment facilitated by the presence of specific ligands (Cl, F, PO4, SO4, HCO3, CO3).
Autoencoding Structural Equation Models

In this note we explore a fully unsupervised deep-learning framework for simulating non-linear structural equation models from observational training data. The main contribution of this note is an architecture for applying moment-matching loss functions to the edges of a causal Bayesian graph, resulting in a generative conditional-moment-matching graph-neural-network. This framework thus enables automated sampling of latent space conditional probability distributions for various graphical interventions, and is capable of generating out-of-sample interventional probabilities that are often faithful to the ground truth distributions well beyond the range contained in the training set. These methods could in principle be used in conjunction with any existing autoencoder that produces a latent space representation containing causal graph structures.
THE EFFECT OF JIGSAW COOPERATIVE LEARNING ON STUDENTS’ GRAMMAR MASTERY

The objective of this research was to find out whether there is significant effect of using jigsaw cooperative learning on students’ grammar mastery, especially in present continuous tense. The research method of this study was experimental research in quantitative approach. The population of this research was the whole students of the eleventh grade students of SMA Prayatna Medan that consists of 188 students that divided into two class, experimental and control class as the samples that consisted of 30 students each class. Both classes were given pre-test that consisted of 20 questions in multiple choices form. After that, experimental class was treated by using jigsaw cooperative learning, while control class was treated by using conventional way. After given the treatment, both classes were given post-test that also consisted of 20 questions in multiple choices form. To analyze the data, the researcher used t-test. According to the result of statistical calculation by using SPSS 2018 showed that Sig. (2-tailed) was lower than significant level (0.004 2.002. It means that there is a significant effect of using jigsaw cooperative learning on students’ grammar mastery. Then, H a was accepted and H o was rejected.
Collaborative educational location-based activities with no teacher supervision: design implications

Comunicacio presentada al DELbA 2020: Workshop on Designing and Facilitating Educational Location-based Applications, celebrat el 15 de setembre a Heidelberg, Alemanya.
Efficacy of CMC-based English Learning Environments: Meta-analysis

본 논문에서는 컴퓨터 매개 의사소통(Computer-Mediated Communication, CMC)의 전반적인 효과성을 메타분석하였다. 국내에서 출간된 29편의 연구를 바탕으로 총 218개의 개별효과크기를 추출하였으며, 이를 연구대상, 연구기간, 집단크기, 연구 영역 및 세부 하위영역에 따라 효과크기를 분석하였다. 연구에 활용한 논문은 “CMC”, “컴퓨터 매개 학습”이라는 검색어를 사용하여 RISS 학술연구정보서비스 및 구글 등을 통해 수집하였다. 분석 결과, “CMC”를 활용한 영어학습은 전체적 으로 중간 크기의 효과를 나타내어(ES=.69) 학습자에게 긍정적인 효과를 미친 것으로 나타났다. 세부적으로는, 정의적 영역의 효과크기(ES=.71)가 인지적 영역의 효과크기(ES=.69)보다 약간 더높았다. 특히, CMC 기반 영어수업은 어휘 및 문법(ES=.87)과 쓰기(ES=.81)에서 더 효과적이었다. 한편, 정의적 영역의 태도 측면에서는 긍정적인 결과를 보였으나(ES=.92), 학습전략 영역에서는 유의한 효과가 없었다. 또한, CMC는 중·고등학생보다 대학생과 초등학생에게, 중간 기간(5주-11주) 과 장기간(12주 이상)일 때, 그리고 소집단일 때 더 효과적인 것으로 나타났다. 현재 영어교육에서는 컴퓨터를 포함하여 다양한 멀티미디어 기기나 플랫폼 및 SNS 기기들이 활용되고 있는데, 본 연구에서 나타난 결과들을 감안하여 수업을 설계한다면 보다 큰 성과를 거둘 수 있을 것이라 생각된다.This study aims to investigate the overall efficacy of computer-mediated communication (CMC) based English learning environments in Korea. A meta-analysis of 218 effect sizes in 29 research articles, which have been published in Korean academic journals, explored cognitive and affective domains in terms of subjects, the number of subjects, and treatment period. The research articles were collected by searching through RISS and Google with keywords such as “CMC” and “computer-mediated learning.” The results showed that English classes using CMC based language learning contexts had positive effects on English learners in general (ES=.69). The overall effect size of the affective domains (ES=.71) was slightly greater than that of the cognitive domains (ES=.69). More specifically, CMC based English learning contexts were particularly effective in listening (ES=.88), writing (ES=.81), and vocabulary and grammar (ES=.87). In terms of affective domains, applying CMC had positive effects on the attitudes toward English learning (ES=.92). However, there was no significant effect on developing learning strategies. Also, it was found to be more effective for university and elementary school students than for middle school and high school students. These results suggest that applying CMC based learning contexts in English classes could be helpful in improving learners’ English abilities. Currently, various multimedia devices, platforms and SNS devices, including computers, are being used in English education, and it is thought that greater results can be achieved if English classes are designed considering the results of this study.
Reducing and linking spatio-temporal datasets with kD-STR

When linking spatio-temporal datasets, the kD-STR algorithm can be used to reduce the datasets and speed up the linking process. However, kD-STR can sacrifice accuracy in the linked dataset whilst retaining unnecessary information. To overcome this, we propose a preprocessing step that removes unnecessary information and an alternative heuristic for kD-STR that prioritises accuracy in the linked output. These are evaluated in a case study linking a road condition dataset with air temperature, rainfall and road traffic data. In this case study, we found the alternative heuristic achieved a 19% improvement in mean error for the linked air temperature features and an 18% reduction in storage used for the rainfall dataset compared to the original kD-STR heuristic. The results in this paper support our hypothesis that, at worse, our alternative heuristic will yield a similar error and storage overhead for linking scenarios as the original kD-STR heuristic. However, in some cases it can give a reduction that is more accurate when linking the datasets whilst using less storage than the original kD-STR algorithm.
RESEARCH ON CHINESE INDIVIDUAL QUANTIFIERS

Nowadays more and more people are interested in learning Mandarin, because nowadays Mandarin is the second most important language in the world. Indonesia has also expanded its cooperation with China, so that now more and more human resources are needed, especially which are fluent in Mandarin. With these conditions, more and more people in Indonesia, especially students, are very interested in mastering this field of science. Like Indonesian, in Mandarin also has quantifiers or measure word, the Chinese words are more diverse. One of them is the individual quantifiers. Individual quantifiers are special measure words which only used for certain nouns, for example 纸 （ zhǐ = paper ） using measure word 张 （ zhāng ） , 衣服 （ yīfu = clothes ） using measure word 件 （ jian ） and so on. However, because Chinese individual quantifiers that are too diverse become a point of difficulty for students, causing students who are learning Mandarin are often wrong in using individual quantifiers. Therefore, I am interested in analyzing the mistakes of individual quantifiers of Bunda Mulia University students. I hope the results of this analysis can be useful for instructors of Mandarin, especially at Bunda Mulia University so that teachers know the weaknesses of students in using measure word, and also so that teachers can help students to avoid or reduce errors in using measure word .
Perancangan Perangkat Lunak Segmentasi Citra Luka Kronik Dengan Menggunakan Algoritma Metode Fuzzy C-Means

The process of segmentation in digital images that separates an object from the background or background can be obtained from the RGB value in each pixel on a digital image so that the object can be processed for other purposes. Along with the development of technology in applications that process digital images, the process of segmentation is becoming increasingly necessary. The results of segmentation must also be more accurate because if the segmentation results are inaccurate it will affect the results of the next process. In general, the segmentation process is divided into three parts based on classification, based on edges and by region. From an image information can be obtained in accordance with the interests, but sometimes on existing images can not be obtained directly the information needed. There are times when it is difficult to process information from an image directly in plain view by only relying on the sense of sight. This is understandable, considering that the image is a large collection of dots of color. One alternative to help display and process this information is by segmentation.
Machine Learning, Its Limitations, and Solutions Over IT

Machine learning is an investigation of computer algorithms and sample data to build a mathematical standard model for making decisions without programming. Machine learning means the computer system is performing a task without being programmed for that task. We studied the machine learning concept in detail by exploring the relationship of machine learning with other fields, machine learning approaches, machine learning models, and limitations of machine learning. There are three main approaches for machine learning study supervised, unsupervised, and semi-supervised. Other than this Reinforcement machine learning, self-learning, feature learning, sparse dictionary learning, deviation detection, and robot learning. An artificial neural network, decision trees, support vector machines, regression analysis, Bayesian network, genetic algorithms, and some training models.
Bibliometric-enhanced information retrieval (bir): A scientometrics assessment of global publications output during 1989-2020

The study examines quantitative and qualitative features of global research output (557) in the domain of Bibliometric-enhanced Information Retrieval (BIR), an upcoming field of research in the discipline of library and information science. The data for the study was sourced from the Scopus database for the period 1989-2020. The BIR research at the global level registered (22.17%) annual average growth and averaged research impact of (21.69%) citations per paper since publication. Although a total of 60 countries had participated in the subject, the distribution of research by country of publication is highly skewed. The top 10 most productive countries in the world alone account for the largest (92.64%) global publications share and (93.16%) global citations share in the subject. At the institutional level, the distribution of research in the subject is scattered across a total of 290 global organizations. The top 10 most productive organizations alone account for (22.62%) global publication share and a (15.55%) global citation share. The top 10 authors contributed (21.36%) global publication share and (6.57%) global citation share respectively during the period. The most productive institute in the subject is the Leibniz Institute of Social Sciences, Germany (30 papers). The Indiana University Bloomington, USA (74.25% and 3.42%) is the most cited institute in the world. P. Mayr (30 papers) is the most prolific author in the world and Y. Ding (126% and 1.71%) the most cited author in the subject. The Journal of the Medical Library Association (24 papers) and Journal of the American Society for Information Science & Technology (18 papers) are the most productive journals on this theme.
From ‘blasphemy’ to ‘hate speech’

The aim of this chapter is to examine the issue of blasphemy and related laws in Europe. We examine the controversial position of Muslims in many European countries and explore ramifications of Islam’s increasing public presence for political and social relationships between the region’s Muslim minority and the non-Muslim majority. In addition, we look briefly at a shift from concern with blasphemy to problems associated with hate speech.
Beware the Trojan horse

Introduction: In this era of social distancing, dependence on electronic gadgets and devices is ever increasing. Possibility of transmission of COVID -19 from these devices cannot be ruled out. Currently, prevention is the only antidote; considering the mounting evidence of transmission of COVID-19 virus primarily through respiratory droplets and fomites. Gadgets are one of the most frequently touched appliances; being often used during or after patient examination, while handling specimens or during various patient procedures. Collecting data regarding mobile sanitization is prudent at these times. The aim of this systematic review is to summarize published evidence on mobile sanitization in these COVID times.Methods: PubMed search on “COVID and mobile phone” revealed only 4 articles related to this topic; therefore we have assimilated data from various organizations, websites and articles and have suggested methodology for sanitization of mobile phones and other gadgets.Results: Infection control practices which include information, communication, education and evaluation are the pre-eminent weapons in the fight against hospital transmitted SARS-CoV-2. Minimizing mobile phone usage, sanitizing them with endorsed biocidal agents and advancements in the field of ultraviolet cabinets is a priority.Conclusion: At the same time we shouldn’t forget that sanitization is only one aspect of prevention of this disease; maintaining hand hygiene, use of masks and social distancing must be followed at all times.
Identifying Crime Hot Spots

The mapping and spatial analysis of crime covers a kaleidoscope of techniques and has been used to explore a variety of topics. Crime mapping using Geographical Information System (GIS) helps better visualization and organization of spatial data for more formal statistical analysis. Spatial analysis is employed in both an exploratory and as well as a more confirmatory manner with the primary purpose of analyzing how certain community or ecological factors (such as population characteristics or the built environment) influence the spatial pattern and trend of crime. This chapter addresses the novices with crime mapping defining crime hot spots; what they are, why they exist, and how to map them. The tool uses vectors to identify locations of statistically significant hot spots and cold spots in the data by aggregating points of occurrence into polygons or converging points that are in proximity to one another based on a calculated distance. It further discusses the use of regression analysis model to examine and explore spatial relationships, and help explain the factors behind their observed spatial pattern.
Introduction to the special issue on Methods and applications in the analysis of social data in healthcare

Image-text retrieval task has been a popular research topic and attracts a growing interest due to it bridges computer vision and natural language processing communities and involves two different modalities. Although a lot of methods have made a great progress in image-text task, it remains challenging because of the difficulty to learn the correspondence between two heterogeneous modalities. In this paper, we propose a multi-level representation learning for image-text retrieval task, which utilizes semantic-level, structural-level and contextual information to improve the quality of visual and textual representation. To utilize semantic-level information, we firstly extract the nouns, adjectives and number with high frequency as the semantic labels and adopt multi-label convolutional neural network framework to encode the semantic-level information. To explore the structure-level information of image-text pair, we firstly construct two graphs to encode the visual and textual information with respect to the corresponding modality and then, we apply graph matching with triplet loss to reduce the cross-modality discrepancy. To further improve the retrieval results, we utilize the contextual-level information from two modalities to refine the rank list and enhance the retrieval quality. Extensive experiments on Flickr30k and MSCOCO, which are two commonly datasets for image-text retrieval, have demonstrated the superiority of our proposed method.
What States and Cities Should Consider in Response to Cybersecurity Incidents

State Tech recommends strong education and training programs, capable partnerships, and robust communication with stakeholders during a cyber incident.
Data Communication and Clustering

Data Communication is the daily process , in one way or the other the data is communication from one place to another and at any time. This paper explores the concept of the data communication as wall as sees the concept of the data communication using the view of clustering and explains in brief the benefits of clustering of data
The Application and Problem Analysis of Computer Technology in English Information Processing

Abstract The traditional method of automatic summarization is based on the statistical extraction of abstract sentences from the grammar, without semantic analysis of the text, resulting in low summarization accuracy. In order to overcome the shortcomings of traditional methods, this article proposes an automatic summarization method based on topic concepts. An English automatic summarization system is designed and implemented based on concept statistics and analytic hierarchy. Concept statistics replace traditional word frequency statistics, based on the main super The concept constructs a vector space model, calculates the importance of the sentence, selects the distribution of the main super concept on the concept hierarchy tree, analyzes the text structure and divides the meaning block, and extracts the abstract with the meaning block as the unit. Preliminarily solved the problem of unbalanced abstract structure of multi-topic articles. The experimental results show that through the methods of concept statistics and semantic hierarchy analysis, the abstracts generated by the English information processing system are more accurate and reflect the main content of the original text more comprehensively.
Caption Alignment for Low Resource Audio-Visual Data

Understanding videos via captioning has gained a lot of traction recently.While captions are provided alongside videos, the information about where a caption aligns within a video is missing, which could be particularly useful for indexing and retrieval.Existing work on learning to infer alignments has mostly exploited visual features and ignored the audio signal.Video understanding applications often underestimate the importance of the audio modality.We focus on how to make effective use of the audio modality for temporal localization of captions within videos.We release a new audio-visual dataset that has captions time-aligned by (i) carefully listening to the audio and watching the video, and (ii) watching only the video.Our dataset is audio-rich and contains captions in two languages, English and Marathi (a low-resource language).We further propose an attention-driven multimodal model, for effective utilization of both audio and video for temporal localization.We then investigate (i) the effects of audio in both data preparation and model design, and (ii) effective pretraining strategies (Audioset, ASR-bottleneck features, PASE, etc.) handling low-resource setting to help extract rich audio representations.
Generation System of Dispatching Order Sheet for Distribution Network with Self Checking Function Based on Generative Countermeasure Network

Abstract Although the recent wave of artificial intelligence research led by deep learning has achieved very good results in the field of supervised learning. Unsupervised learning, as a method that can truly allow computers to learn from unlabeled real data from the real world, can avoid tedious and unavoidable data labeling work in supervised learning. If you want a computer to better understand the complex real world, the best way is to let the computer generate a representation of the real world in a certain way. The first thing needed to accomplish the above goals is the generative model. The most outstanding performance in generative models in recent years is the variational autoencoder and generative confrontation network introduced in this article. As an extension of the autoencoder, the former is a good combination of deep learning ideas and statistical learning. The high-dimensional distribution of the image can be reduced through the encoder network, and then the decoder network can be used to achieve low-dimensional Data distribution automatically generates an image similar to the original image. Therefore, in the subsequent improvement, the researchers took the advantages of combining the variational autoencoder and the generative confrontation network. The experimental results show that this paper has jointly trained VAE and GAN and achieved good results. However, due to the inherent shortcomings of the original generative adversarial network, the combination of the two still cannot achieve very good results.