Reviewerly/exHarmony huggingface dataset:
ETHICAL AND PSYCHOLOGICAL ASPECTS OF EDUCATIONAL EXPERTS BUSINESS RELATIONS ON CONDUCTING INSTITUTIONAL AUDIT

The article considers the basic principles of professional ethics of educational experts in conducting institutional audits of educational institutions, including the rule of law, public interest as the main criteria of professional activity of an expert, professional competence, exclusion of self-profit actions, objectivity and independence, confidentiality of information, etc. The rules of interaction with representatives of educational institutions engaged in educational activities are presented. Emphasis is placed on the psychological aspects of business relations and relationships with colleagues. It is noted, that the work of an educational expert does not only require comprehensive knowledge of legislation in the field of education, but also includes certain moral obligations based on generally accepted norms, which guide the expert in conducting institutional audits in educational institutions. The Code of Professional Ethics of an institutional audit expert in educational institutions is a set of moral and ethical obligations and requirements based on generally accepted norms, which experts have to follow during the institutional audit procedure in educational institutions. The following theoretical research methods were used to solve certain problems: systematic analysis, comparison, systematization, classification and generalization of scientific and methodological literature on the problem; method of systematic analysis of philosophical, psychological-pedagogical, sociological literature for theoretical generalization of leading scientific approaches; interpretation of key provisions of the study.
A Quantum Algorithm for the Sub-graph Isomorphism Problem

We propose a novel variational method for solving the sub-graph isomorphism problem on a gate-based quantum computer. The method relies (1) on a new representation of the adjacency matrices of the underlying graphs, which requires a number of qubits that scales logarithmically with the number of vertices of the graphs; and (2) on a new ansatz that can efficiently probe the permutation space. Simulations are then presented to showcase the approach on graphs up to 16 vertices, whereas, given the logarithmic scaling, the approach could be applied to realistic sub-graph isomorphism problem instances in the medium term.
Entity Relation Extraction Model Based Negative Feedback Attention

The relationship between entity pairs is the key to understanding an entire sentence or paragraph in a natural language, so entity relationship extraction is particularly important in natural language processing tasks. When we understand the information of a sentence, we often need to combine the information into the context. The entity relationship of a sentence is expressed as a relationship, but combined with the next sentence, the entity represents another relationship,and It called negative feedback. In this paper, to solve these problems, we propose NFBATT.a entity relationship extraction model based negative feedback attention.which is based on improved attention and pre-training of BERT.The main is highlighted the negative feedback in attention of Distant Supervision Relation extraction,and in order to get a better vector representation get a better vector representation of semantics, the sentence vector we represent is a combination of word Embedding, entity Embedding and positional embedding of solid and non-solid entity words by deep learning, which we call sentence multi-information representation. We use the negative feedback attention to reduce the influence of negative feedback on sentence relationship discrimination. At the same time, we have also done a lot of experiments to prove it. Through extensive experiments on Sogou Person Knowledge Map dataset.we demonstrate NFBATT&#x0027;s effectiveness.and the accuracy rate is 82.0&#x0025;, which is better than the best method currently available.
Digitalization of Agri-food Supply Chains: Facts and Promises of Blockchain Technology

With the globalization agri-food supply chains become longer and more complex, what makes it difficult to manage. parallel with this there is increasing customer demand for knowing the origin, ingredients and delivery chain of agricultural products. the need for more efficient, traceable and visible supply chain for agri-food products appears from supply side. The digital methods of industry 4.0 are able to provide solutions for these demands particularly those which are able to trace product reliable way from its origin to consumption. blockchain is a disruptive technology by which every important product information can be collected, stored and shared with the actors involved in the product process chain from the first phase to the final customer. The paper provides an overview about blockchain technology and its operation in agri-food supply chains environment, then presents some examples from the practice of different countries where blockchain was applied in the agricultural sector in order to introduce experiences as facts, and further possibilities as promises by blockchain.
INFORMATION SYSTEM OF CONSTRUCTION SERVICE WEB BASED ON PAPUA GAPEKSINDO ASSOCIATION

Information technology is currently growing rapidly. Computers as information technology tools play an important role in solving data processing problems. Therefore, many companies are using information technology to improve enterprise data management. One of them is GAPEKSINDO Association. It is the organization of companies in construction services. In collecting data and processing business entity data, the companies will apply for Business Certificate, Gapeksindo Association in Papua have the problems, they are data collection and data processing is still using Excel. The weakness use Excel is the data storage can be accidentally deleted and search data will take a long time. Based on the problems above, information system is needed to support Papua Gapeksindo Association in collecting data and processing business data. It can provide convenience in collecting data and processing business data, ensuring security in storing data and it will be stored in database. This information system based on web by using the Unified Modeling Language (UML) design method. The development method using the Waterfall method and the testing method using Black Box.
Analysis of Academic Fraud of Student in Unversity Based on Fraud Pentagon Theory

Academic fraud is a serious problem related to integrity and ethics that occurs in every educational institution.This study aims to identify the causes of academic fraud behavior among students at state universities in Magelang based on the Pentagon theory of fraud dimensions.This research is a qualitative study using a case study approach.Data were collected by means of observation and interviews with 15 respondents of undergraduate and diploma level students.Based on the results of the study, it can be concluded that the act of student academic cheating is caused by five main aspects, namely pressure, opportunity, rationalization, ability and finally the student's personal arrogance.A student does not commit academic fraud if one aspect is not fulfilled.Some actions that must be taken to minimize fraud are improving the assessment system and examination supervision, improving the quality of the lecture process, establishing strict sanctions, evaluating regular academic guidance and guidance, and also organizing non-academic programs to improve the balance of IQ, EQ. , SQ, and AQ.
Graduate Students’ Exploration of Opportunities in a Crisis

The following white paper details the University of Calgary’s 2021 graduate student conference titled, ‘Opportunities in a Crisis.’ This white paper works to describe how graduate students explore the terms ‘opportunities’ and ‘crisis’ within their research interests. These research interests were interdisciplinary to various fields such as telecommunications policy, algorithmic studies, critical race theory, and video game studies to list a few. Through this conference, we observed an acute awareness of the ways in which the COVID-19 crisis has impacted research in media activism, feminist media studies, internet infrastructure, and teaching and learning, to mention a handful. This white paper is divided by panel sections, thereby allowing readers to connect with this graduate student conference and help inform future research on topics in communication and media studies, as they are framed in working through these crisis moments in our global history. Our white paper set out to achieve two goals: first, document the presentations and emerging scholarly work of graduate students; and second, reflect on how research can, and very well does, pivot in times of crises, specifically using our current global COVID-19 pandemic as an ongoing, lived experience. This white paper achieves these goals which we believe helps in the preservation of this unique moment in time to be a graduate student.
Program Synthesis Guided Reinforcement Learning for Partially Observed Environments

A key challenge for reinforcement learning is solving long-horizon planning problems. Recent work has leveraged programs to guide reinforcement learning in these settings. However, these approaches impose a high manual burden on the user since they must provide a guiding program for every new task. Partially observed environments further complicate the programming task because the program must implement a strategy that correctly, and ideally optimally, handles every possible configuration of the hidden regions of the environment. We propose a new approach, model predictive program synthesis (MPPS), that uses program synthesis to automatically generate the guiding programs. It trains a generative model to predict the unobserved portions of the world, and then synthesizes a program based on samples from this model in a way that is robust to its uncertainty. In our experiments, we show that our approach significantly outperforms non-program-guided approaches on a set of challenging benchmarks, including a 2D Minecraft-inspired environment where the agent must complete a complex sequence of subtasks to achieve its goal, and achieves a similar performance as using handcrafted programs to guide the agent. Our results demonstrate that our approach can obtain the benefits of program-guided reinforcement learning without requiring the user to provide a new guiding program for every new task.
Considering Eye-tracking as a Validation Tool in Cinema Research

Abstract The use of eye-tracking in data collection, when accompanied by the proper research questions and methodology, is a powerful tool that may provide invaluable insights into the way viewers perceive and experience movies. Film theory can use eye-tracking to test and verify research hypotheses not only with unprecedented accuracy, but also with the ability to address a significant variety of theoretical questions. Eye-tracking can help build contemporary film theory by supporting its various fields of research, and also even assist the production of films themselves by helping filmmakers make more informed creative decisions. The present article is an overview of eye-tracking and its gradual implementation in cinema research; in the context of discussing some recent examples of academic work based on eye-tracking, it considers the technology of eye-trackers and the way in which human vision handles visual information on screen. By testing the attentional behaviour of viewers, eye-tracking can produce more solid answers to questions regarding the way films are experienced; therefore, it may very well prove to be the spearhead of a more robust body of film theory in the near future.
Use of Smartphones among Research Scholars at Bangalore University: A Study

The present a study on use of Smartphones among research scholars at Bangalore university.The study indicated that most of the research scholars are used smartphones, in this paper sought to investigate the digital literacy using smartphones of research scholars in Bangalore University.The study brings about the purpose of using smartphones, browser used to search information in smartphone, mobile application used for research, advantages and limitations of using smartphone.The most used browser was the Google Chrome 81.66% and nearly 95.83% respondents used typed keywords to search for information on smartphones.The reliability of information obtained was checked only by respondents.It also observed that the major benefits for using smartphone was their immediate access to information and the barrier was their screen size, the result from this study reflect the research scholar's willingness to adopt the digital technology for information access.
Інноваційне освітнє середовище як складова формування професійної компетентності майбутніх фахівців дошкільної та музичної галузі

International scientific conference «Modern European psychological and pedagogical education. The development of a creative learning environment» : conference proceedings, October 8–9, 2021. Łódź, the Republic of Poland : “Baltija Publishing”, 2021. 162 pages.
Predicting Moderation of Deliberative Arguments: Is Argument Quality the Key?

Human moderation is commonly employed in deliberative contexts (argumentation and discussion targeting a shared decision on an issue relevant to a group, e.g., citizens arguing on how to employ a shared budget).As the scale of discussion enlarges in online settings, the overall discussion quality risks to drop and moderation becomes more important to assist participants in having a cooperative and productive interaction.The scale also makes it more important to employ NLP methods for (semi-)automatic moderation, e.g. to prioritize when moderation is most needed.In this work, we make the first steps towards (semi-)automatic moderation by using state-ofthe-art classification models to predict which posts require moderation, showing that while the task is undoubtedly difficult, performance is significantly above baseline.We further investigate whether argument quality is a key indicator of the need for moderation, showing that surprisingly, high quality arguments also trigger moderation.We make our code and data publicly available.
Комплексний підхід до діагностики дітей раннього віку з ООП

International scientific conference «Modern European psychological and pedagogical education. The development of a creative learning environment» : conference proceedings, October 8–9, 2021. Łódź, the Republic of Poland : “Baltija Publishing”, 2021. 162 pages.
Sentiment Analysis Using Naive Bayes Algorithm with Feature Selection Particle Swarm Optimization (PSO) and Genetic Algorithm

This study analyzes Sentiment to see opinions, points of view, judgments, attitudes, and emotions towards creatures and aspects expressed through texts. One of Social Media is like Twitter is one of the most widely used means of communication as a research topic. The main problem with sentiment analysis is voting and using the best feature options for maximum results. Either, the most widely known classification method is Naive Bayes. However, Naive Bayes is very sensitive to significant features. That way, in this test, a comparison of feature selection is carried out using Particle Swarm Optimization and Genetic Algorithm to improve the accuracy performance of the Naive Bayes algorithm. Analyses are performed by comparing before and after testing using feature selection. Validation uses a cross-validation technique, while the confusion matrix ??is appealed to measure accuracy. The results showed the highest increase for Naïve Bayes algorithm accuracy when using the feature selection of the Particle Swarm Optimization Algorithm from 60.26% to 77.50%, while the genetic algorithm from 60.26% to 70.71%. Therefore, the choice of the best characteristics is Particle Swarm Optimization which is superior with an increase in accuracy of 17.24%.
Designing Lectora Inspire-Based Application as Interactive Multimedia for Teaching Vocabulary of Recount Text

This research aimed to design an appropriate interactive multimedia to support teaching vocabulary of recount texts to tenth-grade students of MAN 2 Pontianak using Lectora Inspire. The interactive multimedia was designed based on the teacher’s interview, analysis of the current syllabus, and the English textbooks. The writer applied ADDIE as the research model. However, the writer only implemented four phases which were analyzing, designing, developing, and evaluation (ADDE). The product was designed by using Lectora Inspire and some supporting software. The contents of the interactive multimedia were focused on historical recount texts and it was adopted from “Buku Bahasa Inggris Kelas X” Version 2017 that linked to syllabus and learning objectives. The interactive multimedia was completed with some interactive features like feedbacks, navigation buttons, and dictionary. The writer used expert validation to evaluate the product based on the content aspects and the design aspects. It revealed that the product validation ranged from good to very good. Therefore, the interactive multimedia was usable to support teaching and learning processes in MAN 2 Pontianak.
Development of E-Book Based on Local Sumut Culture in High Class IPS Education Courses PGSD UNIMED

This study aims to: (1) describe the results of expert tests regarding the North Sumatran Local Culture-Based E-Book product in the Social Sciences Education Course for Higher Class PGSD Unimed, and (2) describe the use of the North Sumatran Local Culture-Based E-Book development in this course.Unimed PGSD High Class Social Studies Education.The research method used is a research and development (R&D) model with a Van den Akker development model by developing an e-book for High Class Social Studies Education based on North Sumatran Local Culture.The location of the research was carried out in the PGSD FIP Unimed study program which was located on Jl.Willem Iskandar / Pasar V, Medan.The population of this study was all students of PGSD Unimed Stambuk 2020 totaling 225 students consisting of 11 (AK) regular and extension classes.The sample of this research was carried out in a class program and one class was selected, namely Class B Regular 2020, totaling 40 students.The resultsshowed that the North Sumatran Local Culture-Based E-Book on the PGSD High Class Social Studies Education Course that was developed was validated by material and media experts.The average score of the second phase material experts was 71% with the "Fair" criteria and the average score of the second phase material experts was 90.6% with the "Very feasible" criteria.And the results of the media experts assessment were 91.43% with the criteria of "Very decent".In the trial of the E-book product to students, the pretest results with completeness 23.07% and post-test results with completeness 96.15%.
Teacher education – phylogenetic code of modern era

The presented article relevance is determined by the Russian government increased attention to the education issues.The modern teacher training is considered in a triad: as a way to achieve the goals of the youth's national development; as the teaching staff training for the country scientific and technological development.The authors apply to the reflection of the teaching staff training growing role, which insistently requires an answer to such problematic questions as the new pedagogical concepts existence, determining other projections of the education development; the previously unknown format for the professional educational programs development, taking into account the requirements for the education modern subject.The study purpose is to identify the training strategy implementation features of a modern teacher as a carrier of the phylogenetic code.The strategic and tactical features of the teacher educational process organization in Russia are considered in the article from the inductive approach position, which allows to identify the teacher training modern model, the main scenarios for the events development and the leading educational tracks in the teacher's professional establishment.The use of the humanitarian expertise method determines the human activity direction in the modern society, the training teachers' main discourses for the national school.As a result of the express examination, including the reflexive knowledge assimilation and systematization about the national education system state, the conclusions were drawn about the pedagogical theory and practice certain aspects development; on the risks identification in the teaching staff training organization.
VANET: Trust Evaluation Using Artificial Neural Network

There is an increasing emphasis on enhancing the efficiency traffic management systems. Information is exchanged between the vehicular nodes to efficiently monitor and control huge volumes of vehicle. All existing applications in this area have focused on reliable data exchange and authentication process of vehicular nodes to forward messages. This study proposes a new entity centric trust framework using decision tree classification and artificial neural networks. Decision tree classification is used to derive rules for trust calculation and artificial neural networks are used to self-train the vehicular nodes, when expected value is not met. This model uses multifaceted role and distance based metrics like Euclidean distance to estimate the trust. The proposed entity centric trust model, uses a versatile new direct and recommended trust evaluation strategy to compute trust values. The suggested model is simple, reliable and efficient in comparison to the other popular entity centric trust models.
MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets

Internet memes have become powerful means to transmit political, psychological, and socio-cultural ideas. Although memes are typically humorous, recent days have witnessed an escalation of harmful memes used for trolling, cyberbullying, and abuse. Detecting such memes is challenging as they can be highly satirical and cryptic. Moreover, while previous work has focused on specific aspects of memes such as hate speech and propaganda, there has been little work on harm in general. Here, we aim to bridge this gap. In particular, we focus on two tasks: (i)detecting harmful memes, and (ii) identifying the social entities they target. We further extend the recently released HarMeme dataset, which covered COVID-19, with additional memes and a new topic: US politics. To solve these tasks, we propose MOMENTA (MultimOdal framework for detecting harmful MemEs aNd Their tArgets), a novel multimodal deep neural network that uses global and local perspectives to detect harmful memes. MOMENTA systematically analyzes the local and the global perspective of the input meme (in both modalities) and relates it to the background context. MOMENTA is interpretable and generalizable, and our experiments show that it outperforms several strong rivaling approaches.