Reviewerly/exHarmony huggingface dataset:
Web Pedagogical Content Knowledge-Self Efficacy of Pre-Service Physics Teacher

Web technology-based learning design competencies are essential for pre-service teachers, especially in the digital era. However, the exploration of the self-efficacy component of these skills has been little revealed. The purpose of this study was to describe the efficacy of pre-service physics teachers on the competence of Web Pedagogical Content Knowledge (WPACK), determine the relationship between the components of self-efficacy of pre-service physics teachers to these competencies, and map them out. A survey of 309 pre-service physics teachers from nine Indonesian universities was conducted. Descriptive statistics and the Partial Least Square Structural Equation Modeling (PLS-SEM) modeling technique were used to evaluate the retrieved data. The results suggest that the pre-service physics teacher had a high level of WPACK component self-efficacy. Their WPACK self-efficacy components were also positively correlated, especially on the web pedagogical knowledge (WPK) and web pedagogical content knowledge (WPACK) components. This research implies that teacher candidates need to improve the basic competence of web knowledge to increase its integration into physics pedagogy and content.
Agents and Secure Contracts in Cyber-Physical Systems: A Simulation

Future technologies will rely more than ever before on energy, telecommunications, and transportation networks. News of false data injections and other cyber attacks together with physical attacks pose great danger. The free and adaptable interactions between different and emerging stakeholder agents in a network provides ease of access and increased productivity but at the same time makes the network vulnerable to attacks. The vulnerabilities can be explored by using an agent-based simulation model which bypasses conventional modelling bottlenecks. One source of vulnerability is the immutability of the record of interactions between the stakeholders. We describe an agent-based network, ostensibly simulating an electricity network, where all interactions are recorded using smart contracts. We use the Mesa ABM platform for creating a community of stakeholder agents who use the Ethereum ecosystem to execute smart contracts. We show how such systems recover after being exposed to a vulnerable situation. Ours is a contribution to secure AI systems.
Hierarchical Multi-turn Dialogue Generation Model Based on Double-layer Decoding

Intelligent and accurate human-machine dialogue systems can help reduce labor costs in business. Existing models of multi-turn dialogue generation, despite their successes, still suffer from lack of contextual relevance and coherence in the generated responses. In this paper, we propose a hierarchical multi-turn dialogue generation model based on double-layer decoding (HMDM-DD) to exploit the positional relationship and contextual information of the dialogues. First, we use relative position embedding to obtain the sequence of context information, then applying the self-attention mechanism to get long-distance dependencies. Finally, we use double-layer decoding to scrutinize the generated dialogue repeatedly. Experiments on two datasets demonstrate that our model has robust superiority over compared methods in generating informative and fluent dialogues.
MEDIA EFFECT ON CRYPTOCURRENCY POPULARITY AND PRICING

The primary intention behind this study is to assess and compare current studies, aggregate datasets and information from already existing investigations and add own datasets for explaining some effects of popularity and pricing of cryptocurrency on Bitcoin and Ethereum examples. The advantages and disadvantages of using cryptocurrency at the present stage of economic development of Ukraine are considered. The results of the research of the awareness of market participants regarding the disadvantages and advantages of cryptocurrency and the presence of interest in the usage of cryptocurrency in range period from beginning to now are given. The aim of this article is to study the possibilities of using cryptocurrency for economic development. The object of the research is the process of using cryptocurrency in the markets of Ukraine and the whole world. The results of a study of the demand for cryptocurrency in Ukraine. The research was conducted with the result of a questionnaire which were taken from previous investigation and compared with own datasets, and as a result of which it was found out: the level of awareness of cryptocurrency; subjective perception of cryptocurrency; the presence of the potential interest of Ukrainians in the acquisition of cryptocurrency; media effect on people interest; cryptocurrency pricing correlation from people interest; sources of awareness of cryptocurrency. It was found what exactly Ukrainians consider the main disadvantages and advantages of cryptocurrency. Cryptocurrency as a tool of payment and a type of electronic money is found in most countries of the world in the “grey” zone, and regulators, if not forbid, then at least do not recommend citizens to invest in such assets. However, the expertise of Ukrainian blockchain developers allows using cryptocurrency in the Ukrainian markets for the effective implementation of world experience.
Pedagogical Diagnostics of Future Preschool Teachers’ Readiness to Develop Preschoolers' Mathematical Skills

The paper justifies the necessity to organize pedagogical diagnostics aimed to identify the level of future preschool teachers’ readiness to develop preschoolers' mathematical skills. The article analyzes the notions “diagnostics” and “pedagogical diagnostics”, describes the specificity and functions of such diagnostics in higher education institutions, reveals the principles of pedagogical diagnostics, provides the examples of their realization in educational process. Scientific originality of the study involves justifying the essence, content and principles of pedagogical diagnostics of future preschool teachers’ readiness to develop preschoolers' mathematical skills. The research findings are as follows: the author identifies criteria of future preschool teachers’ readiness to develop preschoolers' mathematical skills.
Pedagogical Diagnosis Following a Proposed Framework for the Implementation of Educational Robotics

The incorporation of educational robotics in any institution leads to a series of steps that guarantee its effective implementation, the first step is oriented towards the pedagogical diagnosis in the different actors of the educational process, to identify the problems that arise and thus achieve an adequate intervention. A framework is proposed to develop the pedagogical diagnosis by phases according to the maturity degree of the study programs. In addition, the first phase of this diagnosis is directed to gather information about the teacher that allows establishing guidelines for the new educational programs that include robotics.
Detection and Classification of Embung Land Cover using Support Vector Machine

The agricultural sector is the mainstay sector in the economy of Malang Regency. However, Malang Regency has experienced a decrease in rice harvested area caused by drought. One of the Government's efforts to overcome this is by carrying out embung for agriculture. The use of remote sensing technology is one of the practical tools to monitor the phenomenon of change that occurs continuously and in a large area, in this case, the reservoir. This study aims to determine and analyze the use of SVM classification in satellite imagery to detect embung in Malang Regency. This research uses PlanetScope satellite imagery and Support Vector Machine (SVM) to classify land cover types. This research consists of three main tasks: satellite image preprocessing, satellite image classification, and land cover detection. The results showed that the increase in the number of sample areas in the SVM algorithm impacted the computational time and accuracy of the embung classification. The number of sample areas was small, the computation time was 16 seconds, and the accuracy was 0.5641. While the number of sample areas is large, the computation time is 307 seconds, and the accuracy is 0.7093.
Comparative Analysis of Information Retrieval Behavior of Scientific Bibliographic Databases Users

Statistical study of the information retrieval behavior of information resources users, generated by Institute of Scientific Information on Social Sciences of RAN (INION RAN), in the search for scientific information over the past decade for two groups of users: guests and registered users, has been carried out. The results of the study are compared with that of the similar studies made in earlier periods to detect changes and trends. Comparative analysis showed a decrease in the search activity of users with the status of a guest, both in terms of the duration of the search session and the use of unique search operations, while the behavior of registered users has not changed. For the analysis, the following indicators were used: search session duration, number of unique operations per session, use of various databases and interfaces types. Such analysis of the information retrieval behavior is relevant for understanding effectiveness of information retrieval resources. The statistical data can be used to design new search interfaces for various categories of users.
Identifying Optimal Clusters in Purchase Transaction Data

Clustering in transaction databases can find potentially useful patterns to gain some insight into the structure of the data, which can help for effective decision-making. However, one of the critical tasks in clustering is to identify the appropriate number of clusters, which will determine the performance of any process further applied to the transaction database. This paper presents a methodology to discover the optimal structure of purchase transaction data using the Davies-Bouldin and Calinski-Harabasz validity indices to obtain the number of clusters and formed them with the farthest-first traversals algorithm. The quality of the structures previously formed is evaluated with data complexity measures such as F1, F2, F3, N1 and IR. In this work, we use the support vector machine and multi-layer perceptron classification algorithms, to determine recognition ability in classification problems of more than two classes, and in the context of separability and imbalance of classes present in the groups previously obtained. The experimental results exhibit the viability of the proposed methodology for decision-making.
Graph Convolutional Network Exploring Label Relations for Multi-label Text Classification

Multi-label Text Classification (MLTC) aims to learn a classifier that is able to automatically annotate a data point with the most relevant subset of labels from an large number of labels. Label semantics and relationships are important information for multi-label text classification. Existing methods tend to ignore explore high-order dependencies among labels. In this paper, a model called HRGCN (Hop-Residual graph convolutional network) is proposed to capture label dependency and label structure. The hop-connected graph convolutional network can obtain the deep dependence between the labels through a label graph, where the label graph constructed by a correlation matrix and a feature matrix represents the co-occurrence of the labels. Meanwhile, the self-attention mechanism allows to assign different weights to the text features extracted by BiGRU. Fusion of text representation and label representation to form label-text awareness to achieve interaction and generate multi-label classifiers for end-to-end training. Experimental results demonstrate that the proposed model achieves better performance compared to baseline models on the dataset RCV1-V2 and AAPD.
Classification and Recognition Fusion Algorithm Based on Fuzzy Set Theory and Similarity Measure

D-S evidence theory is an imprecise information reasoning decision method most suitable for the application of target recognition, and it is widely used in uncertain information reasoning systems. The data fusion algorithm is one of the key technologies in the recognition and detection system. The data fusion algorithm based on fuzzy set theory and similarity measurement merges multiple classification recognition results, which helps to improve the accuracy of the detection and recognition system, and improve the detection and detection. Identify the reliability and practicability of the system. Use fuzzy set theory to generate reliability distribution function, measure the amount of information of multiple evidence, based on the evidence similarity measurement, revise the source evidence, and constitute the evidence. After conducting experiments on the fusion algorithm for the measured noise classification data of ships, the simulation results show that the fusion algorithm can further improve the trust in the classification and recognition results of the target. The fusion algorithm is feasible and effective, which greatly improves the performance of the original detection and recognition system.
Prevention of Emotional Entrapment of Children on Social Media

Increasing usage of social media and its widespread reaches across the younger as well as older demographics of the population, along with the added advantage of anonymity, has propelled the pressing need to ensure that these platforms remain a secure place for children so as to safeguard them from issues like child grooming. Over the last few years, the rate of online grooming has seen a massive increase along with an increase in the number of teenage users. The proposed system, Prevention of Emotional Entrapment of Children (PEEC), consists of an age detection module using Deep Neural Network (DNN) as well as a grooming detection module using Support Vector Machine (SVM) to monitor the conversations of only those users who come under the category of minor (below 18). The resultant system gave an accuracy of 84% for age detection and 91% for grooming detection.
Using Blockchain to Coordinate Federal Processes: The Case of Germany’s Federal Office for Migration and Refugees

(a) Situation faced: The German asylum procedure requires close cooperation and information exchange between various authorities at the municipal, state, and federal levels. Federal separation of competencies inhibits the delegation of process governance to a central authority such as the Federal Office for Migration and Refugees (BAMF). This separation also leads to regional differences as federal laws govern the procedure’s general steps, whereas state laws govern implementation. Moreover, existing solutions for cross-organizational collaboration are limited in terms of flexibility, security, and data quality. As a result, the exchange of certain data on asylum procedures still occurs using Excel spreadsheets and e-mails. (b) Action taken: Against this backdrop, the BAMF explored technological options that would support the decentralized governance of the asylum procedure. After a preliminary evaluation, the BAMF decided to explore a solution based on blockchain technology. Building upon a successful proof of concept, the BAMF initiated a pilot project with Saxony’s central immigration authority. This project aims to develop a blockchain solution that supports the coordination of asylum procedures and can be easily adapted to local differences and functional requirements. (c) Results achieved: The use of the blockchain solution allows for efficient, secure, and timely distribution of status information. It supports communication and improves coordination between authorities. Despite an apparent conflict between blockchain principles and data privacy requirements, the BAMF’s design complies with relevant regulation (notably the GDPR). As a first-of-its-kind project, it outlines best practices and provides valuable insights into opportunities and challenges arising from the use of blockchain in the public sector. (d) Lessons learned: The BAMF’s case demonstrates that blockchain solutions can be promising alternatives when the delegation of process governance to a central party is not desirable and when federal principles of organization are to be reflected technologically. However, blockchain projects require special attention to managing know-how and capabilities, software development activities, stakeholders, the regulatory context, and cross-organizational governance.
An Interdisciplinary Approach to Training Teaching Skills for Intercultural Communicative Competence

Although intercultural interaction permeates the entire educational system, the introduction of intercultural education at all educational levels is a slow and complex process. Particularly, the intercultural communicative competence (ICC) development among preschool and primary school children requires peculiar teachers’ knowledge and skills to form pupils’ ICC. The purpose of this article is to analyze the process of developing undergraduates’ ICC, and their methodological knowledge and skills in ICC formation in preschool and primary school children. The methodological basis of the work consists in axiological, interdisciplinary, and communicative approaches. The common goal of the research is to develop intercultural skills of future teachers who are ready, on the one hand, to deal with children from interculturally diverse back-grounds, and on the other hand, to prepare children to act in multiculturally diverse society. The study involved 167 students enrolled in the Bachelor programs of Pskov State University. The empirical research included three stages of the experiment. The content of the examination included complexes of interactive methods, with the help of which it was supposed to strengthen the formation of the ICC of bachelors, the ability to work in a team and the development of a set of instrumental competencies together with the development of the methods of teaching ICC to schoolchildren. The practical result of the study is a set of business games facilitating the undergraduates’ intercultural communicative competence development as well as their ability to develop the ICC in preschoolers.
ACFIM: Adaptively Cyclic Feature Information-Interaction Model for Object Detection

Object detection is one of the most fundamental tasks toward image content understanding due to their wide applications in real-world. Although numerous algorithms have been proposed, implementing effective and efficient object detection is still very challenging for now, especially for the challenges in restricted situations of multi-size objects and weak semantic information. In this paper, we propose a feature information-interaction visual attention model for multi-layer feature fusion and enhancement, which utilizes channel information to weight self-attentive feature maps, completing extraction, fusion and enhancement of global semantic feature with local contextual information of the object. Additionally, we also propose an adaptively cyclic feature information-interaction model, which adopts branch prediction to decide the number of visual attention, accomplishing adaptive fusion of global semantic feature and local fine-grained information. Numerous experiments on the benchmark dataset PASCAL VOC and MS COCO show that our method effectively achieves significant improvements over baseline model.
Recommending assembly points, evacuation routes, and standard operating procedures for potential flooding due to reservoir dam failures: a case study of Gondang Reservoir, Lamongan, East Java

Abstract The failures of water-retaining structures, such as reservoirs and lakes, may cause massive flooding. Located in the Gondanglor village, Lamongan, East Java, the Gondang reservoir is approximately 30 years old. Currently, the village does not have an official evacuation Standard Operating Procedure (SOP), a guide needed during disaster emergency responses, including the risk of reservoir failures. The absence of the SOP means that the community members have not prepared themselves to face any hazard that threatens their safety. This research aims to recommend assembly points, evacuation routes, and Standard Operating Procedures (SOP) as inputs for the preparedness of reservoir dam failures. Criteria used to determine the assembly point locations are the minimum space per person, accessibility of private vehicles, access to safer places, either a street or open space, minimum distance from buildings, accessibility of disaster response vehicles, safe from falls, and other hazards. The evacuation routes consider road networks and conditions, movement directions, and distance between starting and assembly points. The evacuation SOP focuses on communication flows. Community members and Gondang reservoir’s officers are interviewed, and studies related to evacuation procedures are reviewed to help accomplish the research aim. This study has successfully recommended assembly points, evacuation routes, and SOPs for evacuating villagers when the potential of Gondang reservoir failures is identified. Community members and village officers of Gondanglor were involved in the designing of assembly points, evacuation routes, and the SOP. The study has suggested five assembly points and the shortest possible routes to reach the points from varying locations in the village. SOP suggested communication flows during varying emergency stages, including the “Abnormal” (Level 1), “Alert” (Level 2), “Alert” (Level 3), or “Warning” (Level 4) status. The results are expected to increase the preparedness level of village members to reservoir dam failures.
Analysis of Digital Population Services for the Poor in Palembang City Using the Information Technology Infrastructure Library (ITIL) Framework

As it is known that the city government of Palembang has implemented e-government, but how much e-government can help the Palembang city government to interact between the government and all levels of society including the poor in Palembang city. In its application, the condition of e-government services is still insufficient for the infrastructure to support e-government services for the poor in Palembang city. To design e-government services such as good digital population services, it is necessary to design IS / IT services for people in Palembang City with the Service Design stage in the 2011 Version of the Information Technology Infrastructure Library (ITIL) Framework so that they can be accepted by all levels of society including the poor in Palembang city. This study uses a qualitative approach. The results of this research are in the form of a digital population service information system design such as administrative and population services, Family Hope Program (PKH) services and digital archive storage services based on the Service Design stages in ITIL Version 2011 that have been carried out, namely the Service Catalog Management process and Service Level Management in the form of documents. Service Level Requirements, Service Level Agreement and Operational Level Agreement.