Reviewerly/exHarmony huggingface dataset:
Learning Video Making Training In Pangkep Regency South Sulawesi

In mid-2019 all countries in the world faced the Covid-19 pandemic caused by the Corona virus. Until now, in early 2022, this pandemic has not yet found a cure and is even continuing to expand and more and more people are infected with this virus. The outbreak of this virus caused the Indonesian government to impose large-scale social restrictions (PSBB) and restrictions on community activities (PKM) level 3, level 2 and level 1 for areas affected by this disease, including the city of Makassar. One of the impacts of imposing restrictions in the world of education is causing schools and campuses to implement online learning or learning from home. Based on these conditions, designing and preparing the learning process involves many components and activities that must be well planned, systematic and measurable using internet technology and interactive applications. However, most of the teachers who teach in education units in elementary schools in Pangkajene and the Islands districts are still experiencing problems in preparing learning tools, especially audio-visual media which can be an alternative in delivering teaching material. Conditions like this have the potential to be used as a target location for community service activities in terms of training in making learning videos with screen recording applications. In addition, teachers in general already have laptops and even schools in general have personal computers, so understanding of computer operation is very good. Considering the many benefits, community service activities are carried out at SD Negeri 25 Taraweang, Labakkang District, Pangkep Regency, effective for 2 (two) months.
PENGARUH LITERACY CLOUD TERHADAP MINAT BACA DAN KETERAMPILAN MEMBACA PEMAHAMAN SISWA KELAS IV SD

This study aimed at determining (1) the effect of cloud literacy on the students’ reading interest at fourth grade of SDN 2 Sayan, (2) the effect of cloud literacy on students’ comprehension reading skills at fourth grade of SDN 2 Sayan, (3) the effect of cloud literacy simultaneously on reading interest and comprehension reading skills for grade IV SDN 2 Sayan. This research was an experimental research with all fourth grade students of SDN 2 Sayan as population, which consisted of 53 students divided into 2 groups, namely IV A and IV B classes. The sample of this research was determined through random sampling technique. IV A Class as the sample of the experimental group with a sample of 27 people, while IV B class as the control class as many as 26 people. The data on reading interest and comprehension reading skills used questionnaires and essay tests. The data were analyzed using MANOVA assisted by SPSS 2.1 for windows. The results showed that 1) there was a significant difference in reading interest of the students who learned by using cloud literacy applications with the students who learned without using cloud literacy, 2) there was significant differences in reading comprehension skills of students who learned by using cloud literacy applications with students who learned without using cloud literacy, 3) there was a significant effect of the implementation of cloud literacy on the students’ reading interest and comprehension reading skills at fourth grade of SDN 2 Sayan simultaneously.
The art of education: Creative thinking and video games

The COVID-19 pandemic has shed new light on some significant problems that have been present in the education systems for decades. Тhe lack of various educational methods is becoming more and more evident, leading to a decreasing interest in learning and critical thinking. Оn the other hand, more and more research shows that different types of digital games can enhance creative and critical thinking in students. А closer look at the requirements of the educational programs shows how strict they are in their demands on students. Digital games like role playing games can be a bridge between students’ imaginations and complex educational material. Video games feature a variety of game mechanics that stimulate creative thinking and spark interest in otherwise difficult to understand topics. Motivation and engagement in digital games can be used in a specific way for educational purposes. If we think of teaching as an art in itself, what better method of teaching than through interactive arts.
TO THE QUESTION OF TACIT KNOWLEDGE: KANTIAN IDEAS IN THE FORMATION OF THE CONTEXT OF THE PROBLEM

The article traces a line in the development of epistemology, which led to the formation of the concept of tacit knowledge. The author demonstrates that the formation of this area of research is based on the solution to the problem of prerequisite knowledge found by I. Kant: a priori foundations of knowledge are such prerequisites for knowledge that are not the meaningful ideas but the forms of knowledge organization. The tacit knowledge as non-reflexive, non-explicit knowledge that exists on the periphery of cognitive activity functionally corresponds to just such a component of cognition; its significant part is made up of ontogenetically a priori prerequisites for cognition. The tacit knowledge is irreducible to the personal knowledge; on the contrary, the category of collective tacit knowledge exists. The article provides an overview of the possible sources of tacit knowledge identified in various areas of the study of the problem.
PROJECT ACTIVITY IN THE IMPLEMENTATION OF THE STATE YOUTH POLICY IN UNIVERSITIES

The article discusses the use of project activities as a mechanism for building educational systems, organizing student associations, creating conditions for the socialization and self-realization of students in the context of the implementation of state youth policy in higher education in Russia. The article presents the experience of using project activities in the formation of design competencies and ensuring the effectiveness of organizing work with youth in two universities (Branch of RANEPA and the Far Eastern State Transport University).
THE «DATA PROCESSING SCHOOL» PROJECT AS A TOOL FOR INCREASING STUDENTS’ LITERACY IN APPLIED FIELDS OF DATA SCIENCE

The digitalization of all spheres of society dwelling has spurred a deep transformation of everyday activities and routine practices both in professional and private life fields. Data science applications (including data processing and statistical analysis) have spread far beyond the limits of mathematical and natural sciences; they are actively used in humanities and social sciences, to analyze data as well as to generate prognosis given those data with application of information technologies and computer tools. This paper aims at presenting a training project “Data Processing and Analyzing School” as a tool for upscaling students’ literacy in applied fields of data science. The project has reached its goal, to train intensively and within a short term the students who are going to develop their own research projects in the applied fields of methods, models, tools of data science intended for collection, storing, pro-cessing and analyzing the data, results interpretation in research projects of humanities and social sciences. The paper is intended for specialists and faculty staff of educational institutions as well as for managers who organize digital sciences departments.
ВЗАИМОСВЯЗЬ ЭМОЦИОНАЛЬНОГО ВЫГОРАНИЯ ПЕДАГОГОВ С УДОВЛЕТВОРЕННОСТЬЮ ЛИЧНОСТИ СВОИМ ТРУДОМ И САМООЦЕНКОЙ ПРОФЕССИОНАЛЬНОЙ КОМПЕТЕНЦИИ (НА ПРИМЕРЕ ПЕДАГОГИЧЕСКИХ РАБОТНИКОВ ДАЛЬНЕГО ВОСТОКА)

The purpose of the article is to identify the nature of the possible relationship between the level of professional burnout, the de-gree of satisfaction of the individual with his work and self-assessment of professional competence. The analysis shows that the level of professional burnout affects the teacher's self- assessment of his professional competence, but at the same time does not affect the satisfaction of the individual with his work.
Deposit Money Bank Credit and the Nigerian Economic Growth: An Empirical Analysis

This paper set out to empirically evaluate the impact of deposit money banks’ credit on the performance of Nigerian economic growth. Data covering 1986 to 2020 were collected from secondary sources and unit root test, cointegration test and the ECM method
Empirical Relationship between Corporate Social Responsibility and Financial Performance: An Evidence from Banking Sector in Bangladesh

Corporate social responsibility (CSR) has become widely recognized as an integral aspect of firms' financial performance and practices. Consequently, the relationship between CSR and financial performance has been subject to scrutiny. This study aims to i
Privacy Preserving Through Federated Learning

The growing popularity of machine learning has increased concerns about privacy protection. This research addresses these concerns by proposing a federated learning framework that integrates federated multi-layer perceptron (FMLP) and the Paillier homomorphic encryption algorithm. The framework enables clients to transmit encrypted gradients during the learning process, thereby preserving the privacy of their data. An improved Paillier algorithm that can accelerate training addressing the computational overhead. In terms of overall computational cost and decryption speed, an improved decryption process for Paillier homomorphic encryption scheme is proposed which improves the efficiency. The model is trained using FMLP, the performance of the implementation compared to other models using metrics such as accuracy and time for decryption. In comparison with other Paillier variations, the experimental findings demonstrate that our approach is substantially faster at decryption. The accuracy given by the proposed model is 0.9692 on the MNIST dataset. The proposed architecture effectively balances privacy protection and machine learning performance, providing a promising solution to privacy concerns in the field of machine learning.
WiFi Emitter Identification Based on Few-Shot Convex Optimizable Network

The method based on deep learning applied in the field of communication signal emitter classification has been fully developed and achieved good classification performance. However, This requires ample labeled data to learn, and data acquisition and processing in the field of communication signals is a costly task. In this regard, based on the method of few-shot learning, this paper proposes Convex Optimized Linear Classifier with Attention Mechanisms(AM-COLC) framework for small samples emitter Identification. SVM classifier is used to solve the classification problem as a convex problem which improves the generalization of the model on new classes. we use a convolutional attention mechanism to improve end-to-end training and obtain better classification performance. In order to discuss the generalization and robustness of the signal identification model used in this article, we designed a comparative experiment to compare the classification performance of the baseline model in the experiment from the number of samples and the size of the model, indicating that the model has a certain inhibition of overfitting effect and is more suitable for signal classification with fewer samples.
ChineseCTRE: A Model for Geographical Named Entity Recognition and Correction Based on Deep Neural Networks and the BERT Model

Social media is widely used to share real-time information and report accidents during natural disasters. Named entity recognition (NER) is a fundamental task of geospatial information applications that aims to extract location names from natural language text. As a result, the identification of location names from social media information has gradually become a demand. Named entity correction (NEC), as a complementary task of NER, plays a crucial role in ensuring the accuracy of location names and further improving the accuracy of NER. Despite numerous methods having been adopted for NER, including text statistics-based and deep learning-based methods, there has been limited research on NEC. To address this gap, we propose the CTRE model, which is a geospatial named entity recognition and correction model based on the BERT model framework. Our approach enhances the BERT model by introducing incremental pre-training in the pre-training phase, significantly improving the model’s recognition accuracy. Subsequently, we adopt the pre-training fine-tuning mode of the BERT base model and extend the fine-tuning process, incorporating a neural network framework to construct the geospatial named entity recognition model and geospatial named entity correction model, respectively. The BERT model utilizes data augmentation of VGI (volunteered geographic information) data and social media data for incremental pre-training, leading to an enhancement in the model accuracy from 85% to 87%. The F1 score of the geospatial named entity recognition model reaches an impressive 0.9045, while the precision of the geospatial named entity correction model achieves 0.9765. The experimental results robustly demonstrate the effectiveness of our proposed CTRE model, providing a reference for subsequent research on location names.
A multi-center performance assessment for automated histopathological classification and grading of glioma using whole slide images

Accurate pathological classification and grading of gliomas is crucial in clinical diagnosis and treatment. The application of deep learning techniques holds promise for automated histological pathology diagnosis. In this study, we collected 733 whole slide images from four medical centers, of which 456 were used for model training, 150 for internal validation, and 127 for multi-center testing. The study includes 5 types of common gliomas. A subtask-guided multi-instance learning image-to-label training pipeline was employed. The pipeline leveraged "patch prompting" for the model to converge with reasonable computational cost. Experiments showed that an overall accuracy of 0.79 in the internal validation dataset. The performance on the multi-center testing dataset showed an overall accuracy to 0.73. The findings suggest a minor yet acceptable performance decrease in multi-center data, demonstrating the model's strong generalizability and establishing a robust foundation for future clinical applications.
MsPrompt: Multi-step prompt learning for debiasing few-shot event detection

Event detection (ED) is aimed to identify the key trigger words in unstructured text and predict the event types accordingly. Traditional ED models are too data-hungry to accommodate real applications with scarce labeled data. Besides, typical ED models are facing the context-bypassing and disabled generalization issues caused by the trigger bias stemming from ED datasets. Therefore, we focus on the true few-shot paradigm targeting to construct a novel training set that accommodates the low-resource scenarios. In particular, we propose a multi-step prompt learning model (MsPrompt) for debiasing few-shot event detection (FSED), consisting of the following two components: a multi-step prompt module equipped with a knowledge-enhanced ontology to leverage the event semantics and latent prior knowledge in the pretrained language models (PLMs) sufficiently for tackling the context-bypassing problem, and a prototypical network module compensating for the weakness of classifying events with sparse data and boost the generalization performance. Experiments on two public datasets ACE-2005 and FewEvent show that MsPrompt can outperform the state-of-the-art models, especially in the strict low-resource scenarios reporting 11.43% improvement in terms of weighted F1-score against the best baseline and achieving an outstanding debiasing performance.
Research on Intelligent Warehouse Path Planning Based on Multi-Objective Reinforcement Learning

In the context of intelligent warehousing, path planning is a crucial means for the efficient and safe handling of materials. Traditional path planning methods usually only consider the shortest path and fail to balance multiple objectives, such as maximizing cargo load and avoiding congestion. To address this issue, we propose a novel multi-policy multi-objective reinforcement learning approach for intelligent warehouse path planning, focusing on the path planning of Automated Guided Vehicles (AGVs) within the warehouse environment. Our method simultaneously optimizes multiple objectives, using our algorithm to achieve trade-offs among them and obtain multiple policies. Experimental results in simulated warehouse environments show that our method outperforms other state-of-the-art approaches in terms of multiple objectives. The proposed approach offers a promising solution for intelligent path planning in modern warehouses.
Model-Based Reinforcement Learning for Robotic Arm Control with Limited Environment Interaction

Reinforcement Learning (RL) has been applied to robotic arm control, which enables the agent to learn an effective policy to solve complex tasks. However, it requires constant interaction with the environment leading to low sample efficiency. In this paper, we propose a robotic arm control approach based on planning via lookahead search, which is a model-based RL algorithm to improve the sample efficiency. The approach builds an environment model in order to obtain the dynamics of the environment. Thus the model can be used to plan future actions by a tree-based search. The experiments show that our approach can solve the task of robotic arm control with less environmental samples.
Anthropological risks of digital support absolutisation of the educational process: concept and classification

Abstract: Introduction. The relevance of the research is determined by the world tendency of widespread introduction of digital means and technologies in all spheres of human activity. The use of digital support of the educational process is aimed at increasing the level of accessibility of the education, but the absolutisation of digital support can lead to negative consequences.&#x0D; The authors assume that the development of a general classification system of anthropological risks of digital support absolutisation will help to exclude or minimise their consequences when organising the educational process.&#x0D; The aim of the research is to build a classification structure of the discussed pedagogical categories. In achieving this goal, the authors formulated such tasks as reviewing the terminological apparatus in the field of digital support of education, identifying criteria sufficient for attributing risks to a certain type, and dividing anthropological risks into certain classes according to their characteristics or features. &#x0D; Methods. General theoretical methods were used in the present research: analysis, synthesis, comparison, induction, deduction, abstraction, generalisation, concretisation, modelling.&#x0D; Results. According to the results of the research, a number of key definitions within the specified issue were formulated. The general classification system of anthropological risks of digital support absolutisation of the educational process is developed. The conclusion is made concerning the need for further research in this area of study, as well as the development of the required competences of all subjects of the educational process to avoid absolutisation of digital support.
Sentiment analysis and opinion mining

The Elgar Encyclopedia of Technology and Politics is a landmark resource that offers a comprehensive overview of the ways in which technological development is reshaping politics. Providing an unparalleled starting point for research, it addresses all the major contemporary aspects of the field, comprising entries written by over 90 scholars from 33 different countries on 5 continents.
Shun-Ichi Azuma [People in Control]

How did your education and early career lead to your initial and continuing interest in the control field?
Development of practical measures to improve the qualifications of university teachers in a changing educational environment

The growing change in standards in higher education is a consequence of the improvement of technologies, social trends, and growing digitalization. Professional development, which is becoming increasingly important in the field of higher education, is aimed at adapting to such a dynamic development, especially affecting teachers. The purpose of this article is to consider the demand for advanced training of university teachers and efforts aimed at meeting the increased requirements for advanced training of personnel in the higher education system. Thus, the authors consider the process of professional development in the field of improving subject knowledge. The authors conclude that professional development becomes a strategic necessity for teachers in a specific context and can be implemented, on the one hand, by considering the expectations and wishes of the participants and, on the other hand, by ensuring the sustainable institutionalization of the concept of professional development.
Fiber-integrated single-photon device with an efficient fiber-quantum dot interface

Recent advances in solid-state quantum emitters have led to the realization of quantum communication, quantum teleportation, and quantum simulations. However, to implement such quantum technologies in a practical way, it is essential to interface quantum emitters with low-loss optical platforms, such as fiber optics. So far, there have been several different approaches for coupling single-photon emissions from quantum dots into fiber optics. In particular, integrating quantum dots into nanophotonic structures can significantly enhance light extraction and produce Gaussian like far-field patterns. However, a small numerical aperture of fiber still limits single-photon coupling efficiency. Alternatively, adiabatic coupling between tapered single-photon devices and fibers can provide near-unit coupling efficiency, while the delicate tapered structures cause long-term stability problems. Therefore, none of the previous approaches have realized an efficient and reliable implementation of fiber-integrated quantum emitters. In this study, we demonstrate efficient and compact plug-and-play single-photon sources based on hole-based circular Bragg gratings. A thin-membrane planar resonator with hole gratings produces an ultra-narrow vertical beam whose emission angle matches the small numerical aperture of a single-mode fiber. Using a pick-and-place technique, the fabricated single photon devices can be precisely integrated into the core of a single-mode fiber. The integrated fiber–QD system enables the compact plug-and-play operation of single photons from a source to a detector with high coupling efficiency and long-term stability.