Reviewerly/exHarmony huggingface dataset:
The role of libraries as a social institution in the transforming Russian society

The authors analyze the identified research and real-case publications on the critical problem: the libraries’ role as a social institution non-corresponding to the mission being conferred on them and its implementation. The purpose of the article is to actualize the problem related to the libraries’ role as a social institution and to attract attention of researchers in various domains, evoking interest to building professional competences in the specialists of social and cultural areas, including libraries. The authors suggest to develop management standards for the social institution of libraries to improve the quality of library services. The uniqueness of the study lies in the general analytical and comparative methods applied to existing standards, laws and conceptual documents for the institutions of culture. The researchers use competitive and systems methodological approaches. The empirical data of revealed sources are obtained through the methods of comparative analysis and systems analysis. The originality of the article is that the role of libraries is specified as a social institution going through transformation and intensive digitalization. The key functions of many social institutions (educational, cultural, families, etc.) are deflecting; their missions and roles are changing and being lost. On one hand, this is due to the chaos and inconsistency of transformation processes, and on the other hand, due to weakening control system and strengthening management system in business and economical processes. Today, despite the national programs, the problems of social systems affect economics, finances, ecology, etc. The current socioeconomic situation of transformation and intensive digitalization in every sphere of the Russian society, calls for regulated functions of social systems and their components. The analysis findings demonstrate that the libraries as a social institution have been losing their mission, while random and chaotic transformation processes result in imbalance and do harm. The social institutions depreciate the libraries’ implementing social functions which is evidenced by the analysis of legal and regulatory and conceptual acts in the cultural and social sphere. One of the solutions is to strengthen management of the libraries as a social institution and improving expertise of librarians.
CNN-Trans-Enc: A CNN-Enhanced Transformer-Encoder On Top Of Static BERT representations for Document Classification

BERT achieves remarkable results in text classification tasks, it is yet not fully exploited, since only the last layer is used as a representation output for downstream classifiers. The most recent studies on the nature of linguistic features learned by BERT, suggest that different layers focus on different kinds of linguistic features. We propose a CNN-Enhanced Transformer-Encoder model which is trained on top of fixed BERT $[CLS]$ representations from all layers, employing Convolutional Neural Networks to generate QKV feature maps inside the Transformer-Encoder, instead of linear projections of the input into the embedding space. CNN-Trans-Enc is relatively small as a downstream classifier and doesn't require any fine-tuning of BERT, as it ensures an optimal use of the $[CLS]$ representations from all layers, leveraging different linguistic features with more meaningful, and generalizable QKV representations of the input. Using BERT with CNN-Trans-Enc keeps $98.9\%$ and $94.8\%$ of current state-of-the-art performance on the IMDB and SST-5 datasets respectably, while obtaining new state-of-the-art on YELP-5 with $82.23$ ($8.9\%$ improvement), and on Amazon-Polarity with $0.98\%$ ($0.2\%$ improvement) (K-fold Cross Validation on a 1M sample subset from both datasets). On the AG news dataset CNN-Trans-Enc achieves $99.94\%$ of the current state-of-the-art, and achieves a new top performance with an average accuracy of $99.51\%$ on DBPedia-14. Index terms: Text Classification, Natural Language Processing, Convolutional Neural Networks, Transformers, BERT
Discussion on Redundant Processing Algorithm of Association Rules Based on Hypergraph in Data Mining

With the rapid advancement of big data, it is becoming a great problem for people to find objective information in the database. The relevance data processing rule for digging the information can be the way. Relevance data processing rule for digging the information is mainly studied in three aspects: data dimension, data abstraction level, and processing variable type. In the aspect of rules, the research mainly focuses on three aspects: active relationships, passive rules, and uncommon relationship rules. Association rules of digging the data can be the most well-employed investigation goal and aim for the data digging. Along with the advancement of the information scale, the time rate of traditional relationship rules exploration of counting ways is too low. How to increase the time rate for the way of counting is the main research content of relevance data processing rules for digging the information. Current relevance data processing rules for digging the information have two limitations: (1) metrics such as support, confidence, and lift rely too much on expert knowledge or complex adjustment processes in value selection; (2) it is often difficult to explain rare association rules. Based on the existing research, this paper proposes a Markov logic network framework model of association rules to address the above shortcomings. The theory of a hypergraph and system is proposed, and the method of a hypergraph in 3D matrix modeling is studied. Aiming at the new characteristics of big data analysis, a new super edge definition method is introduced according to the definition of the system, which greatly enhances the ability to solve problems. In the cluster analysis and calculation of hypergraph, this paper will use the hypergraph segmentation operator hMETIS to carry out the cluster analysis method in order to achieve higher accuracy in cluster analysis and calculation. As for the test of cycle ones, which is in line with the relevance of the hypergraph with clear directions, the thesis will offer a brand-new way to make an analysis and turn the rule of relevance into the hypergraph with clear directions with a new definition of the near linking matrix, and it will change the dealing way from the test of the cycle and more ones into the linking f bricks and circles, which is a new way to explore. This paper uses two datasets of different sizes to conduct rule prediction accuracy experiments on the Markov logic network framework model algorithm of association rules and the traditional association rule algorithm. The results show that compared with the traditional association rule algorithm, the rules obtained by the Markov logic network framework model of association rules have a higher prediction accuracy.
Crypto-Anarchism: The Ideology of Blockchain Technologies

One of the most modern currents of anarchism - crypto-anarchism - arose as a response to the global development of digital technologies and the Internet and operates exclusively within the framework of the “global web”. The paper attempts to study one of the most unusual branches of anarchist philosophy and its impact on the digital life and politics of several states. With the help of functional and comparative methods of political research, the author analyzes crypto-anarchism as part of the ideology of anarchism, the main goal of which is to find out how viable crypto-anarchism is as an independent movement. The article raises the question of whether the ideas of crypto-anarchism can be used to effectively address current socio-political problems. The theoretical basis of crypto-anarchism looks more and more relevant, as it affects the security of the individual on the Web and is aimed at fighting against widespread state control. Despite the fact that, as the study showed, cryptoanarchism as a movement does not have sufficient elaboration and influence on real politics, its deeper study can be useful for preparing political programs aimed at Internet users (which constitute around 62.5 % of the global population), as well as studying political models and their development paths using virtual simulations and virtual states (Liberland, Wirtland), which are characteristic of crypto-anarchism.
Affective Political Polarization and Hate Speech: Made for Each Other?

Abundant academic research has shown evidence of the growing polarization across the globe both in general and in terms of affective polarization. Previous research on this topic primarily employed reactive research methods like surveys or experiments, which however do not allow researchers to observe the behavior of the units of analysis in a natural setting. Presents an alternative approach that involves analyzing the observed behavior of social media users and identifying the key polarizing cleavages through the study of hate speech with respect to distinct target groups. We present a novel coding schema for textual data, which includes two components: first, an operationalized definition of hate speech as a phenomenon with at least one of the three elements - insult, discrimination, or aggression; and second, an original coding guide for human coders annotating the use of hate speech. We apply our approach to the analysis of empirical data that includes over 5000 posts on the social media platform VK about the meetings between the Presidents of Russia and Belarus in 2020-2021. After coding the collected data, we performed the empirical analysis that identified two generic cleavages. One is about domestic politics in Belarus and Russia, whereas the other is related to the opposition between these two countries on the one hand, and Western countries on the other. We also found an additional Russian/Belarusian cleavage that is peculiar to the collected dataset. Our methodology also allowed us to identify and analyze the dynamics of macro-groups that were targets of hate speech. Importantly, these results - as any other dynamic aspect of analysis - would be highly challenging in research based on reactive methods. Thereby our results highlight the prospects of applying the proposed methodology to a broad range of textual data, as well as the benefits of exploratory analysis that helps overcome the limitations of survey instruments.
STRATEGI BPBD KABUPATEN PACITAN DALAM UPAYA PENANGGULANGAN BENCANA BANJIR DAN TANAH LONGSOR

This study aims to determine the condition of natural disasters such as floods and landslides and the efforts of BPBD to overcome them. The research was conducted in Pacitan City on the basis of the consideration that floods and landslides are common in this area. Data was collected through open interviews, observation, and document review, then the data were analyzed descriptively qualitatively. The results showed that the condition of the Pacitan City area consists mostly of highlands in the form of steep mountains and shallow river areas so that it is very vulnerable to natural disasters in the form of floods and landslides, especially during the rainy season. The flood natural disaster that occurred in Pacitan City was mainly caused by the silting of the riverbed and the narrowing of the river's width. Meanwhile, landslides are caused by erosion caused by rainwater and the increasing number of residential areas that make the foot of the slope increasingly eroded. Efforts made by BPBD to overcome natural disasters of floods and landslides that occurred in Pacitan Regency were by launching various forms of strategies such as the DESTANA program, Socialization, Procurement of Disaster Simulations, Construction of Embankments, Installation of Information Signs for Disaster-Prone Areas, Map-making of Disaster-Prone Areas and Determination of Evacuation Areas, and Mangrove Planting. The various strategies implemented also serve to increase public awareness and preparedness for disasters.
Values for a Post-Pandemic Future

Abstract The costs of the COVID-19 pandemic are yet to be calculated, but they include the loss of millions of lives and the destruction of countless livelihoods. What is certain is that the SARS-CoV-2 virus has changed the way we live for the foreseeable future. It has forced many to live in ways they would have previously thought impossible. As well as challenging scientists and medical professionals to address urgent value conflicts in the short term, COVID-19 has raised slower-burning value questions for corporations, public institutions, governments, and policymakers. In simple terms, the pandemic has brought what we care about into sharp relief, both collectively and individually. Whether this revaluation of our values will last beyond the current pandemic is unknown. Once COVID-19 has been tamed, will the desire to return to our previous lives be irresistible? Or will living under pandemic conditions have taught us something that will be incorporated into how we design our future lives and technologies? These are hard questions for the ethics of technology, which this volume aims to explore and address.
The Effect of Caterpillar Game on Higher Order Thinking Skills (HOTS) in Early Childhood Education

Purpose – This study aimed to determine the effect of the caterpillar game on Higher Order Thinking Skills (HOTS) in children aged 5-6 years at TK Negeri Pembina Ki Hajar Dewantoro, Dungingi District, Gorontalo City.Design/methods/approach – The research uses experimental quantitative with pre-experimental type one grub pre-test and post-test. Data analysis used the Shapiro-Wilk normality test, followed by a t-test using SPPS software. Research respondents consisted of 30 children.Findings – The findings of this study resulted in an analysis of the value of the tcount amounted to 3,689. While the value of the ttable (α) = 0.05, which equals 0.674. If tcount &gt;ttable 3.689 &gt;0.674, in other words, tcount &gt; ttable, then H 0 rejected and H 1 received. So that there is an influence between the caterpillar game on the ability of Higher Order Thinking Skills (HOTS) in children aged 5-6 years at the State Kindergarten of Pembina Ki Hajar Dewantoro, Dungingi District, Gorontalo City.Research implications/limitations – This study focuses on children aged 5-6, the activity, and how the child completes the task correctly. In the learning process, some children are still challenged in their level of understanding, so there is a need for proper implementation of learning.Practical implications – This research contributes in the form of innovations to teachers in providing fun learning and references for the teaching team in developing learning media for children.Originality/value – This research provides an in-depth understanding of learning media innovation and developing Higher Order Thinking Skills (HOTS) on early childhood education.Paper type: Research paper
Implementating ‘Calistung’ Clinic Program to Overcome Students’ Reading Difficulty

There were 8 chlidren who had difficulty reading in grade I Ibnu Khaldun, every child has different reading difficulty. This study aims to identify reading difficulties experienced by students and the application of the reading, writing, and counting skills (Calistung) clinic program in overcoming students' reading difficulties. This study used a qualitative descriptive method. Data were collected through observation, interviews, and documentation followed by data triangulation. Data analysis was performed through data reduction, data display and conclusion drawing/verification. Based on the results of the study, the reading difficulties experienced by students were recognizing letters such as pronouncing the letter incorrectly; distinguishing similar letters adversely; stuttering, connecting syllables, reading simple words and sentences; and difficulty in reading consonants, vowels, and diphthongs. Students who have reading difficulties were assisted through the Calistung clinic program.
The Effectiveness of Digital Literacy Indicators in Improving Students' Reading Interest

The purpose of this study was to determine the effectiveness of digital literacy indicators in improving students' reading interests. The research method used is quantitative. The samples in this study were the fifth-grade students at SDN Pajang IV, SDN Tegalrejo, SDN 1 Jajar, and SDN Setono, totaling 86 respondents. The data collection technique used is a questionnaire. The data analysis used is SEM (Structural Equation Model) analysis with SmartPLS and SPSS programs. This research was conducted by testing the instrument outside the sample but in one regional population. After that, the instrument was distributed to the research sample. The results of the study revealed that the effectiveness of digital literacy in improving reading interest was 29%. Further research needs to be done to improve elementary school students’ digital literacy and reading interest.
Innovative Knowledge Management Tool of the Visually Impaired Students’ Mental Health Care for Teachers, Parents and Public Health Personnel

Research on innovative knowledge management tool of the visually impaired students’ mental health care for teachers, parents and public health personnel is an academic investigation for research and development purpose. Knowledge management was employed in the early stage of the investigation. The objectives of this study were comprised of 1) developing an innovation to be used by teachers, parents, and public health personnel to provide support to the visually impaired children; 2) sharing knowledge regarding mental health support of the visually impaired students to vision teachers, parents, and relevant public health personnel; and 3) outlining a number of approaches for mental health support to visually impaired children. This study began with an interview with relevant individuals to identify various issues of innovative development with knowledge management. A knowledge management team was formed with participating blind schools which aimed to measure the knowledge level of each school, to design and develop an innovation, to provide access to the knowledge and to identify methods for knowledge sharing. The innovation was adopted in five schools, where the presentation of the investigation results, and knowledge sharing forums were required. There were three main findings: 1) the innovative knowledge management tool was perceived as a handbook with both regular font and braille codes for teachers, parents, and public health personnel to assist visually impaired children with their mental health. The E1/E2 efficiency score of the handbook was 83.55/82.36 which was higher than the stated requirements; 2) Teachers, parents, and public health personnel received a statistically significant higher mean score of cognitive disabilities in mental health support of visually impaired children after the use of the handbook at the.05 level; and 3) a policy recommendation at school, Education Area Office, and Department of Mental Health served as a guideline of mental health support for children with visual impairment.
Use of Social Media for Collaborative Learning in Online Learning: A Literature Review

When schools are closed to avoid face-to-face learning during this pandemic, students use social media to continue learning. Social media has become a tool for distance learning, from situations that require online learning from home. The purpose of this study was to conduct a literature study on how to accept and use social media in learning. This article summarizes insights from a recent research literature review published between 2015-2020 on how social media is accepted and used in learning. The results of the study indicate that the use of social media can be used to support learning activities. This is because social media is easy to use and popular among students, so social media can be used to support learning. From the ease of using social media in sharing learning resource information, interacting, collaborating, and discussing, of course collaborative learning outside the classroom can be realized. However, it is necessary to have good planning, management, and strategies in the use of social media to support collaborative learning, so that it can achieve the goals and needs of students' online learning.
Evidence-Based Regularization for Neural Networks

Numerous approaches address over-fitting in neural networks: by imposing a penalty on the parameters of the network (L1, L2, etc); by changing the network stochastically (drop-out, Gaussian noise, etc.); or by transforming the input data (batch normalization, etc.). In contrast, we aim to ensure that a minimum amount of supporting evidence is present when fitting the model parameters to the training data. This, at the single neuron level, is equivalent to ensuring that both sides of the separating hyperplane (for a standard artificial neuron) have a minimum number of data points — noting that these points need not belong to the same class for the inner layers. We firstly benchmark the results of this approach on the standard Fashion-MINST dataset, comparing it to various regularization techniques. Interestingly, we note that by nudging each neuron to divide, at least in part, its input data, the resulting networks make use of each neuron, avoiding a hyperplane completely on one side of its input data (which is equivalent to a constant into the next layers). To illustrate this point, we study the prevalence of saturated nodes throughout training, showing that neurons are activated more frequently and earlier in training when using this regularization approach. A direct consequence of the improved neuron activation is that deep networks are now easier to train. This is crucially important when the network topology is not known a priori and fitting often remains stuck in a suboptimal local minima. We demonstrate this property by training a network of increasing depth (and constant width): most regularization approaches will result in increasingly frequent training failures (over different random seeds) whilst the proposed evidence-based regularization significantly outperforms in its ability to train deep networks.
Data Augmentation for Dementia Detection in Spoken Language.

Dementia is a growing problem as our society ages, and detection methods are often invasive and expensive.Recent deep-learning techniques can offer a faster diagnosis and have shown promising results.However, they require large amounts of labelled data which is not easily available for the task of dementia detection.One effective solution to sparse data problems is data augmentation, though the exact methods need to be selected carefully.To date, there has been no empirical study of data augmentation on Alzheimer's disease (AD) datasets for NLP and speech processing.In this work, we investigate data augmentation techniques for the task of AD detection and perform an empirical evaluation of the different approaches on two kinds of models for both the text and audio domains.We use a transformer-based model for both domains, and SVM and Random Forest models for the text and audio domains, respectively.We generate additional samples using traditional as well as deep learning based methods and show that data augmentation improves performance for both the text-and audio-based models and that such results are comparable to state-of-the-art results on the popular ADReSS set, with carefully crafted architectures and features 1 .