Reviewerly/exHarmony huggingface dataset:
PENGARUH PENGGUNAAN BLENDED LEARNING BERBASIS SCHOOLOGY TERHADAP HASIL BELAJAR PESERTA DIDIK PADA MATERI HIDROLISIS GARAM

This research aimed at increasing student learning achievement on Ion and pH Saline Solution Equilibrium lesson by using Schoology blanded Learninglearning model. Qualitative method was used in this research with quasi- experimental research design. All the eleventh-grade students of Natural Science of State Senior High School 1 TapungHilir in the Academic Year of 2018/2019 were the population of this research, and the samples were the eleventh-grade students of Natural Science 1 as the experimental group and the students of Natural Science 2 as the control group. Pretest-posttest control group design was used in this research. Sampling was done by using random technique. Analyzing the data was using one-way ANOVA. The data were analyzed by using inferential statistic with the help of SPSS 16.0 software. The results of calculating the data showed that the score of Sig. was lower than α, the significance score was 0.003. It showed that the learning achievement mean on Ion and pH Saline Solution Equilibrium lesson was increasing through the use of Schoology based Blended Learning. It could be concluded that there was an effect of using Schoology- based Blended Learning toward student learning achievement on Ion and pH Saline Solution Equilibrium lesson.
KONTRIBUSI LINGKUNGAN KELUARGA DAN CARA BELAJAR TERHADAP HASIL BELAJAR SISWA KELAS X

This research is motivated by the problem of low learning outcomes in the Basic Electricity and Electronics subject at SMK Negeri 1 Sumbar. The purpose of the study was to reveal how much the contribution of the family environment and how to learn about learning outcomes. The research method used in this study is descriptive correlational. The study population 96 people and the sample 49 people. The research sample was determined using simple random sampling technique. The results of data analysis showed that (1) the family environment and the way of learning of students together contributed significantly to student learning outcomes by 37.99%. (2) family environment significantly contributes to student learning outcomes by 23.88%, (3) student learning methods contribute significantly to student learning outcomes by 26.39%. So it can be concluded that he better the family environment and effective and efficient ways of student learning, the higher the learning outcomes. Keywords: Student team achievent division, learning models, learning outcomes, experiment, control
MIND MAPPING MATA PELAJARAN MATEMATIKA UNTUK KELAS IX SEMESTER GANJIL

The study s a qualitative descriptive approach, and his research type is the study of library studies. Researchers aim to project sub-sub materials through the mathematical mind mapping of major semester ix classes, researchers are trying to collect data by reading, studying understanding and analyzing books as well as research journals, accessing Internet sites, or documents related to mind mapping. Mind mapping is the most effective and efficient way to input, store and extract data from the brain or into the brain. It works according to the natural workings of our brain, thus optimizing all the human brain's potential and capacity. The manufacturing of the mind mapping process requires that a number of basic characteristics are attained. There is also the basic characteristic; Paper, colour, line, letter, keyword, key image, and structure. Thus basically the mind mapping process is based on the development of the mind mapping process, resulting in a mind mapping that can help teachers and students in the mathematical process during the difficult part of the semester, and can make learning more meaningful and enjoyable because the learning material is visible to the whole so that students can readily understand the material given.
Modeling Expert Knowledge in a Heuristic-Based Gin Rummy Agent

We developed a heuristic-based reflex agent, Tonic, for the EAAI 2021 Undergraduate Research Challenge, which tasks competitors to create an autonomous player to play the card game gin rummy. Tonic's heuristics originate in expert knowledge and inform decision making for the three actions comprising a turn: drawing a card, discarding a card, and deciding when to knock. However, because these strategies are based in human intuition, there is often a lack of specificity to directly model them as algorithms. We developed parameterized models describing that intuition based on factors such as the number of turns played and an estimation of the opponent hand. To hone their performance, we conducted both manual analysis and parameter optimization (grid search) using self-play and play against a simple baseline agent. These heuristic models enable Tonic to win against the baseline agent at least 68% of the time.
ARTIFICIAL INTELLIGENCE AND PATHOLOGY

The microscopic diagnosis of cancer remains challenging. Recent data from our group within the French (nationwide) LymphoPath network shows that 20% of diagnoses are inaccurate, with direct impact on patient care. Indeed, molecular techniques, that are not affordable for all pathology departments, have become critical for the final diagnosis. Currently, automated solutions that could help pathologists with diagnostic decisions or histological grading are lacking, and it is not reasonable to expect pathologists to be experts on rare tumours if they only see a few cases per year. Digital microscopy offers unique features which are not available in conventional optical microscopy. Assisted by dedicated software tools, digital microscopy enables dynamic and prompt access to any detail of the stained slides at several microscopic magnifications. Furthermore, the calibrated qualities and the number of discrete pixels in a digital slide allow automated image analysis and quantification by using computer vision and, in particular, deep learning approaches. Image feature extraction methods based on pixel detection or less often on object segmentation has brought much hope in improving the accuracy of the human eye. Automatic analysis of cancer whole-slide images has recently been performed to allow experts for predicting tumour classification, gene mutations and survival outcomes. However, the positioning of such technologies in routine practice is limited by the trained networks which frequently do not meet industrial constraints required for a general application such as certification, qualification and explainability (black box effect) of algorithms. This presentation will describe different approaches of machine learning aiming at classifying cancer subtypes through the analysis of histopathologic features. Advantages and drawbacks of these techniques will be discussed with a special emphasis on the risk of biased performance assessment of deep learning systems. The critical role of data sets quality will be discussed and different strategies will be envisaged to broaden the use deep neural networks. Providing that these limitations are taken into account and circumvented, it seems that artificial intelligence solutions dedicated to cancer histopathology should pave the road to precision medicine through data integration into a holistic patient dashboard for oncology care teams. Keywords: Bioinformatics; Computational and Systems Biology, Genomics, Epigenomics, and Other -Omics, Tumor Biology and Heterogeneity Conflicts of interests pertinent to the abstract P. Brousset Consultant or advisory role: Roche, MSB, Janssen Cilag laboratory Research funding: Roche, Pierre Fabre
PROBLEMS AND PROSPECTS OF ONLINE EDUCATION IN HIGHER EDUCATIONAL INSTITUTIONS

2614.5 Problems and prospects of online education in higher educational institutions Distance learning involves the interaction of real subjects of education in a virtual environment using special means of communication.The specificity of the interaction environment and the peculiarities of the organization of educational information in the absence of direct contact between the student and the teacher determine the psychological and pedagogical features of distance learning.This theme is very topical nowadays.And many scientists have already studied the topic: Berg, Gary, Stygalov Yu.M., Bayeva L. V. and many more.It brought lot's of challenges and opportunities.It proved to be ineffective when trying to apply the offline means of teaching to on-line education.Our goal is to derive the best and most productive ways of education.Educational process is very versatile and thus needs to be seen from different positions:
Monitoring System Based on VIN Recognition

With the increase of motor vehicles, more and more traﬀic violations such as obscuring the license plate, decks, and other means to evade punishment of crimes has also increased, which have seri- ously endangered public safety. In response to this phenomenon, this article combines target detection, text recognition, target tracking and other technologies to develop a vehicle monitoring system based on VIN recognition. By collecting and recognizing the VIN characters under the front windshield and comparing with the information in the database, the deck vehicles can be found in time, which will provide furtherpro- tection for public traﬀic safety.
Technical Perspective for

When a data analyst runs some query to analyze her data, she often wants to ask some follow-up questions, about the result of the query. Why-questions take many shapes, and occur in many scenarios. Why is a particular tuple in the answer? Why is it not in the answer? Why is this graph decreasing? Why did we observe a sudden burst of error messages in online monitoring? Database researchers have noted the need for why-questions, and the literature contains several approaches, mostly tailored to specific applications. Despite the interest and the work in this area, there is currently no consensus of what an explanation to a query answer should be, and how one should compute it.
An Internationally Fair Mediated Digital Discourse Corpus

In this paper, the authors present a French Mediated Digital Discourse corpus, (88milSMS http://88milsms.huma-num.fr https://hdl.handle.net/11403/comere/cmr-88milsms). Efforts were undertaken over the years to ensure its publication according to the best practices and standards of the community, thus guaranteeing compliance with FAIR principles and CLARIN recommendations with pertinent scientific and pedagogical reuse. Since knowledge on how resources are reused is sometimes difficult to obtain, ways of improving this are also envisaged.
Pelatihan Penulisan Judul Dan Introduction Dalam Karya Ilmiah Bagi Mahasiswa

Scientific work is the result of writing that presents opinions, observations, reviews and research in a particular field by paying attention to norms and ethics for writers. participants in determining the title and making the introduction properly and correctly according to the guidelines for writing scientific papers. The target audience in the service activities are students in the Faculty of Economics, especially the Accounting Study Program who will be in charge of the proposal proposal seminar. The methods used in this training activity are as follows: Lecture method, discussion method, and exercises. Evaluation in this activity is carried out in three stages, namely the activity planning stage, the stage during the activity process, and the final stage of the activity. The results of this activity provide great benefits for the training participants who in this case are students in the process of making scientific papers starting from determining the problem, formulating titles, writing introductions as well as references and research data, so that scientific works produced by students become quality scientific work.
Marginal Utility Diminishes: Exploring the Minimum Knowledge for BERT Knowledge Distillation

Recently, knowledge distillation (KD) has shown great success in BERT compression.Instead of only learning from the teacher's soft label as in conventional KD, researchers find that the rich information contained in the hidden layers of BERT is conducive to the student's performance.To better exploit the hidden knowledge, a common practice is to force the student to deeply mimic the teacher's hidden states of all the tokens in a layer-wise manner.In this paper, however, we observe that although distilling the teacher's hidden state knowledge (HSK) is helpful, the performance gain (marginal utility) diminishes quickly as more HSK is distilled.To understand this effect, we conduct a series of analysis.Specifically, we divide the HSK of BERT into three dimensions, namely depth, length and width.We first investigate a variety of strategies to extract crucial knowledge for each single dimension and then jointly compress the three dimensions.In this way, we show that 1) the student's performance can be improved by extracting and distilling the crucial HSK, and 2) using a tiny fraction of HSK can achieve the same performance as extensive HSK distillation.Based on the second finding, we further propose an efficient KD paradigm to compress BERT, which does not require loading the teacher during the training of student.For two kinds of student models and computing devices, the proposed KD paradigm gives rise to training speedup of 2.7× ∼3.4×.
How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements

Humor is an important social phenomenon, serving complex social and psychological functions.However, despite being studied for millennia humor is computationally not well understood, often considered an AI-complete problem.In this work, we introduce a novel setting in humor mining: automatically detecting funny and unusual scientific papers.We are inspired by the Ig Nobel prize, a satirical prize awarded annually to celebrate funny scientific achievements (example past winner: "Are cows more likely to lie down the longer they stand?").This challenging task has unique characteristics that make it particularly suitable for automatic learning.We construct a dataset containing thousands of funny papers and use it to learn classifiers, combining findings from psychology and linguistics with recent advances in NLP.We use our models to identify potentially funny papers in a large dataset of over 630,000 articles.The results demonstrate the potential of our methods, and more broadly the utility of integrating state-of-the-art NLP methods with insights from more traditional disciplines.
DETERMINANTS OF HOUSEHOLD’S POVERTY AMONG CROP FARMERS IN KADUNA STATE, NIGERIA

This study examined the households’ poverty status among crop farmers in Kaduna State. Multi-stage sampling procedure was used to select 242 farmers for this study. Data were collected through using structured questionnaire with the help of trained enumerators. Descriptive and inferential statistics such as tobit regression and Foster, Greer and Thorbecke (FGT) model was employed in this study. The results showed that the mean age of the respondents was 45 years and were married, mean household size of the farmers was 11 persons, and about (80.6%) had formal education. The poverty situation of the farmers revealed that 51% were poor and the strategies adopted by the farmers to combat poverty were mostly through diversification into commercial farming, increased personal savings and involvement in non-farming activities. Tobit regression analysis revealed that age, household and income negatively influences the poverty status of the farmers, statistically significant at 1%, this implied that, poverty is concentrated among households in the study area. Major constraints encountered by the crop farmers, include high cost of inputs, inadequate credit facilities and inadequate market linkages. In conclusion, poverty incidence had a negative and significant effect on rural crop farmers. It was recommended that farmers should form cooperative societies to access micro loan; farmer should be linked to the urban market through extension agent and media to attract good value for their crops. Government and Non-Governmental Organizations (NGOs) should make farm inputs readily available at a subsidized rate
Possibilities Of Dynamic Assessment Of Student Engagement In Online Learning

The COVID-19 epidemic has intensified the use of online technologies in human communication around the world.In higher education, online learning has become one of the main types of learning.Although technology (Microsoft Teams, Zoom, etc.) for implementing this method of learning is created and has high functionality, nevertheless, a full-fledged transition to online education requires the formation of the relevant competencies for both the teacher and the student.An important aspect of online education that ensures its effectiveness is student's engagement in educational activities.The concept of engagement is well developed in pedagogical and psychological sciences.Engagement can be studied at different scales of educational activity, has a variety of types and characteristics that affect the features of the educational process.The article substantiates the possibility of studying short-term (situational) manifestations of engagement based on the analysis of dynamic aspects of students' non-verbal behavior.As noted in numerous studies in the field of social psychology, active group interaction leads to synchronicity of verbal and nonverbal behavioral manifestations of group members.In learning activities, engagement also manifests itself as increased synchronicity and cooperation in students' and teachers' behavior.Computer vision and artificial intelligence allow for a constant dynamic assessment of student engagement and reflect its indicators on the teacher's computer screen.Using this assessment as a feedback form will improve the quality of learning in digital environment.
Inverse problems with the digital image correlation: approach and applications

A viable approach to solve inverse problems in elasticity is proposed. It is based on regression algorithms to estimate materials and/or loading parameters by fitting the experimentally-evaluated displacement field to representative analytical solutions. Displacements are measured by the digital image correlation (DIC) technique and they are used as input for numerical procedures able to minimize the estimation errors of the unknowns and to quantify the unavoidable rigid body motions of the samples/components. In addition, thanks to ad-hoc developed iterative algorithms, non-linear phenomena related to high and localized stress/strain states, can be captured successfully. This latter represents a relevant novelty of the methodology as it allows to investigate plasticity-induced mechanisms in solid mechanics which are impossible to analyze with more traditional DIC-based approaches. Three different case studies are considered: 1) estimation of the stress intensity factor in fracture mechanics problems, 2) estimation of the elastic properties of a material by the Brazilian tests, 3) estimation of the contact pressure generated by thermally activated shape memory alloy (SMA) rings used for pipe coupling. The reliability and the accuracy of the method is demonstrated through systematic comparisons of the results with conventional techniques in experimental mechanics.
Analisa Kepuasan Pelanggan Menggunakan Klasifikasi Data Mining

The research objective was to obtain a model of rules in classifying the level of customer satisfaction at Indiis Cafe Pematangsiantar. By knowing the level of customer satisfaction, the owner of Indiis Cafe can improve the service if it is not good and further improve the service when the level of satisfaction is good. This study measures the level of customer satisfaction at Indiis Cafe Pematangsiantar. The method used in this study is the C4.5 Algorithm, where the data source used is a questionnaire / questionnaire given to customers at Indiis Cafe Pematangsiantar. The variables used include (1) Product, (2) Price, (3) Place (4) Promotion and (5) Service. The results of the study obtained 15 rules for the classification of customer satisfaction levels with 8 satisfied status rules and 7 unsatisfied rules. The C4.5 algorithm can be used in the case of customer satisfaction levels with an accuracy rate of 98.00%. From the results of the analysis is expected to be applied so that it can be used as a decision to improve service to customers.
Transform and statistical parameter-based image noise level prediction

Noise level estimation in an image is important and useful in many image processing algorithms such as image denoising, image segmentation, and image compression. Accurately estimating the noise level without prior knowledge of the image is the major challenge of today’s research. We present an improved patch-based fast noise level estimation using DCT and standard deviation method for fast and reliable noise level estimation and the result is compared with the available state-of-art methods. Experimental result shows the proposed method provides greater accuracy, the stability and also the proposed method is an average of six times faster than that of the state- of – art methods for noise level estimation.
Mandarin Language Students Illocutionary Acts in Japanese Language Learning at the University of Sumatera Utara

Speech acts play an essential role in language learning as an aspect that can improve a learner's ability to speak orally. In this research, illocutionary speech-acts were described in the Japanese language learning process for students of the Mandarin Language Department, Faculty of Cultural Sciences, University of Sumatera Utara. This research is qualitative. The data are speech acts in the lecture process in class, especially in the discussion. The data sources were 40 fourth-semester students of the Mandarin Language Department. The theory used was the Searle speech act theory. Data collection techniques were carried out through observation, recording , and note-taking techniques. Data analysis techniques were carried out applying Miles &amp; Huberman's opinion by condensing data through transcription of recorded data into written form, identifying forms, speech act functions, data presentation, and concluding. As a result, there were forms of illocutionary speech acts, namely directive, expressive, and declarative. Furthermore, the purpose used in class was in directive speech acts which were to order, ask, invite (engage). In other words, in expressive speech-act was praising, and in declarative speech-act was prohibiting. In conclusion, Japanese speech acts in Japanese class occured between lecturers and students and only consisted of several types of speech acts due to students' limited mastery of Japanese.
THE BODY IN THE HISTORY OF WESTERN EUROPEAN AND RUSSIAN THEATRE: CULTURAL VARIABILITY OF SHAME

Modern scientific knowledge approaches the study of the physical and aesthetic bodies with a considerable body of texts. However, on the territory of the theater, the body is still considered exclusively from the point of view of the actor's artistic tools. Theatrical physicality and the character of physical empathy in the theater are not limited to the boundaries of the performing arts, but exist in close relationship with the visual and empirical experience of the spectator, performer, and director. The aesthetic and ethical aspect of the attitude to the body in the history of theatrical art has repeatedly changed, including under the influence of changing cultural criteria of "shameful". The culmination of the demarcation of theatrical shame, it would seem, should be an act of pure art, independent of the moral restrictions of society. However, the experiments of modern theater continue to face archaic ethical views. The article attempts to understand the cultural variability of such a phenomenon as shame in its historical and cultural extent using examples from theater art from antiquity to the present day.