Reviewerly/exHarmony huggingface dataset:
Specificity of Decentralized Autonomous Organizations as the Implementation of Blockchain Technology

Decentralized Autonomous Organization (DAO) is a new form of organization, the innovation of which consists in the use of blockchain technology to record the applicable rules governing the project as well as the rights and obligations of its members, usually investors. Moreover, the method of financing the project is also new here, since tokens (cryptocurrency), which are acquired by investors for the capital contributed, are most frequently used. Management in DAO is previously unheard of (until the creation of DAO-2016), since there are no governing bodies, and all decisions are made by the voting of all members of the organization. In the article, an attempt is made to assess the DAO management process, having previously analyzed blockchain technology and the specificity of this new form of organization. The own research was conducted on a selected DAO project, when analyzing the management process implemented by voting by the members of this organization from its launch (May 2022) to 11 May 2023. The obtained results confirmed the dominance of majority shareholders in DAO votes, although the method of voting (Qudratic voting) used in the project under study to protect the rights of small shareholders significantly reduced this impact. The unusual unanimity of decision-makers was also noted (as much as 65% of unanimous votes), which, as it turned out, could be the result of information campaigns conducted before each vote by groups of small investors deeply involved in the project. These activities should also translate into the accuracy of the decisions made, and thus the quality of the DAO management process.
Blockchain in OER: students' perspectives on the use of blockchain technology for credential authentication

The article outlines the potential benefits of blockchain technology in the education sector, particularly in open educational resources. The literature on open educational resources, blockchain technology and credential authentication is reviewed. The potential benefits and possible challenges of adopting blockchain technology to the process of verifying and authenticating students' academic achievements, which are crucial to ensure their credibility and recognition in other institutions as well as in the labour market, were presented. The main objective of this article was to provide students' perspectives on their familiarity with ORE, blockchain technology and how it can be adopted to verify educational credentials. The author of the article highlights the new benefits of implementing blockchain technology, primarily in terms of verifying the authenticity of diplomas and educational achievements, increasing their value and credibility in the eyes of employers. The introduction of these technologies can influence a more transparent teacher evaluation process and ensure secure storage of learner data. The results of this study indicate the potential of blockchain technology in enhancing the credibility and authenticity of academic achievement in OER. The study highlights the importance of further education and awareness initiatives, dealing with technical complexities and addressing data privacy and security issues to ensure the successful integration of blockchain into the education system. Through the use of distributed registries, a secure and transparent platform can be created to share and verify educational credentials.
Comparative analysis of selected algorithms for qualitative data clustering

Data clustering can apply to both numeric and categorical attributes. The numerical characteristics of data are all such values that can be written as digits. This work is not directly devoted to this category of attributes. The research's primary and most crucial goal is to compare the clustering processes, the quality of the obtained clusters, and the clustering results between the selected algorithms for clustering numerical sets (using coding techniques) and qualitative data sets. Design experiments were performed on three datasets. The algorithms used to group the observations into classes were the k-means algorithm and the kmodes algorithm. Three different distance determination measures were used in the case of the k-means algorithm - the Euclidean measure, the urban measure, and the Chebyshev measure. It can be concluded that for the structures of the three selected data sets, the k-modes algorithm presents better results of the clustering quality and thus more effectively distributes observations to classes. It also does it in a much shorter time than the k-means algorithm. The keyword used here, however, is dataset structure.
The similarity of the similarity measures in the context of clustering algorithms for categorical data

This research aims to assess the similarity between determining the value of the similarity of objects by using various measures. Particularly for categorical data, the used similarity measures may behave differently depending on whether the analyzed data are binary, non-binary, or mixed. In the case of categorical data, it begins to matter how many attributes we have and how many different values they can take. Cluster analysis is an essential technique in unsupervised machine learning. Object-clustering cannot be determined without determining similarities between objects in the set and searching for clusters with the most significant internal consistency and external separatory. Therefore, we need knowledge about which measure behaves similarly and which differs depending on the nature of the analyzed data. According to our knowledge, there is no such research in the literature. In research, we analyzed six different measures for eight different datasets (differing in size and data type). The results say, among others, which measures take the longest time for calculations and which are the most correlated with others, so they can be used interchangeably without fear of losing valuable information about the mutual similarity of objects.
Domain Adaptation Approach for Arabic Sarcasm Detection in Hotel Reviews based on Hybrid Learning

Posting sarcastic comments on hotel opinion websites became a common trend. Therefore, sarcasm detection becomes of primary importance, mainly since sarcasm can flip the review's polarity. To our knowledge, no work related to detecting sarcasm exists in the hospitality industry. The lack of a labeled dataset is an utmost issue for researchers. We propose a new approach based on the domain adaptation using the self-training technique for the semi-supervised learning of sarcastic Arabic hotel reviews. We use AraBERT for contextual embedding and machine learning models for self-training classification. The obtained accuracy reaches 91.1%.
Implementation of Anonymization Algorithms for Log Data Analysis on a Cloud-Based Learning Management System

In this study, we aimed to construct a system to perform statistical analyses for educational improvement while preserving privacy. Various probabilistic statistical algorithms have been developed for data agitation. However, one of the major challenges in using these algorithms is determining appropriate parameters. To address this issue, our previous research proposed the “α-criterion” as a criterion that must be satisfied by appropriate parameters. This paper outlines a system that calculates the parameters that satisfy the α-criterion while performing real-time data agitation on the given aggregate data. To verify that our implementation actually works, we show a use case with sample data on kibana-Elastic log analysis provided by Elastic N. V.
Improving essential knowledge and self-efficacy in computers network course: The potential of chatbots

In university-level education, the computer network is a mandatory course for computer science and engineering majors. A computer network course provides theoretical and hands-on foundations for network connectivity, network topologies, internet protocols, networking devices, and gateways. In the traditional classroom, students face challenges in gaining essential knowledge and self-efficacy. Teachers face challenges in assessing students' quality of knowledge due to the abundance of online computer science teaching resources. Chatbot technologies have the potential to overcome these challenges and enhance essential knowledge and self-efficacy. However, the argument remains whether a course-specific standard chatbot or a generalizable chatbot could produce high-quality answers. Therefore, in this paper, we research 1) to what extent does using chatbots improve higher education students' essential knowledge of computers network? and 2) to what extent does using chatbots improve the self-efficacy of higher education students? Our first insight indicates that groundbreaking generalizable chatbots such as You and ChatGPT produce rather detailed answers to the questions associated with computer network courses, but those answers may not be the model answers that teachers expect from the students. Hence, research-based course-specific standard chatbots are required to teach computer network topics effectively.
Best Practices for Knowledge and Quality Management in IT Projects

With the ever-increasing demand for development and innovation, organizations must prioritize quality management aspects to ensure the quality of their online products and services. This includes implementing effective test management strategies to identify and resolve issues before they impact end users. The aim of this research is to underscore the need for a robust test management plan to ensure stable and trustworthy web services. As such, it proposes best practice solutions for organizations in the field of quality control and testing, which have a high impact on improving knowledge management. This study used an observational approach to assess the importance of quality assurance in IT projects and its impact on knowledge management. The findings presented in this paper highlight the critical role that quality control and testing procedures play in promoting effective knowledge management. Furthermore the outcomes of the study show that in order to deliver world-class software and services, organizations must prioritize effective test management and continuous quality assurance.
Adaptive dwell time for eye typing

The presented studies investigated the possibility of eye movement usage for entering text using on-screen keyboards. The attention was focused on the keyboard layout and the time of typing a character. Three dwell times were used to select keyboard keys: 2-second, 1-second, and adaptive. The last of them required the development of an appropriate algorithm to adjust the character choice to the user's skills and to facilitate the use of the software. The paper analyzes whether the keyboard type and character selection time affect the text input speed and the number of errors made. During the experiment, in which seven people participated, the text consisting of approximately 70 characters was entered. This operation was repeated for each keyboard layout and dwell time. The studies have confirmed the reduced error number and the greater comfort for the adaptive selection solution.
Application of creative problem-solving methods in remote learning. Bibliometric analysis

Educational processes have undergone significant evolution over recent years. The evolution of educational processes is a source of consideration in numerous academic and professional publications. This has been influenced on the one hand, by information and communication technologies, the development of which has affected all areas of human activity; on the other hand, expectations regarding the content of educational processes as well as the emphasis on the development of soft skills have changed significantly. At the interface of these two predictors influencing the evolution of educational processes, the question arose: how often is the use of creative problem-solving methods in educational processes addressed in the literature, and is this trend maintained in the case of processes using advanced ICT? The above determinants influenced the choice of the aim of the research conducted by the authors of this article, which is to identify the range of publications and trends in scientific studies devoted to techniques, tools, and approaches to the implementation of educational processes.
Job Seeker Recommendation for Employers: A Graph-Based Recommendation Approach Using Node Embedding

Through the growth and development of online recruitment platforms, thousands of individuals can see vacancies promoted by companies. The fact that hundreds of job seekers are applying for a single position prolongs the hiring process for companies and increases the need for human resources to review these applications. In this study, a graph-based recommendation system is suggested as a solution to the issue that many companies are now experiencing. Node embedding and feature representation methods are explored and the link prediction successes of these methods are examined, by associating the candidates that applied for the same jobs in a graph structure.
Technical Measures for Prevention of Theft Committed with the Use of Information and Telecommunication Technologies

Theft committed with the use of information and telecommunication technologies has taken a lead in cybercrimes and is a major concern of developing countries. Provision of effective prevention activity in the sphere under study poses a serious problem. Its solution depends on a systematic approach to resolving organizational and legal aspects of social prevention of this type of theft. The specificity of IT-theft (stealing and fraud) calls for correction of established methods of special prevention measures and building new approaches to such activity (technologisation of the process). The article allocates a set of major perspective technical measures for prevention of IT-theft aimed to develop and implement a single monitoring system (data mining and extraction of useful data from total data volumes); the fight against anonymity (the possibility of de-anonymization of users is considered). The study also highlights countering fraudulent spam (the possibility of using the technology of «gray lists», the introduction of a specialized tariff plan when using e-mail as well as the development of domestic software based on individual Bayesian filters capable of analyzing the statistics of the occurrence of words in the texts of fraudulent messages). The main directions of practical implementation of the proposed approaches are determined. In conclusion, the author emphasizes the fact that the application of these technical preventive measures will allow the entire cybercrime prevention system to be holistic and logically complete.
A Blockchain-based approach for secure IoT

The Internet of Things (IoT) has recently taken on a crucial role in numerous areas of our life, offering convenience and efficiency. One notable area of its application is in the growing adoption of smart home applications. This rapid growth has emphasized the pressing need to enhance the IT infrastructure to ensure the transparency, security, and privacy of user data. In this regard, blockchain technology has emerged as a promising solution to fulfill these critical demands. Hence, this study suggests an approach utilizing Hyperledger Fabric to enhance the security of smart home systems through blockchain technology. The proposed solution addresses the security limitations often encountered in existing permissioned blockchain approaches. The architecture consists of four layers: Cloud Storage Layer, Blockchain Platform Layer, Application layer and IoT Devices Layer. We have integrated a cloud storage layer to take benefit of its inherent advantages in terms of efficiency and availability. Smart home devices frequently require computing resources and substantial storage capacity, both of which can be effectively provided by the cloud.
Extending LUNAR method with COF chaining distances

A new method called LUNAR uses a neural network to combine various local outlier methods and show that they are specific instances of a more general message-passing framework used in graph neural networks. LUNAR creates a graph using K-nearest neighbors and does not incorporate the chaining distance used by COF, a connectivity-based outlier factor that measures how much a sample deviates from a pattern. COF improves the efficiency of the LOF method when the density of a pattern's neighborhood is comparable to that of an outlier. This work proposes a modification to the LUNAR method to unify the local outlier methods it currently uses and COF. Adding nodes of chain used by COF to the LUNAR might enhance its effectiveness without a drastic increase in computation time since it might be easily calculated after the K-nearest neighbors search. Thus, this study provides an exhaustive comparison of the LUNAR method and proposed modification in terms of graph neural network model learning efficiency.
Cybersecurity in SMEs – Drivers of Cybercrime, Insufficient Equipment and Prevention

Following scientific contribution bundles results from the research project "CyberWuP", which is supported by the Ministerium des Inneren, für Digitalisierung und Kommunen Baden-Württemberg (Germany). Results on three research questions of related studies are presented. Key data on the current equipment and know-how regarding cybersecurity in SMEs as well as information on the required support of SMEs are provided. In addition, a structural equation model is used, which shows the significant positive influence of "motives for crime" and "lack of prevention" on cybercrime as a threat to SMEs.
Interaction Modelling of High Dimensional Production Data

The final goal of this study is to present a practical interaction modeling methodology for big and complex industrial data in which there are mixed difficulty of high dimension, sparsity, interaction, multicollinearity, series bias, low signal noise ratio, nonlinearity, and so on. Particularly, this paper focuses on the issues of high dimension, sparsity, and interactions. In addition, it is necessary to estimate unknown factors and interactions in order to improve overall efficiency in complex and diverse situations of industry. The main purpose here is to summarize the applicability and challenges of Factorization Machines (FMs) to manufacturing data. FMs has possibility because of the ad-vantage of to identify unknown factors and its robustness to data with more than 95% missing values which has already been reported for recommendation problems. Manufacturing data assumed here incudes the inspected quality of the final products, and a set of various variables (settings, sensing results, etc.) of the series production process which may relate to the quality. Common characteristics of manufacturing data include mixture of numerical and categorical variables, high dimensionality, many missing values, and so on. Therefore, this paper particularly focuses on sparse high-dimensional type-mix data. So far, while interaction modeling of high-dimensional data has been discussed and verified, mainly for screening methods, still there is very few studies to apply FMs to the manufacturing data with many numerical variables. There-fore, we report the evaluation results using some synthesis data and simulation data from actual oracle model to summarize FMs applicability as the first step. The second step is to compare the performance of sparse FMs which has feature selection function, beside our proposal.
Discovering the distribution of attention in destination marketing using 360° video: the eye-tracking study

Virtual tours in 360° video format are a modern way to destination marketing. The relevance of the study of innovative ways of promoting territories is determined by the process of digitalization of the economy and the high prevalence of digital communications. The scientific problem addressed in this study is to examine the effectiveness of 360° video in the marketing of territories in order to determine its impact on consumers and assess the emotional attitude to the territory. The present study used eye-tracking technology to analyze patterns of attention distribution, which are related to the level of knowledge about territories, while watching a 360° video showcasing to these territories. The scientific novelty of this study is a consideration of attention distribution features using eye-tracking technology while watching a 360° video in the context of territory marketing. The sample consisted of 30 students from Switzerland and Russia and the study was conducted in two countries. The objective of the study was to identify differences in eye movements when watching a 360° video in groups of people with different degrees of awareness of Switzerland. The results showed that the average duration per fixation was significantly longer in the Russian group, as was the degree of cognitive load, compared to the Swiss group. The Swiss group required more time to fixate their gaze on large objects, while the amount of the objects which were observed significantly decreased in both groups in the last part of the video. The results of the study can be used as recommendations for the creation and effective use of 360° video in the marketing of territories.
Enhanced Computer-Aided Diagnosis Model on Ultrasound Images through Transfer Learning and Data Augmentation Techniques for an Accurate Breast Tumors Classification

Cancer is a critical global public health problem with meager median survival. It is therefore quite essential to detect this disease at an early stage to improve diagnostic results and consequently avoid serious complications. For this purpose, various researchers have implemented automated methods with the use of different medical imaging modalities. Accordingly, the expansion of deep learning techniques grants opportunities to enhance diagnosis, cure, and prevention. In this study, a diagnostic system for accurate classification of ultrasound breast abnormalities based on the powerful ResNet-50 CNN is proposed with the aim of providing early detection of breast cancer decease. The contribution of this work lies in the novel approach taken to improve the performance of the ResNet50 model in the classification of ultrasound breast cancer images. Transfer learning allows for the model to leverage pre-existing knowledge, while the application of data augmentation techniques enhances the diversity and quality of the training data. Additionally, the optimization of the batch size as a hyperparameter ensures that the model is able to effectively learn from the training data, leading to improved accuracy and efficiency in the classification process. This approach is crucial in the early detection and treatment of breast cancer. Quantitative and qualitative evaluations have been detailed in this study using Breast Ultrasound Dataset BUSI. Our presented work shows interesting results in terms of accuracy, specificity, sensitivity, and AUC which exceed the performance of other compared works. Moreover, the proposed method helps boost the clinical diagnosis of breast cancer. It may integrate a radiologist network, allowing them to constantly follow up on the patient's medical history.