Reviewerly/exHarmony huggingface dataset:
Styles of business communication: space of activity and individual professional realization. Part 2

The research deals with styles of business communication, which appears to be a topical issue nowadays. Its topicality correlates with the current needs and trends in the sphere of professional activity: in personnel selection the focus is on the ability to solve specific problems, not just on the profession; orientation is not towards an individual person but towards joint activity in a unique environment; there is an emphasis on complex assessment and employment of the resources, not of separate qualities of a person, etc. The purpose of the present study is a comparative analysis of business communication styles in the samples of penal colony employees, hospital nurses and managers. The subject of research is business communication styles of persons engaged in socionomic professions. Hypotheses of the study are as follows: 1) Styles of business communication are determined by the conditions of the environment; 2) In each particular environment, there are assumed to be some style niches, within the boundaries of which a person chooses certain patterns of behavior. Research methods: expert survey, psychodiagnostics (test questionnaires «Coping behavior in stressful situations» and «Questionnaire for ways of coping» by T. Kryukova, test questionnaires 16–PF by R.B. Cattell, questionnaire «Level of subjective control»); research technique — questionnaire «Styles of business communication» by V.A. Tolochek. In this article, we consider the results of t-comparison and correlation analysis obtained with the help of the research questionnaire. The results confirm the working hypotheses.
Basic Concepts of Acting Psychotechnics in the School of Modern Acting Art

Each educator, while committed to the school of theatrical arts, develops his or her own style of teaching based on his or her many years of experience. This process is not intentional. Here, as we have said above, just as there are aspects of acting that are inherent in nature, so there are inherent aspects of pedagogy that are inherent in nature
Improved mean shift Clustering based MRI image segmentation for Cervical cancer

Cervical cancer unfortunately has emerged as one of the major causes of death, consequently making its detection extremely vital, although very challenging to diagnose and treat. The present study deals mainly with the performance study and analysis of modified mean shift Clustering (MMSC) techniques suitable for cervical images segmentation. This work presents the design, implementation and evaluation of a cervical images segmentation system employing MMSC for improving cervical cancer classification using MRI images. The input image is initially preprocessed with Gaussian filter and canny edge detection then the MMSC approach is utilized to segment the cervical cancer region. The proposed methodology is evaluated with necessary performance metrics like RMSE and PSNR.
Analisis Clustering menggunakan K-Medoid pada Data Penduduk Miskin Indonesia

In Indonesia, the problem of poverty is always the first center of attention. This is due to the government's awareness that there is an unsuccessful process in dealing with the problem of poverty which can lead to the emergence of various problems in the economic, social, and political fields. Various efforts, policies and programs are believed to be still not effective in reducing the number of people living below the poverty line, this is proven by the number of poor people increasing from year to year. Of course, the formulation of policies and programs needs to be improved and appropriate policy formulations carried out in stages, in the preparation of these policies must consider and understand the characteristics of poverty in each region. In this study, researchers used data sourced from the National Statistics Agency for 2007-2019. This study uses a data processing process that uses data mining techniques with the K-Medoid Clustering method. The k-medoids method is a clustering method that is useful for breaking a dataset into several groups. The advantages of this method are able to overcome the shortcomings of the k-means method which is sensitive to outliers. And another advantage of this method is that the results of this clustering process do not depend on the order of entry of a dataset. The K-Medoids Clustering method can be applied to data on the percentage of poor people by province, so that it can be seen the grouping of provinces based on the data. From the results of grouping with 3 clusters, it is obtained that the percentage cluster of low-level poverty levels is 19 provinces, clusters of percentages of moderate-level poverty levels are 12 provinces and clusters of percentages of high-level poverty levels are 3 provinces. In this study, it is expected to provide information to the government about the classification of data on the poor in Indonesia which has an impact on equity in the provision of assistance in Indonesia..
Visual ensemble representations in Deep Neural Networks trained for natural object recognition

Humans can quickly pool information from across many individual objects to perceive ensemble properties, like the average size or color diversity of objects. Such ensemble perception in humans is thought to occur extremely efficiently and automatically, but how it arises in the first place is unknown. Does ensemble perception arise because the visual system must solve many different types of perceptual problems, or are ensemble properties represented even in a system with the sole goal of recognizing individual objects? We used an artificial visual system—a deep neural network (DNN)—to determine whether the ensemble properties of average size and color diversity were present in a network pre-trained to recognize only individual natural objects. We presented the network with new images that were completely different from its training set: images of white circles of different sizes (randomly chosen from a specified range) or letter arrays containing four colored consonants with each letter drawn either from a broad sample of 19 colors (high diversity) or a randomly selected range of six adjacent colors (low diversity). Therefore, the ensemble properties of interest were a summary statistic for the whole image and not recoverable from any individual element. We tested whether a ResNet50 neural network could predict the average size or distinguish high vs low color diversity arrays by using the activations from different layers as input to a linear regressor and a linear classifier (SVM). We found that the network activations were highly accurate at predicting the average size and identifying the color diversity, even at the earlier layers in the network. In contrast, information about individual object features (object size) increased in the deeper layers. This demonstrates that artificial visual systems trained to only recognize individual objects also extract ensemble properties of multiple objects extremely early in visual processing.
THINGS-fMRI/MEG: A large-scale multimodal neuroimaging dataset of responses to natural object images

A detailed understanding of visual object representations in brain and behavior is fundamentally limited by the number of stimuli that can be presented in any one experiment. Ideally, the space of objects should be sampled in a representative manner, with (1) maximal breadth of the stimulus material and (2) minimal bias in the object categories. Such a dataset would allow the detailed study of object representations and provide a basis for testing and comparing computational models of vision and semantics. Towards this end, we recently developed the large-scale object image database THINGS of more than 26,000 images of 1,854 object concepts sampled representatively from the American English language (Hebart et al., 2019). Here we introduce THINGS-fMRI and THINGS-MEG, two large-scale brain imaging datasets using functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). Over the course of 12 scanning sessions, 7 participants (fMRI: n = 3, MEG: n = 4) were presented with images from the THINGS database (fMRI: 8,740 images of 720 concepts, MEG: 22,448 images of 1,854 concepts) while they carried out an oddball detection task. To reduce noise, participants’ heads were stabilized and repositioned between sessions using custom head casts. To facilitate the use by other researchers, the data were converted to the Brain Imaging Data Structure format (BIDS; Gorgolewski et al., 2016) and preprocessed with fMRIPrep (Esteban et al., 2018). Estimates of the noise ceiling and general quality control demonstrate overall high data quality, with only small overall displacement between sessions. By carrying out a broad and representative multimodal sampling of object representations in humans, we hope this dataset to be of use for visual neuroscience and computational vision research alike.
ANALISIS UNSUR PEMBANGUN PADA KUMPULAN CERPEN“MENGHARDIK GERIMIS” KARYA SAPARDI DJOKO DAMONOSEBAGAI BAHAN PEMBELAJARAN

This research is the result of a qualitative method which aims to analyze the intrinsic and extrinsic elements of the short strories “Menghardik Gerimis”, “Untuk Elisa”, and “Stasiun” by Sapardi Djoko Damono and find out whether the three short stories can be used as teaching materials for students class XI vocational school. The method used when research is using descriptivequalitative. Data collection techniques used are: observasi, tests, questionnaires, and documentation. The result analysis that have been carried out reveal that there are intrinsic elements: themes, plot, characters and characterizations and setting. Furthrtmore extrinsic elements: moral values, and social values in accordance with the syllabus of class XI semester one with standars of potential and basic potential in learning Indonesia. These three short stories are worthy of being made as teaching materials for class XI vocational students. Because the response of students shows that the short stories contained in the book Menghardik Gerimis by Sapardi Djoko Damono are interesting to read as used as teaching material even though the students had never before read the short story book Menghardik Gerimis by Sapardi Djoko Damono.
Sequential anomaly detection using nonparametric density estimators [Parametrik olmayan yoǧunluk tahmincileri ile ardışık anomali tespiti]

In this paper, we introduce an online anomaly detection algorithm to detect the anomalies in the observed data with two step approach in an unsupervised framework. In the first step, we estimate the density of the sequentially observed data with a novel kernel based approach. To this end, we partition the observation space and use nonparametric Kernel Density Estimator (KDE) in each region on a partition such that we do not assume any underlying distribution for the data. Then, we compare the estimated density of the data with a threshold to decide whether it is anomalous. We also solve the bandwidth selection problem in kernel based approaches in an efficient way. For this, we assign a set of kernel bandwidth values to each region, and make each estimator to converge to the best bandwidth choice for the corresponding subspaces in time. In our experiments, we show that our algorithm significantly outperforms the anomaly detection algorithms, which are highly used in the literature.
More Data Is Better Only to Some Level, After Which It Is Harmful: Profiling Neural Machine Translation Self-learning with Back-Translation

Neural machine translation needs a very large volume of data to unfold its potential. Self-learning with back-translation became widely adopted to address this data scarceness bottleneck: a seed system is used to translate source monolingual sentences which are aligned with the output sentences to form a synthetic data set that, when used to retrain the system, improves its translation performance. In this paper we report on the profiling of the self-learning with back-translation aiming at clarifying whether adding more synthetic data always leads to an increase of performance. With the experiments undertaken, we gathered evidence indicating that more synthetic data is better only to some level, after which it is harmful as the translation quality decays.
Improve speaking skills through storytelling methods in sixth-grade elementary school students

Speaking is a process of conveying information, ideas, or ideas from the speaker to the listener. Based on the initial survey the speaking ability of sixth-grade students was very low. This study aims to determine the improvement of speaking skills through the storytelling method to sixth-grade students of  SDN Kutorejo I. This research uses action research which is carried out in two cycles consisting of four stages, namely (plan, act, observe and reflect), with students' research subjects sixth-grade SDN Kutorejo I, totaling 38 students. Data collection techniques using observation, field notes, and documentation. Data analysis used quantitative descriptive analysis techniques on the results of the students' speaking ability test as an assessment instrument. Based on the results of data processing obtained through the results of research, it shows that active learning through the storytelling method has a positive impact in improving speaking skills, this can be seen in the first cycle (47,36 %), the second cycle ( 86,83%), classically having achieved mastery. So it can be concluded that learning with the storytelling method has a positive impact in improving students' speaking skills which are marked by an increase in student learning completeness. With the hope that teachers should train students more often, use interesting learning models related to speaking skills activities so that students do not feel bored in participating in the teaching and learning process..
Use the DRY Principle

The DRY principle is another important principle that a professional coder must follow when he writes a piece of code for an application. The SRP and OCP principles that you learned in Chapter 4 are related to the DRY principle. We came to know about this principle from Andy Hunt and Dave Thomas in their famous book, The Pragmatic Programmer. In this book, the DRY principle is stated as follows:
Solar Radiation Forecasting for Moderate Climatic Zone

The challenge with solar energy prediction is that the solar radiation is intermittent and uncontrollable. Energy forecasting can be used to mitigate some of the challenges that arise from the uncertainty in the resource. Weather data was sourced from India Meteorological Department for Bangalore and Chennai location. This paper provides statistical approach to predict the solar power in future. Analysis was done for different predictive models; Multiple Regression Model is used as we have multiple inputs. The results indicate the prediction of solar radiation has better accuracy during higher irradiation period rather than lower irradiation period.
Teenage Party Goers’ Motivation for Clubbing in Batam 2018

A very rapid change in Batam, unavoidably changes in society’s behavior, moreover with the global rivalry makes Batam trying continuously to make a change that make the society continuously motivated. Teenagers play a big role in this change, but unfortunately teenagers nowadays only live for pleasure. Teenagers search for entertainment spots, one of those places is nightclub. Batam is a city that adjacent to the neighbor country, Singapore that has the very exotic night, which will make teenagers entertained and fell into things that make them happy and can forget problems they are facing. This study was intended to determine the teenage party goers’ motivation for clubbing in Batam.  This study is a qualitativedescriptive study. The subjects are two teenage party goers in Batam, aged 22-23 years old which fond of clubbing with range from two to four times a week. The data collection for this study is using the interview method. There are intrinsic motivations that underlie each subject while they go clubbing that is on the basis of the problems they face with the purpose of getting entertained and pleasure due to feeling tired and burdened with works and lecture activities they live. Meanwhile, there are also extrinsic motivation arising from clubbing activities that they do due to the influence of other people’s invitation and presence of avoiding punishment because they do not want to be considered as an unfaithful person.  From the interviews’ results, researcher obtained there are intrinsic and extrinsic motivations that motivate the teenage party goers’ to go clubbing in Batam.
An Analysis on Rewards and Punishments toward Students’ Motivation in English Learning

Rewards and punishments are one of the strategies used to motivate students in learning English. This study purpose to find out the kinds of rewards and punishments used by the teacher in motivating students and to know the students’ responses of rewards and punishments in learning English at VIII-I grade at SMP Islam Ibnu Khaldun. The method used in this study was descriptive study. The participants of this study were an English teacher and 30 students of VIII-I grade. This study used interviews and questionnaires of instruments. Analyzing the data for the interview, the writer included the audio-tape recording, then the writer transcripted what the interviewers said and the writer classified the answers based on the transcription, and then draw the conclusion. To analyze the data the writer used percentage formula of questionnaire. From the results of the interview several rewards were used by the teacher in the classroom; praise, gifts, and grades and there were some punishments that were used by teachers; memorizing vocabulary, writing sentences, reading Al-Qur’an letters and cleaning class. In addition, the results of the questionnaire showed that most students responded positively to the provision of rewards and punishments by the teacher. In addition, 99 % of the students agreed that they became more active, more responsible, and also motivated. It turns out that those who considered punishment to be effective were 100%. Therefore, it could be concluded that implementation of rewards and punishments in learning process was more effective because it can make students study hard.
Co-occurrence statistics from vision and language capture thematic relationships between objects

Thematic relationships have been defined as the grouping of objects together by virtue of their complementary roles in the same scenario or event. Importantly, thematic thinking has been shown to occur implicitly, and can have a strong influence on a variety of cognitive behaviors, including the allocation of visual attention. Here we examined the extent to which an unsupervised machine-learning algorithm trained on object co-occurrence statistics could capture thematic relationships between objects within events. We asked Mturk workers (n=240) to list the most common objects found in 24 events ( e.g. “a child’s birthday party”). We then trained several models with a common continuous-bag-of-word (CBOW) architecture, but with different training corpora. The primary question of interest was whether human ratings for objects belonging to common themes were better described by training on visual scenes or text description. Vision-based models were trained on a database of over 22k densely segmented real-world and photorealistic scenes, and captured the frequency of co-occurrence between objects in visual scenes. Language-based models were trained on a database of over 2.6 billion websites, Wikipedia pages, and news articles, that captured the frequency of co-occurrence between objects in written descriptions. For each model, we ranked each target object in an event by the strength of the similarity between the object’s word vector and the vector representation of the event. We found that object rankings provided by language-based models were more strongly correlated with human rankings of objects than the rankings provided by image-based models, though there was unique variability in what scenarios language vs. vision models performed better on. Together these findings reveal the need to reexamine the way in which we define thematic relationships, and point towards the importance of understanding the impact of both visual and textual inputs.
Privacy Protection Framework for Credit Data in AI

Rich and fine personal and enterprise data are being collected and recorded, so as to provide big data support for personal and enterprise credit evaluation and credit integration. In this process, the problem of data privacy is becoming more and more prominent. For example, the user’s location data may be used to infer address, behavior and activity, and the user’s service use records may reveal information such as gender, age and disease. How to protect data privacy while meeting the needs of credit evaluation and credit integration is one of the great challenges in the era of big data. In this paper, we propose a security privacy protection framework in artificial intelligence algorithm, analyze the possibility of privacy leakage from data privacy security, model privacy security and environment privacy security, and give the corresponding defense strategy. In addition, this paper also gives an example algorithm of data security level, which can ensure the security of data privacy.
Особливості організації виховного процесу куратором академічної групи вищого навчального закладу

International scientific and practical conference «Pedagogy, psychology and teaching methods: international experience» : conference proceedings, July 16–17, 2021. Riga, Latvia : «Baltija Publishing», 2021. 295 pages.
A NEW WAY OF USING THE SPIDER DIAGRAM METHOD IN HEAVY METAL POLLUTION STUDIES FOR NEAR-SURFACE GEOPHYSICS AND THE OTHER EARTH SCIENCES

In this study, a new way of using the Spider Diagram (SD) method has been shown in the investigation of the pollution spread and pollutant species caused by heavy metal-containing leachate in soil ground. Thus, it has been shown that this method can be used in conjunction with the results of earth science studies and its contribution to interpretation was emphasized. Because today, heavy metals in leachate water are an important environmental problem. It was determined that SD and geophysical methods could be used together in studies investigating this pollution. The heavy metal-containing leachate may contaminate the agricultural/non-agricultural soils/grounds or underground/surface waters by spreading in the permeable geological unit. This pollution spread and pollutant species can be analyzed by geochemistry (soil samples) studies. In addition, the horizontal-vertical boundaries of this pollution and the direction of pollution can be determined by geophysical methods. The results of soil analysis can also be interpreted using the SD method. However, it has been determined that this method can be used to interpret with geophysical results. This comparison has been found to contribute to the geophysical results in interpretation and it has been observed that it strengthens the geological interpretations. As a result, it is shown that SD method is a method that allows evaluating a large number of data in a short time and it can be used together with earth science methods. If these methods are used together in heavy metal pollution investigations, it has been shown whether the pollution in the soil is caused by the leachate or the bedrock unit, and in addition, whether the underground and surface water resources in the region are under the threat of pollution caused by leachate. It was thought that such a study would also be useful in Environmental Impact Assessment (EIA) studies.