Reviewerly/exHarmony huggingface dataset:
Evaluation of the efficiency of teaching future informatics teachers in computer networks based on modeling of networks.

When organizing training in computer networks on real equipment, educational institutions face organizational, technical and material difficulties. Using network modeling, the above difficulties can be avoided in teaching future informatics teachers about computer networks. The aim of the research was to prove the effectiveness of teaching future informatics teachers to computer networks based on network modeling. The method of theoretical analysis is carried out in order to comprehensively study the state of the problem under consideration. A pedagogical experiment has been conducted for evaluating the efficiency of teaching computer networks based on modeling. The study has been organized in experimental and control groups. Mathematical statistics were used for the analysis. In the course of the study, it was found that the development and implementation of the proposed teaching methodology provides an opportunity for full and high-quality training of future informatics teachers in the field of computer networks, which allows them to solve professional problems in the design, maintenance, configuration and administration of computer networks.&#x0D; Keywords: programming environment; informatics teacher; network technologies; teaching methods; informatization.
Optimization Method of Web Fuzzy Test Cases Based on Genetic Algorithm

Abstract In order to solve the problems of low code coverage, few vulnerabilities found, and poor fuzzing effect caused by the small number of test cases and single types in Web fuzzing, on the basis of studying the current Web fuzzing methods, the existing fuzzing Web applications are tested Program research. A genetic algorithm-based method for optimizing fuzzing test cases for Web applications is proposed. It analyzes and counts the traffic of public network website business with Web service attack characteristics, and uses genetic algorithms to generate a large number of test cases with various types to explore the Web service vulnerability that exists. Based on the creation of a Web attack signature database with weights, this method uses genetic algorithms to randomly pre-generate the test cases of the fuzzing test, and uses the response of the Web service to repeatedly iterate the weights of different attack signatures in the Web attack signature database. So as to generate the best test cases. Experimental analysis shows that this method effectively finds security vulnerabilities in Web applications.
A Comparative Study on Anonymizing Networks: TOR, I2P, and Riffle Networks Comparison

Background and Objectives: Among miscellaneous networks, onion-based routing network technologies such as The Onion-based Routing (ToR), Invisible Internet Project (I2P), and Riffle networks are used to communicate anonymously by different worldwide users for security, privacy, and safety requirements. Sometimes, these types of networks sacrifice anonymity for the sake of efficient communication or vice-versa. This paper surveys aforementioned networks for investigating their potential and challenges.Methods: Onion-routing networks encapsulate messages in several layers of encryption similar to layers of an onion. The anonymous communication networks are involved dining cryptographers (DC) problem so-called DC-nets, which need sending anonymous message with unconditional sender and untraceable receipt. So, DC-nets must be resistant against traffic analysis attacks although they will attenuate the network bandwidth. In this line, ToR is a free software that provides anonymous communication, I2P networks are based on hidden internet service project which uses tunnelling for anonymous communications, and Riffle networks include a small set of camouflaging servers that provide anonymity for authorized users. This paper presents a comparative study on anonymizing ToR, I2P, and Riffle networks in terms of associated prominent parameters in this vein.Results: The comparison is based on similarities, differences, and challenges in network behaviors. This comparison is beneficial for further researches and future improvements.Conclusion: The review of the current paper reveals that the Riffle networks are more resilient and have great confidentiality and integrity against other onion-based routing networks.
Performance Evaluation of Hybrid Machine Learning Algorithms for Medical Image Classification

Medical imaging is the process of creating images of parts of human body for diagnosis and treatment purposes. These images are collected from traditional X-ray based methods like Mammography and Computed Tomography (CT). Some advanced sources of images include Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Since large volumes of digital medical images are deposited in repository, it is a humongous task to access these voluminous images. To access the required image representation from data store, a technique called Content Based Image Retrieval (CBIR) is currently in use. Content-based image retrieval (CBIR) is an assembly that can overcome the problem mentioned above as it is based on the visual analysis of contents that are part of the query image. CBIR retrieves the images which are needed based on its visual contents. CBIR includes Feature extraction and Feature matching. In feature extraction, information like colour, texture and shape known as feature vectors are retrieved through various extraction methods. Similarly, in feature matching the extracted features are compared between normal and abnormal images for classification. The major challenge in CBIR is implementing flexible methodologies to process the different images of different characteristics like colour, shape and pattern. At the same time, applications for retrieving images for proper indexing is done through Picture Archiving and Communication Systems (PACS). In this chapter, retrieving medical images from different data stores and performance of various machine learning classifiers such as Support Vector Machine (SVM) and Deep Learning methodology are focussed to improve the classification accuracy.
Information about Russian Research Organizations in Multilingual Data Sources

International and Russian-language data sources that provide information about Russian research-related organizations are considered. It is demonstrated that Russian-language data sources contain more information about Russian research-related organizations than most international data sources, but this information remains unavailable for English-language data sources. Experiments on comparison and integration of information about Russian research organizations in international and Russian data sources are outlined. Data sources such as GRID, Russian and English chapters of Wikipedia, Wikidata and eLIBRARY.ru are considered. The work is an intermediate step towards the creation of an open and extensible knowledge graph.
Professional pedagogical formation of the competence of the teacher-manager

Recent studies have considered the practical aspect of professional and pedagogical competence formation of students of pedagogical specialties as future managers of the educational environment. Aim of the research is to develop theoretical foundation, methodology and psychological experimentation of psychological and pedagogical conditions for formation professional competence of managers in educational system. The research methods include description and classification, semantic data interpretation, methods of graphical representation of data, ranking as well as content analysis. The received results can be used in the development of competence of psychological and pedagogical field of knowledge, ability to have an active influence on the development process and self-development of social and valuable characteristics of the personality of future expert of the educational environment.&#x0D; Key words: Competence; Communicative competence; Pedagogical education; Multilevel training; Teacher-manager.
SVAD: End-to-End Sensory Data Analysis for IoBT-Driven Platforms

The rapid advancement of IoT technologies has led to its flexible adoption in battle field networks, known as Internet of Battlefield Things (IoBT) networks. One important application of IoBT networks is the weather sensory network characterized with a variety of weather, land and environmental sensors. This data contains hidden trends and correlations, needed to provide situational awareness to soldiers and commanders. To interpret the incoming data in real-time, machine learning algorithms are required to automate strategic decision-making. Existing solutions are not well-equipped to provide the fine-grained feedback to military personnel and cannot facilitate a scalable, end-to-end platform for fast unlabeled data collection, cleaning, querying, analysis and threats identification. In this work, we present a scalable end-to-end IoBT data driven platform for SVAD (Storage, Visualization, Anomaly Detection) analysis of heterogeneous weather sensor data. Our SVAD platform includes extensive data cleaning techniques to denoise efficiently data to differentiate data from anomalies and noise data instances. We perform comparative analysis of unsupervised machine learning algorithms for multi-variant data analysis and experimental evaluation of different data ingestion pipelines to show the ability of the SVAD platform for (near) real-time processing. Our results indicate impending turbulent weather conditions that can be detected by early anomaly identification and detection techniques.
CSR Keyword Trend Analysis Using Topic Modeling

As the interest in ESG has been rising due to the COVID-19, social requests on Corporate Social Responsibility(CSR) grow rapidly. In this study, sustainability report - a non-financial report of the corporation including eco-friendly social activities - was used to analyze the CSR with the unstructured dataset. In order to utilize the unstructured dataset, this research analyzed the CEO’s greetings in sustainability report and CSR issue keywords in 2013, 2016, and 2019 through text-mining and topic modeling, separately. CSR issue keywords in 2013 were share, principle, production, satisfaction, and nation. Keywords in 2016 were opportunity, humanity, ethical management, excellence, and warmth. Keywords in 2019 were digital, sustainability, partner, electricity, and hydrogen. These results indicate that the concept of CSR has been expanded. CSR includes not only the concept of responsibility for social contribution, but also the environment, climate change, ownership structure, and win-win cooperation. Thus, due to the expansion of social responsibility, the importance of sustainability report will gradually increase when the corporation resolves the social problem. This study is expected to be used as basic data related to CSR.
PENGARUH EFEKTIVITAS PEMBELAJARAN DARING TERHADAP PRESTASI BELAJAR PESERTA DIDIK DALAM MATA PELAJARAN AQIDAH AKHLAK

The research aims to know possitive effects of online learning effectiveness on student achievements in MTs Serba Bakti Suryalaya. The used methode of this research is descriptive method through quantity approach with a population at 510 people, a sample of 26 people by using data collection technique such as questionnaire, observation, and documentation. To test variable x and y it’s done by determining mean value that interpreted by using interpretation data classification, and to relationship between varible x and y by using Rank Spearman correlation analysis technique. Based on data processing result, it’s obtained that the influence of online learning effectiveness on student achievement in MTs Serba Bakti Suryalaya have significant possitive effects with low category, it’s proven that rs= 0,23 is on interval 0,21 – 0,40 and t hitung (34,92) is more than t tabel (1, 711). The effectiveness of online learning determines student achievement by 53% and 47% is determined by other factors. It’s suggested for other researchers to do research by learning other factors that take effect to student achievement.
First-Order Rewritability and Complexity of Two-Dimensional Temporal Ontology-Mediated Queries

Aiming at ontology-based data access to temporal data, we design two-dimensional temporal ontology and query languages by combining logics from the (extended) DL-Lite family with linear temporal logic LTL over discrete time (Z,&lt;). Our main concern is first-order rewritability of ontology-mediated queries (OMQs) that consist of a 2D ontology and a positive temporal instance query. Our target languages for FO-rewritings are two-sorted FO(&lt;) -- first-order logic with sorts for time instants ordered by the built-in precedence relation &lt; and for the domain of individuals---its extension FO(&lt;, ≡) with the standard congruence predicates t ≡ 0 (mod n), for any fixed n &gt; 1, and FO(RPR) that admits relational primitive recursion. In terms of circuit complexity, FO(&lt;, ≡)- and FO(RPR)-rewritability guarantee answering OMQs in uniform AC0 and NC1, respectively. We proceed in three steps. First, we define a hierarchy of 2D DL-Lite/LTL ontology languages and investigate the FO-rewritability of OMQs with atomic queries by constructing projections onto 1D LTL OMQs and employing recent results on the FO-rewritability of propositional LTL OMQs. As the projections involve deciding consistency of ontologies and data, we also consider the consistency problem for our languages. While the undecidability of consistency for 2D ontology languages with expressive Boolean role inclusions might be expected, we also show that, rather surprisingly, the restriction to Krom and Horn role inclusions leads to decidability (and ExpSpace-completeness), even if one admits full Booleans on concepts. As a final step, we lift some of the rewritability results for atomic OMQs to OMQs with expressive positive temporal instance queries. The lifting results are based on an in-depth study of the canonical models and only concern Horn ontologies.
COVID-19 Safe Guard: A Smart Mobile Application to Address Corona Pandemic

The novel coronavirus, COVID-19, has been declared as a pandemic all across the world by the World Health Organization (WHO). World lockdown and social distancing has been considered as ideal tools to prevent community transmission. That too, in a country like India which is densely populated, it is very difficult to prevent the community transmission even during lockdown. The people are forced to move out of their houses during this pandemic for emergency purposes. The people moving out during this pandemic situation for unavoidable situations, i.e., to get their groceries, for medical emergencies and so on, have the probability of getting infected by COVID-19. Also in case of some medical emergency, people may take the path which is blocked and return back and again take a different route. The people also does not know whether a particular path is blocked until they travel through the specific path. This might put the life of person in danger. The proposed mobile Android application gives the safest route and notifies the user if the path the user chose is blocked in advance. This saves a lot of time of the user by preventing the user to move, to and fro the blocked areas and then choosing a different route. This application also help those who is forced to move out during for a medical emergency. The proposed Android application helps the people to get updates regarding COVID-19 in their locality and make them feel safe during this pandemic.
ENSINO DE LÍNGUAS MINORITÁRIAS: UMA REVISÃO DE LITERATURA SOBRE O TALIAN

This work presents a literature review of studies involving Talian, a Brazilian minority language spoken by Italian immigrants. Although Brazil has received several immigrants since the arrival of the Portuguese in the 16th century, languages other than Portuguese are seldom given any recognition in schools, being categorized as minority languages. Thus, further investigation on Talian has proved necessary in order to point out research in the area and relate them to current educational language policies. The literature review reveals that there are several studies that have investigated Talian, mainly under the perspective of sociolinguistics. It is observed that the teaching actions of this minority language are the result of individual actions and not municipal policies.
A modified constraint for iterative phase-only multiplane hologram generation

We introduce a modified hologram plane constraint to the global Gerchberg-Saxton algorithm for improved multiplane phase-only hologram generation. We demonstrate a 17% increase in the reconstruction quality compared with previously reported methods.
Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions

Enabling open-domain dialogue systems to ask clarifying questions when appropriate is an important direction for improving the quality of the system response. Namely, for cases when a user request is not specific enough for a conversation system to provide an answer right away, it is desirable to ask a clarifying question to increase the chances of retrieving a satisfying answer. To address the problem of ‘asking clarifying questions in open-domain dialogues’: (1) we collect and release a new dataset focused on open-domain single- and multi-turn conversations, (2) we benchmark several state-of-the-art neural baselines, and (3) we propose a pipeline consisting of offline and online steps for evaluating the quality of clarifying questions in various dialogues. These contributions are suitable as a foundation for further research.
Effectiveness Evaluation of Reconnaissance Satellite System based on Analytic Hierarchy Process and Cloud Model

Considering the complexity of effectiveness evaluation index of reconnaissance satellite system (RSS), the cloud model theory and AHP method are combined. A fuzzy evaluation method of combat effectiveness based on cloud model is proposed. A multi-layer evaluation index system is constructed through the AHP method, and the weight of each level index is calculated. Combining the multi-dimensional cloud model with the AHP method, a quantitative fuzzy evaluation method for qualitative indicators and quantitative data is designed, and the qualitative evaluation results are obtained. The experimental results show that the proposed evaluation method can effectively realize the fuzzy quantitative evaluation under the complex evaluation index system with good performance in terms of calculation accuracy and fuzziness.
Neuromorphic Photonics for Intelligent Signal Processing

Neuromorphic photonic processors create a radically new hardware platform for intelligent signal processing by offering sub-nanosecond latency and high bandwidth with low energy. Here, we discuss the current development of neuromorphic photonics and its applications in real-time processing of multichannel, ultrafast signals.
Towards Knowledge Organization Ecosystem (KOE)

Increasingly, it has been hard to ignore that standalone KOSs are becoming functionally ineffective components for knowledge-based AI systems, given their inability to capture the continuous faceti...
An Intestinal Centerline Extraction Algorithm Based on Federated Framework

The intestine is an important organ of the human body, and its internal structure always needs to be observed in clinical applications so as to provide a basis for accurate diagnosis. However, due to the limited intestinal data obtained by a single institution, deep learning cannot effectively train the intestines, and the effect is not satisfied. For this reason, we propose a distributed training method to carry out federated learning to alleviate the situation of patient sample data shortage, not shared and uneven data distribution. And the blockchain is introduced to enhance the interaction between networks, to solve the problem of a single point of failure of the federated learning server. Fully excavate the multiscale features of samples, to construct a fusion enhancement model and intestinal segmentation module for accurate positioning. At the local end, the centerline extraction algorithm is optimized, with the edge as the main and the source as the auxiliary to realize centerline extraction.
Digital Technologies’ Impacts On Student Social Adaptation During Coronavirus Pandemic

The coronavirus pandemic has caused a rather difficult period of adaptation of students to the university system and the new educational process. Digital technologies came to the rescue, which contributed to some solution of emerging adaptation issues for nonresident students. This article is aimed at identifying the features of social adaptation of nonresident students to the educational process at the university. As a research method, the questionnaire method was used, which allowed to identify and analyze the peculiarities of adaptation of first-year students from other cities to the university environment and university requirements. The article reveals the influence of digital technologies on the adaptation of students to the university environment and new living conditions. It was determined that, in general, the participants have a high level of adaptation to the university environment, even in the conditions of the coronavirus pandemic.&#x0D; Keywords: digital technologies; educational environment; social adaptation; student youth
Investigation of The Relationship Between Bitcoin and Gold Prices With The Maki Cointegration Test

The gold mine has been a commodity used for thousands of years, today it is also an investment tool with the highest reliability. However; cryptocurrencies that are recently used are affecting our portfolio. Bitcoin is the most traded cryptocurrency. Since there are alternative investment instruments involved in portfolios, the relationship between these two independent values inspired the emergence of this study. The aim of this study was to investigate whether there is a causality-cointegration relationship between daily Bitcoin prices and gold prices for the periods between 10,01,2014 and 11,12,2020. In the application section, Toda Yamamoto causality and the Maki Cointegration test were applied. According to the results of the Toda Yamamoto causality test, there is a two-way causality relationship. According to the results of the Maki cointegration test, there was no long-term relationship between the series. As a result, it is expected that in the long term, investors will have a risk-reducing effect by including both investment instruments in the same portfolio.
Accessibility Analysis of the Bosnia and Herzegovina Universities' Websites in the Relation to Their Position on the Webometrics Ranking List

In today's digital age, presence on the Internet and the Web mean presence in general life. This is generally more pronounced in the context of higher education because these institutions are the primary drivers of overall social development. The quality and highly accessible website of each university is a must as these websites are generally the first contact and first impression for new potential students and also the first step for establishing cooperation with the social economy and society. For this reason, websites need to meet some of the basic standards when it comes to accessibility of the content they provide and it is essential to ensure that content on the websites can be accessed by people with disabilities. This paper deals with the analysis of websites of universities in Bosnia and Herzegovina in the context of their accessibility, from one side, and their position on the international scene in the context of the Webometrics list of universities, on the other side. Based on the obtained results and conducted analysis, recommendations were given on how to improve some of the elements on websites in order to make these websites more accessible in accordance with the standards related to this topic.