Reviewerly/exHarmony huggingface dataset:
Lithological mapping using remote sensing techniques: A case study of Alagbayan area, Dornogobi province, Mongolia

This study investigated the multispectral remote sensing techniques including ASTER, Landsat 8 OLI, and Sentinel 2A data in order to distinguish different lithological units in the Alagbayan area of Dornogobi province, Mongolia. Therefore, Principal component analysis (PCA), Band ratio (BR), and Support Vector Machine (SVM), which are widely used image enhancement methods, have been applied to the satellite images for lithological mapping. The result of supervised classification shows that Landsat data gives a better classification with an overall accuracy of 93.43% and a kappa coefficient of 0.92 when the former geologic map and thin section analysis were chosen as a reference for training samples. Moreover, band ratios of ((band 7 + band 9)/band 8) obtained from ASTER corresponds well with carbonate rocks. According to PCs, PC4, PC3 and PC2 in the RGB of Landsat, PC3, PC2, PC6 for ASTER data are chosen as a good indicator for different lithological units where Silurian, Carboniferous, Jurassic, and Cretaceous formations are easily distinguished. In terms of Landsat images, the most efficient BR was a ratio where BRs of 5/4 for alluvium, 4/7 for schist and 7/6 to discriminate granite. In addition, as a result of BR as well as PCA, Precambrian Khutag-Uul metamorphic complex and Norovzeeg formation can be identified but granite-gneiss and schist have not given satisfactory results.
USE OF TOOLS AND SERVICES OF CLOUD-BASED SYSTEMS IN TEACHERS’ TRAINING AND PROFESSIONAL DEVELOPMENT: ASPECT OF AUGMENTED REALITY

The article reveals the aspect of using augmented reality (AR) as a means of a cloud-based open education and science system. Research methods: analysis, generalization, systematization of scientific-methodical sources on the research problem, analysis of cloud-based tools and services, determination of theoretical bases of teacher professional development on using cloud-oriented systems of open education and science. In the context of open science, the interest of scientists in the use of ICT during the period of the large-scale COVID-19 pandemic was analyzed and it was found that the number of publications in the areas of use of information and communication technologies, the introduction of distance learning, the development of information and communication and STEM competence has increased rapidly. The survey of lyceums teachers resulted that teachers chose three simple means for organizing distance learning, as well as the direct dependence in the choice of these means – digital analogs of full-time education (Zoom – lesson, Viber – diary, website – school announcements). The author considered the issue of the lack of high-quality digital content and the use of AR-objects, implemented using the MERGE Cube technology, in pedagogical practice. The cloud-based system of using MERGE Cube for learning is described, the stages of using MERGE Cube in the educational process, the criteria for the quality of AR-content (correspondence of virtual additions to the content of the lesson or the topic being studied; sound effects emphasize artistic or other meaning; video fragments demonstrate processes, events or video instructions by content; the process of playing AR objects is simple, intuitive; the process of playing AR is technically stable; the text font is dynamic; 3D images are clear; AR objects are reproduced in various operating systems). The author draws attention to the need to develop instructions and digital maps for the lesson. It was found that the key factor in the spread of AR technology in education is open access to educational content and the development of teachers’ relevant competencies.
Multipath Cross Graph Convolution for Knowledge Representation Learning

In the past, most of the entity prediction methods based on embedding lacked the training of local core relationships, resulting in a deficiency in the end-to-end training. Aiming at this problem, we propose an end-to-end knowledge graph embedding representation method. It involves local graph convolution and global cross learning in this paper, which is called the TransC graph convolutional network (TransC-GCN). Firstly, multiple local semantic spaces are divided according to the largest neighbor. Secondly, a translation model is used to map the local entities and relationships into a cross vector, which serves as the input of GCN. Thirdly, through training and learning of local semantic relations, the best entities and strongest relations are found. The optimal entity relation combination ranking is obtained by evaluating the posterior loss function based on the mutual information entropy. Experiments show that this paper can obtain local entity feature information more accurately through the convolution operation of the lightweight convolutional neural network. Also, the maximum pooling operation helps to grasp the strong signal on the local feature, thereby avoiding the globally redundant feature. Compared with the mainstream triad prediction baseline model, the proposed algorithm can effectively reduce the computational complexity while achieving strong robustness. It also increases the inference accuracy of entities and relations by 8.1% and 4.4%, respectively. In short, this new method can not only effectively extract the local nodes and relationship features of the knowledge graph but also satisfy the requirements of multilayer penetration and relationship derivation of a knowledge graph.
Looking for a Bookstore in Town: Intellectual Readers and the Death of “Gutenberg Galaxy”

The author analyses the problems of erosion of the book culture and the role of bookishness in the contemporary Western and Russian identities. While analysing the processes of disappearance and displacement of bookshops, the author presumes that culture of bookstores and communication subcultures in them cannot compete with networks and e-commerce. It is assumed that the logic of capitalism favours the progress of on-line bookstores, specialising in the serial and mass literature while independent bookstores prefer to sell intellectual, non-fiction, and academic books that are not interesting to consumer readers of mass culture. The author tries to analyse causes of private non-mass bookstores crisis. The author believes that intellectuals of 2000s were optimistic in their prognosis for the development of bookstores as spaces of cultural initiatives. By the end of 2020, due to the coronavirus pandemic, the number of independent bookstores decreased significantly when on-line bookstores occupied their place. It is assumed that the cultures of reading, book collections, personal libraries lost the positions they held in the 20th century and even in the first decade of the 21st century. The author presumes that independent bookstores became cultural ghettos and intellectual reservations, when net bookstores became successful actors of the mass culture. In general, it is predicted that heterogeneous, regionally localised minority book cultures and reading strategies of the New Medievalism may replace the “mass” book as a cultural institution of a modern political imagined communities as elements of the dying Gutenberg Galaxy with its heterogeneous national identities.
Data Entry Form Designing Tools and Software Usability in DHIS2

The advent of configurable software has shifted the implementation of software solutions from total reliance on software developers to towards increased participation of end-users. End-users are now able to create software solutions without the need for writing code but through configuration and customisation. Despite the increasing use of configurable software challenges on designing the software platform architecture, process of testing and usability exists in configurable software. The research aimed at evaluating how available interface elements influence usability in DHIS2. Empirical data was collected by studying the design of custom data collection forms for routine health data collection with two groups of users. 80% and 90% were recorded as completion rates of the designed task and overall efficiency of 86.23% and 89.94% was achieved between the two groups. Lack of relevant editing features, increased distance between related objects, lack of conformity to Keep It Simple, Stupid (KISS) and minimalistic design principle were found to be the major challenges affecting the usability.
Design of an OCR System and its Hardware Implementation

Abstract: In this paper, we present a scheme to develop to complete OCR system for printed text English Alphabet of Uppercase of different font and of different sizes so that we can use this system in Banking, Corporate, Legal industry and so on. OCR system consists of different modules like preprocessing, segmentation, feature extraction and recognition. In preprocessing step it is expected to include image gray level conversion, binary conversion etc. After finding out the feature of the segmented characters artificial neural network and can be used for Character Recognition purpose. Efforts have been made to improve the performance of character recognition using artificial neural network techniques. The proposed OCR system is capable of accepting printed document images from a file and implemented using MATLAB R2014a version. Key words: OCR, Printed text, Barcode recognition
Incremental Decision Rules Algorithm: A Probabilistic and Dynamic Approach to Decisional Data Stream Problems

Data science is currently one of the most promising fields used to support the decision-making process. Particularly, data streams can give these supportive systems an updated base of knowledge that allows experts to make decisions with updated models. Incremental Decision Rules Algorithm (IDRA) proposes a new incremental decision-rule method based on the classical ID3 approach to generating and updating a rule set. This algorithm is a novel approach designed to fit a Decision Support System (DSS) whose motivation is to give accurate responses in an affordable time for a decision situation. This work includes several experiments that compare IDRA with the classical static but optimized ID3 (CREA) and the adaptive method VFDR. A battery of scenarios with different error types and rates are proposed to compare these three algorithms. IDRA improves the accuracies of VFDR and CREA in most common cases for the simulated data streams used in this work. In particular, the proposed technique has proven to perform better in those scenarios with no error, low noise, or high-impact concept drifts.
Error Analysis on the Malaysian Students’ Writing for ESL Classroom

English has been regarded as one of the core subjects in the Malaysian Certificate of Education or known as the Sijil Pelajaran Malaysia (SPM). There has been a concern over the writing components in English language papers at the SPM level. Writing requires students’ competence in syntax, coherence, developing and arranging ideas, mechanics in writing and appropriate use of vocabulary. A concern over students who produce very limited number of words in their essays has resulted in the lack of content to be assessed which then led to low scores. This paper presents an error analysis focusing on the types of errors found in students’ written test scripts to identify the weaknesses of students' writing abilities. The study adopts a qualitative approach where the students’ written assessment texts were collected. A total of 18 upper secondary Year Four students (aged 16 years old) were selected as the research participants. The selection of participants was carried out in a secondary school in one of the states in Malaysia. The students’ written tests were analysed to identify the emerging categories of language use, focusing on language and grammar inaccuracies. It is found that students have the idea on how to write the answers to the task but seem to have difficulties putting the ideas in the correct structure.
Cluster Analysis on Covid-19 Outbreak Sentiments from Twitter Data using K-means Algorithm

With the remarkable advances on the Internet and recent computer technologies, social media has become prominent platforms to share opinions.Nowadays, COVID-19 is considered as one of the major crises in the world.People use social media to express their thoughts about COVID-19 and actions that have been taken to control it.There is an immense need to discover and understand the public sentiment related to COVID-19 to give better insights for the decision makers and governments in making accurate decisions.In regard to this interest, several researches have explored COVID-19 outbreak sentiment analysis, however most of these studies have used classification approach which require the data to be manually labelled.In analyzing large number of data, labelling process can be an intricate task and expert dependent.This study aims to explore COVID-19 pandemic sentiment by using clustering approach.The data is obtained by crawling COVID-19 related posts from Twitter.The crawled data is pre-processed, and terms are extracted by using Term Frequency-Inverse Document Frequency (TF-IDF) technique.Singular Value Decomposition (SVD) technique is then used to reduce irrelevant features.K-means algorithm is employed to cluster the tweets into k clusters.The results of each cluster are plotted using t-Distributed Stochastic Neighbour Embedding (t-SNE) technique and lexicon-based sentiment analysis has been applied to discover sentiments of these clusters.The results showed relatively 9 clusters were obtained with different topics ranging highest score of 83.25% positivity and 16.75% of negativity are reported.Dominant topics are explored using word cloud and the clustering results have been evaluated with 0.0070 Silhouette coefficient.In future, this study suggests in using other word embedding technique as a data representation to deal with sparsity and high dimensionality of textual data.
A Deep Inference System for Differential Linear Logic

Differential linear logic (DiLL) provides a fine analysis of resource consumption in cut-elimination.We investigate the subsystem of DiLL without promotion in a deep inference formalism, where cuts are at an atomic level.In our system every provable formula admits a derivation in normal form, via a normalization procedure that commutes with the translation from sequent calculus to deep inference.
IMPACT OF JUSTICE IN EMPLOYEE PERFORMANCE AND MODERATING ROLE OF ARTIFICIAL INTELLIGENCE: A CONCEPTUAL STUDY

This study investigates the impact of justice’s dimensions: distributive justice, procedural justice, interactional justice on performance appraisal satisfaction, and artificial intelligence utilisation to improve employee performance in Penang multinational companies (MNCs). They are grounded with Organizational Justice Theory as the theoretical framework to examine the relationship between Artificial Intelligence utilisation in enhancing justice to improve employee performance. In conducting this study, the required data will be collected through a closed-ended structured questionnaire in Penang, Malaysia. The questionnaire was adopted and adapted from many related studies. The data collection approach for this research is based on internet-based technologies such as online platforms. This paper proposes ways of managing employee performance by boosting performance appraisal satisfaction based on a practical performance appraisal that is relevant and unbiased. The paper will provide novelty into justice in performance appraisal through artificial intelligence that is pivotal to the employee performance optimisation in Penang multinational companies. The paper will help organisations recognise the significance of enhancing the performance appraisal as a practical strategic approach to integrate human resource activities with business policies and provide a better understanding of the impact of justice to the performance appraisal in the Penang multinational companies (MNCs).
A Long Texts Summarization Approach to Scientific Articles

Automatic text summarization aims at condensing the contents of a text into a simple and descriptive summary. Summarization techniques drastically benefited from the recent advances in Deep Learning. Nevertheless, these techniques are still unable to properly deal with long texts. In this work, we investigate whether the combination of summaries extracted from multiple sections of long scientific texts may enhance the quality of the summary for the whole document. We conduct experiments on a real world corpus to assess the effectiveness of our proposal. The results show that our multi-section proposal is as good as summaries generated using the entire text as input and twice as good as single section.
Fast and scalable classical machine-learning algorithm with similar performance to quantum circuit learning

The application of near-term quantum devices to machine learning (ML) has attracted much attention. Recently, Mitarai et al. [Phys. Rev. A 98, 032309 (2018)] proposed a framework to use a quantum circuit for ML tasks, called quantum circuit learning (QCL). Due to the use of a quantum circuit, QCL employs an exponentially high-dimensional Hilbert space as its feature space. However, its efficiency compared to classical algorithms remains unexplored. Here, we present a classical ML algorithm that uses the same Hilbert space. In numerical simulations, our algorithm demonstrates similar performance to QCL for several ML tasks, providing a perspective for the computational and memory efficiency of quantum ML algorithms.
La comunicazione di Matteo Salvini durante la pandemia: l’immigrazione e la delegittimazione dell’avversario politico

Following the critical discourse analysis approach, this article intends to highlight how the anti-immigration perspective is (re)produced within the Facebook page of the Italian political leader Matteo Salvini during the pandemic scenario between March 2020–March 2021. Quantitative and qualitative analysis have been applied to Salvini’s posts and users’ comments aiming at identifying the linguistic strategies that contribute to instrumentalizing the emergency and aim to reinforce the process of “securitization” of national borders as well as the re-legitimation of national identities. Findings suggest that the main discursive strategies used by the political leader do not include migrants as a danger for the spread of the virus. Rather, Salvini systematically organized the migratory narration on negative campaigning blaming political opponents and recontextualized the moralization of borders. The contribution helps to reveal how the anti-migration discourse is reproduced during the COVID-19 outbreak and how the politicization of the migration serves as a context for the normalization of migrant’s exclusion.
Analysis of Early Reading Ability of Children in Group B of Kindergarten Tut Wuri Handayani Samahani Aceh Besar

Reading is an activity of following a series of writings arranged in a linear manner. Experience is an important aspect of the reading process. Children who have a lot of experience will have a wider opportunity in developing an understanding of vocabulary in reading. The formulation of the problem in this study, what activities are done in training beginning reading skills in group B in TK Tut Wuri Handayani Samahani Aceh Besar. How is the description of beginning reading ability in group B in TK Tut Wuri Handayani Samahani Aceh Besar. What are the obstacles faced by teachers in teaching beginning reading skills to group B children in TK Tut Wuri Handayani Samahani Aceh Besar. The purpose of this study is to see a picture of the activities given to children in group B in TK Tut Wuri Handayani Samahani Aceh Besar. To see the beginning reading ability in group B children in TK Tut Wuri Handayani Samahani Aceh Besar. To find out the obstacles faced by teachers on the ability to read beginning in group B children in TK Tut Wuri Handayani Samahani Aceh Besar. The type of research is Qualitative research The instrument used is an interview, the sample size is 1 principal and 2 classroom teachers. The results show that beginning reading is very important for early childhood which will be the foundation of the beginning of reading. Teachers who teach at TK Tut Wuri Handayani Aceh Besar, are very understanding about the development of children's reading and also teachers at TK Tut Wuri Handayani Aceh Besar always encourage children by providing creative media that can attract children's attention in beginning reading so that teachers provide reading learning start by using a system of play while learning.
Morphogenesis Analysis for Digital Image Production with L-System

The process of forming an image requires a correct color composition, location and distance between the lines to produce a good image. Human abilities in both creativity and high imagination are very limited, especially in forming new images by utilizing existing image patterns or images that resemble old images. Here we showed the implementation of L-System to generate new image generations with additional flame as a fire effect/glow on images for image transformation. This research used the L-System algorithm, Iterated Function System, and Voronoi Diagram to improve the result of image transformation. The results of this study indicated that mathematical calculations can be applied in the formation of images and the resulting images can be abstract and symmetrical. The next generation of images produced in this research can be in unlimited numbers as the generation of morphogenesis processes. The process of generating images is carried out randomly by merging the two existing images with morphogenesis analogy. The resulting images can be exported into jpg, png, and svg formats. Furthermore, this research showed that the implementation of the calculation for the variation reach the value of 99.48% while the image variation composition has a value of 99.29%.
Experimental Characterization of Fault-Tolerant Circuits in Small-Scale Quantum Processors

Experiments conducted on open-access cloud-based IBM Quantum devices are presented for characterizing their fault tolerance using [4,2,2]-encoded gate sequences. Up to 100 logical gates are activated in the Ibmq_Bogota and Ibmq_Santiago devices and we found that a [4,2,2] code’s logical gate set may be deemed fault-tolerant for gate sequences larger than 10 gates. However, certain circuits did not satisfy the fault tolerance criterion. In some cases the encoded-gate sequences show a high error rate that is lower bounded at ≈ 0.1, whereby the error inherent in these circuits cannot be mitigated by classical post-selection. A comparison of the experimental results to a simple error model reveal that the dominant gate errors cannot be readily represented by the popular Pauli error model. Finally, it is most accurate to assess the fault tolerance criterion when the circuits tested are restricted to those that give rise to an output state with a low dimension.