Reviewerly/exHarmony huggingface dataset:
International Association for Gondwana Research (IAGR) 2021 annual convention and 18th international symposium on Gondwana to Asia: A report

The identification of brachiopods requires specialist knowledge held by a limited number of researchers and is very time-consuming. The new technique of deep learning by artificial intelligence offers promising tools to break these shackles to develop computer automatic identification. However, we found that the traditional convolution neural network is not sufficient to automatically identify brachiopod species. Thus, we propose a new tailored Transpose Convolutional Neural Network (TCNN) in order to automatically identify brachiopod fossils with high efficiency. In this network, we add an “upsampling” Transpose Convolutional Layer and synthesize the data of this layer with the data of a Convolutional Layer to fully mix the small and large scales features extracted by the neural network. Compared with the traditional Convolution Neural Network (CNN), the Transpose Convolutional Neural Network (TCNN) can achieve a high identification accuracy using a smaller training data set of images of brachiopods. Results from this study show that the TCNN can achieve 98%, 98% and 97% identification accuracy respectively, with training data sets of 400 images of 3 species, 484 images of 4 species and 630 images of 5 species. In contrast, the traditional CNN can achieve only a low identification accuracy (67%) with 400 images of 3 species and requires 3000 images per 3 species to achieve a 95% identification accuracy. For most of brachiopod species, it is almost an impossible task to collected thousands of samples and as more brachiopod species are fitted into automatic identification, it is significant to have a reliable network which can achieve high accuracy on a small data set. In summary, the TCNN is a more efficient neural network that could be better applied to automatically identify brachiopod fossils.
Pelatihan Menulis Karya Ilmiah Berbasis Pustaka Acuan di SMA Negeri 6 Kota Tangerang Selatan

Abstract: As an education senior High School 6 South Tangerang City is a school that has good integrity in the South Tangerang City area. This is in accordance with the vision and mission of senior High School 6 South Tangerang City that wants to be a religious, superior, creative character and environmentally friendly outcome. So to obtain superior results, need expertise or competence that is mastered by students. One of the expected competencies is to be able to write scientific papers so we provide tridarma of tertiary institutions for the community through training in writing reference-based scientific papers in senior High School 6 South Tangerang City. This kind of training is needed because now students need references to make scientific work that is a mandatory task in Indonesian language lessons. However, problems that arise related to the difficulty of getting reference sources in accordance with the specified theme. So with this training it is expected that students of senior High School 6 South Tangerang City are able to write scientific papers with various sources of reference unreadable not just copy and paste on Google
Обчислювальна техніка та програмування. Лабораторні роботи. Частина 1

The manual contains materials of 12 classes, which include theoretical information and examples of solving various problems using elements of the C # algorithmic language: branching operators, loops, arrays, etc. These materials explain the principles of working with basic data types, programming arithmetic expressions and working with text strings. For each lesson there are individual tasks for students, the implementation of which will consolidate the knowledge of the basic principles of work using a modern algorithmic programming language C #.
Morphology, Petrology and Geochemistry of Pan-African Migmatites around Liman Katagum and Bagel, Bauchi North-East Nigeria

Liman Katagum and Bagel are part of the Eastern margin of the Northern Basement Complex rocks of Bauchi Nigeria. The rocks are well exposed within the river channel in the western part of the study area, towards the east are few outcrops trending in NE-SW direction. The main aim of this research is to classify the rocks using morphological petrology and geochemical parameters. Three distinct migmatites were identified; Banded orthogniesses which are foliated coarse grained to porphyritic in texture with alternating bands of felcic rich quartzo-feldspathic and dark biotite rich bands, chemically the orthogneisses are tholeiitic with some slightly calc-alkaline showing enrichment of iron, and also calic to calc-alkaline. They also exhibit peraluminous character indicating source from S-type granitoids. The Diatexite migmatites are weakly foliated, and have granular texture, they are dominantly metaluminous to peraluminous showing mix source between I and S type granitiods, most of them are calc-alkaline showing enrichment of alumina. The anatectic granite are similar to the diatexites, only that they are more leucocratic, have more silica and k-feldspar with lesser biotite and other mafic minerals. The presence of biotite and amphibole within the banded orthogneisses is an indication that the rocks are within amphibolite facie metamorphism while the appearance of orthopyroxene within the diatexite migmatite indicates that the grade of metamorphism has increased to granulite facie metamorphism towards the eastern part of the study area.
Propuesta de una guía didáctica para padres de estudiantes de básica primaria diagnosticados con algún tipo de trastorno de aprendizaje de la institución educativa Emaús de la ciudad de Medellin- Antioquia desde escenarios virtuales.

The present article has the purpose of proposing a didactic guide for parents of elementary schoolstudents diagnosed with some type of learning disorder at the Emmaus Educational Institution in thecity of Medellin Antioquia from virtual settings. An exploratory - descriptive methodology isapproached with a qualitative approach and documentary review techniques. The results allowcorroborating that the learning disorder constitutes a set of difficulties concerning reading, writing,calculating, English, which are included under the terms of dyslexia or difficulty with reading;dyscalculia or also called difficulty with mathematics and arithmetic and dysgraphia, whose difficultylies in writing. In addition to this, it is also related to communication and language problems. Tocontribute to the solution of this problem, a learning guide was proposed based on the universal designof learning - DUA, which is a suggested method that considers diversity, both in educational practices,accessibility, and technological devices. This method seeks to generate adaptations to any type ofneed, limitations, or barriers, accessing information in a universal way.
Bayesian Distance Clustering

Model-based clustering is widely-used in a variety of application areas. However, fundamental concerns remain about robustness. In particular, results can be sensitive to the choice of kernel representing the within-cluster data density. Leveraging on properties of pairwise differences between data points, we propose a class of Bayesian distance clustering methods, which rely on modeling the likelihood of the pairwise distances in place of the original data. Although some information in the data is discarded, we gain substantial robustness to modeling assumptions. The proposed approach represents an appealing middle ground between distance- and model-based clustering, drawing advantages from each of these canonical approaches. We illustrate dramatic gains in the ability to infer clusters that are not well represented by the usual choices of kernel. A simulation study is included to assess performance relative to competitors, and we apply the approach to clustering of brain genome expression data. Keywords: Distance-based clustering; Mixture model; Model-based clustering; Model misspecification; Pairwise distance matrix; Partial likelihood; Robustness.
Using Deep Learning for Emotion Analysis of 18th and 19th Century German Plays

We present first results of the project “Emotions in Drama” in which we explore the annotation of emotions and the application of computational emotion analysis, predominantly deep learning-based methods, in the context of historical German plays of the time around 1800. We performed a pilot annotation study with five plays generating over 6,500 annotations for up to 13 sub-emotions structured in a hierarchical scheme. This emotion scheme includes common types like joy, anger or hate but also concepts that are specifically important for German literary criticism of this period like friendship, compassion or Schadenfreude. We evaluate the performance of various methods of emotion-based text sequence classification including lexicon-based methods, traditional machine learning, fastText as static word embedding, various transformer models based on BERT- or ELECTRA-architectures and pretrained with contemporary language, transformer-based methods pretrained or finetuned for historical and/or poetic language as well as the finetuning of BERT models via our own corpora and plays. We do achieve state-of-the-art results with hierarchical levels with two or three classes, i. e. the classification of valence (positive/negative). The best models are the transformer-based models gbert-large and gelectra-large by deepset pretrained on large corpora of contemporary German, which achieve accuracy values of up to 83%. Lexicon-based methods, traditional machine learning as well as static word embeddings are consistently outperformed by transformer-based models. Models trained on historical texts show small and inconsistent improvements. The performance becomes significantly smaller for settings with multiple sub-emotions like 6 or 13 due to the general challenge and class imbalances in which the models achieve 57% and 47% respectively. We discuss how we intend to continue our annotations and how to improve the prediction results via various optimization techniques in future work.
ANALISIS KEAMANAN DATA SISTEM INFORMASI DI PUSKESMASPLERET BANTUL YOGYAKART

Background: Server is a computer system providing various services which can be accessed by client computer that is connected to the network. Server is used to keep data needed client computer. Server data management system information public health center in public health center Pleret Bantul stores saves services patient data and contains public health center's report consist of public health center visiting report, work unit report, health provile report. Sinse server data uses computerization system so that the data is suspectible of broken or lost. In maintaining security data public health center Pleret Bantul assign a password as the only autentikasion the server security system. That's way public health center information and patient data have to be maintain the security system. Objective: The purpose of this research was to know the system security on management system information public health center server data in public health center Pleret Bantul. Method: This research used qualitative with the methods research descriptive. Collecting data should be conducted through primary data which was obtained by live interview way and observation, secondary data was obtained through documentation study that deals with the theme of the research. An interview was conducted by to 4 people who are the informer. Result: there was not permanent procedure system security of server data in public health center Pleret Bantul and the implementation of safety system is done y using password. Officers public health center knowledge about the security system was good but in the implementation or his application users still hasn’t spearheading it well. The attitude of the officers were still not good and less liable in the provision of a password because there was no supervision. The efforts made public health center in dealing with the security system back-up data, server the coordinator of information system management public health center, following the training care and the use of information system management public health center held by the department of health district.
AWARENESS AND EFFECT OF COVID-19 SURVIVAL PROGRAMME ON POVERTY ALLEVIATION AMONGST SELF-EMPLOYED IN AKURE-SOUTH LOCAL GOVERNMENT OF ONDO STATE IN NIGERIA

Poverty as socio-economic consequences has been worsened by COVID-19 closure measures. Even though, countries across the globe have implemented survival programme, it appears that in Nigeria, the awareness and effect of the programme is in doubt as the vulnerable group battle with survival. Equally, readings shows that extant researches concentrated on the impact of COVID-19 pandemic but scarcely consider the public awareness and effect of COVID-19 survival programme (CSP) on poverty alleviation. Therefore, the main thrust of this study is to investigate the level of awareness and the effect of CSP on poverty alleviation amongst the self-employed with reference to Akure-South Local Government (LG) of Ondo State in Nigeria. This study is rooted in Uses and gratification and Henri Fayol's management theoretical propositions. The purposive sampling technique was used to select 200 sample size from male and female in Akure-South LG and a self-structured Likert style rating scale questionnaire was distributed to elicit responses. The data gathered was analyzed using Statistical Package for Social Sciences (SPSS). This study found that the level of awareness about CSP amongst the self-employed is low in Akure-South LG. It equally found that the effect of CSP on poverty alleviation amongst the self-employed is insignificant. This paper recommends that for CSP to have significant impact on poverty, the federal government should authorize the publication of information about CSP in various LG through communication media to improve the level of awareness, numbers of beneficiaries, establish feedback mechanism to monitor the effect of CSP implementation, ascertain accountability and transparency.
Optical Encoding of Quantum Information

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Population Behavior in the Mathematical Model of the Spread of COVID19 Type SEIRS

Mathematical modeling utilized to simplify real phenomena that occur in everyday life. Mathematical modeling is popular to modeling the case of the spread of disease in an area, the growth of living things, and social behavior in everyday life and so on. This type of research is included in the study of theoretical and applied mathematics. The research steps carried out include 1) constructing a mathematical model type SEIRS, 2) analysis on the SEIRS type mathematical model by using parameter values for conditions 1and , 3) Numerical simulation to see the behavior of the population in the model, and 4) to conclude the results of the numerical simulation of the SEIRS type mathematical model. The simulation results show that the model stabilized in disease free quilibrium for the condition and stabilized in endemic equilibrium for the condition .
Students’ Perception towards Storytelling Courseware in Understanding Types of Reading Text

The purpose of this study is to design a storytelling courseware to help students in understanding types of reading text especially descriptive, narrative and procedure. Besides, this study also identify students’ perception towards storytelling courseware in understanding types of reading text. A quantitative survey research design was used in this study. The research samples consisted of 70 first year students in senior high school. The samples were taken from 2 different school. Data were gathered using a questionnaire. Descriptive statistics which are mean and standard deviation indicated that students’ perception towards the design of the courseware is quite good. The overall mean for students’ perception on achievement from storytelling courseware was quite high, which is 3.07. This study shows that multimedia courseware to enhance student acquisition on types of reading text give a very positive way in teaching and learning process. The students respond show that multimedia courseware become one of the interesting method in teaching and learning English reading.
Performance Analysis of Applying Deep Learning for Virtual Background of WebRTC-based Video Conferencing System

With the advancement of artificial intelligence(AI) technology, AI is being used in various industries such as factory automation and autonomous driving. Video conferencing systems have also added functions that use AI to overcome the limitations of existing algorithms, for example, super resolution and virtual background functions using image segmentation. However, web-based video conferencing limits the application of these features due to a limited web browser environment. In this paper, we introduce several approaches to apply deep learning in a web browser environment to provide the features that use deep learning models, and introduce image segmentation models used for virtual background functions in each method and evaluate their performance. Finally, we discuss areas that need to be considered to apply deep learning models to web-based video conferencing.
How the OA Switchboard Is Building the Infrastructure for an OA-Driven Scholarly Communications Landscape

This report from the 2021 NC Serials Conference provides information about the OA Switchboard, which was founded in 2020 following a project led by the Open Access Scholarly Publishing Association (OASPA). The OA switchboard creates a standardized messaging protocol and reporting tool between publishers, institutions, and funders.
INTEGRATED MODEL OF KNOWLEDGE MANAGEMENT PRACTICE: SURVEY ON THE MANUFACTURING INDUSTRY IN INDONESIA

The COVID-19 pandemic hit the world in the past year; almost all industries experienced a significant downturn in business. Knowledge Management Practice is considered as one of the important solutions to improve Firm Performance that has experienced a decline due to the pandemic disaster. This study aims to examine the effect of Knowledge Management Practice on organizational performance in the manufacturing industry in Indonesia. The analytical technique used in this research is partial least square (PLS-SEM). The population in this study was 75 Manufacturing Companies in the Karawang Industrial Estate, Indonesia. The intended respondent is the Manager or Supervisor in the company. The results of this study reveal that there is a positive influence of the variables studied on the company's performance.
Modeling and Analysis of Translation Ability of Applied English Talents Based on Data Mining Algorithm

In order to improve the security of modeling data of translation ability of applied English talents, a translation ability model of applied English talents based on principal component analysis and big data mining is proposed. A data storage structure feature analysis and integrated processing model for modeling data of applied English talents' translation ability is constructed. A semantic text feature coding control model for modeling data of applied English talents' translation ability is constructed by using distributed integration of semantic ontology and model base and joint control of English talents' translation. This paper constructs the password structure of principal component feature distribution of applied English talents' translation ability modeling data, realizes semantic roughness construction of applied English talents' translation ability modeling data and talents' application ability modeling according to the coding feature distribution of applied English talents' translation ability modeling protocol, and adopts improved BP neural network to realize the construction of applied English talents' translation ability model. The test shows that this method has better data security assessment ability and higher confidence level in modeling the translation ability of applied English talents, and improves the translation ability planning ability of applied English talents.
Image Re-composition via Regional Content-Style Decoupling

Typical image composition harmonizes regions from different images to a single plausible image. We extend the idea of image composition by introducing the content-style decomposition and combination to form the concept of image re-composition. In other words, our image re-composition could arbitrarily combine those contents and styles decomposed from different images to generate more diverse images in a unified framework. In the decomposition stage, we incorporate the whitening normalization to obtain a more thorough content-style decoupling, which substantially improves the re-composition results. Moreover, to handle the variation of structure and texture of different objects in an image, we design the network to support regional feature representation and achieve region-aware content-style decomposition. Regarding the composition stage, we propose a cycle consistency loss to constrain the network preserving the content and style information during the composition. Our method can produce diverse re-composition results, including content-content, content-style and style-style. Our experimental results demonstrate a large improvement over the current state-of-the-art methods.
Information needs of students under the institute electronic educational environment

The article describes the results of the study of information needs of students in the formation of the electronic educational environment of the Kazan State Institute of Culture. The study aims to solve the following problems: identify and evaluate the attitude of students to the use of new information technologies in the educational process; define the range of sources of information used by students; to evaluate the degree and effectiveness of using various elements of the electronic educational environment of the institute. The methodological base of the study was a systematic approach as a direction of the methodology of scientific knowledge, which is based on the consideration of the object of study as a system. This study uses empirical research methods, in particular survey and comparison methods, as well as analysis of publications and regulatory documents. Studying the information needs of students to optimize their information support system, intensify their activities in mastering the basic educational program and improve the quality of training of future specialists.
A Multi-view Recognition Technology Based on the Prediction of Active Robot’s Optimal Viewing Angle

In task of multi-robots exploring unknown environments, the collaborative information between them can be used to improve the accuracy of object recognition. This paper proposes and implements a multi-view active learning model, which uses images obtained from different perspectives to improve the performance of target detection and recognition. Using robot clusters to obtain multi-view information of the target, through the promotion of the target detection network based on deep learning, the algorithm based on multi-view learning comprehensively utilizes the complementarity of multi-view information, combined with the initiative of multiple robots to estimate the best perspective, Constantly adjust the best observation angle of the cluster to improve the accuracy of target detection. The final experiment shows that the recognition rate of the active multi-view recognition technology proposed in this paper is 12% higher than that of a single view.