Reviewerly/exHarmony huggingface dataset:
A New Approach to Semantic Parsing of Metonymic Phrases by a Business Intelligence System

The functioning of numerous business intelligence systems (BIS) substantially depends of the content of databases. In many thematic domains, this content is highly dynamic, e.g., in case of the prices for oil, aluminum, wheat, etc. It is the reason for the birth of a BIS project aimed at updating the content of relational databases by means of extracting actual values of data from the texts in natural language (NL). The principal subject of the paper is theoretical foundations of building semantic representations (SRs) of the metonymic phrases. In linguistics, a metonymic construction is a linguistic construction in which an entity is referred to by the name of something closely associated with that thing or situation. The main scientific results set forth in the paper are as follows: (a) a logical structure of a minimalist terminological knowledge base helping to fulfil semantic parsing of metonymical phrases, it is motivated by the theory of K-representations; (b) an original algorithm of finding a semantic interpretation of metonymic phrases of some kinds.
Security and Privacy Concerns Associated with the Internet of Things(IoT) and the Role of Adapting Blockchain and Machine Learning - A Systematic Literature Review

The Internet of Things (IoT) is one of the most important technologies of our day, and it will continue to become more significant as more businesses realize the competitive advantages of having connected devices. Blockchain helps maintain correct data that is unaltered and permanent. At the same time, the Internet of Things (IoT), addresses hardware connected via the internet and may use machine learning to discover trends and produce accurate projections utilizing this data. Those three technologies have taken the place of the conventional methods that were previously employed for the design and architecture of new devices. Combining them yields a wide range of potential outcomes, some of which might result in the next great thing. We also contend that this convergence will speed up the development of autonomous models and information technology that aids companies in managing the security and privacy of their clients. We highlight and expose several challenges and upcoming research areas using machine learning algorithms and blockchain techniques to address security and privacy issues in the IoT space. In this paper, we thoroughly analyze the use of blockchain and machine learning technologies for IoT, focusing on security and privacy. Critical thoughtful writing has been heightened by examining research articles published in well-regarded magazines between 2018 and 2022. Finally, there are still challenges that can steer academics toward potential future advancements in IoT security and privacy.
Implementation and Cost-Effectiveness of Blockchain Technology within the IP Ecosystems

The Fourth Industrial Revolution (Industry 4.0) enables the new digital economy by merging technologies such as blockchain, big data, artificial intelligence, the Internet of Things (IoT), and other emerging technologies through digital transformation. Blockchain technology is among the emerging technologies that are affecting the ways of business operationalization through the automatization of tasks, but also the management of intellectual property rights (IPRs) protection process. The digital economy has an important role in both social and as an engine for economic growth while the IPRs have an important role in the development of the digital economy. The aim of this paper is to investigate and analyze the cost-effectiveness and positive impact of blockchain technologies adoption and implementation in IPRs protection process within the IP ecosystem.
The Impact of Image Processing on Perceptual Hash Values

In the last few decades image processing has become the focus of research in different fields. It can be used to extract information from images, improve their quality and make it easier for computers to understand them. Some of the challenges that pose a great problem for working with images are detecting modified images or detecting similar images. In order to address those challenges, image hashing can be used. Hashing is calculating a digest value from images and perceptual hash algorithms are a type of hash algorithms with the main idea that similar data has similar hash values, which means that the hash values remain approximately the same if the content is not significantly modified. This paper will compare different perceptual hash algorithms, apply them to differently modified images and analyse the impact of those modifications on perceptual hash values provided by different algorithms. The obtained values will be compared and advantages and disadvantages of different algorithms will be discussed.
Teacher’s Motivation for Applying the Unplugged MEMA Method for Early Programming Teaching

Teachers’ motivation is crucial for the application of innovations in their teaching. Therefore, it is important to find out what factors promote teachers’ intrinsic and/or extrinsic motivation so that the innovation is accepted and used in the classroom. This paper points out the need to understand the factors that motivate teachers to use the Unplugged MEMA method for early programming teaching. In this context, ways to motivate current and future teachers to use the MEMA method over time are identified, and possible motivating factors are explained using the existing conceptual model of educational innovation adoption. The results of this work may help create a teacher education model for the future application of the MEMA method and the implementation of similar innovations in the classroom.
Multilingual Named Entity Recognition Solution for Optimizing Parcel Delivery in Online Commerce: Identifying Person and Organization Names

This paper presents a comprehensive solution to enhance parcel delivery in online commerce by implementing multilingual named entity recognition. The solution is designed to accurately identify person and organization names, with a primary emphasis on correctly identifying recipients. The ultimate goal is to use this information to automatically validate recipients and select the most accurate one to improve data accuracy and reliability for parcel delivery. The process begins by collecting a large dataset of online commerce data, including customer search queries, and annotating it with person and organization names. The data is then preprocessed, cleaned to eliminate irrelevant information, and prepared for training a named entity recognition model. Next, the model is trained and evaluated using this data to ensure its ability to identify named entities and extract recipients from queries accurately. The process employs an iterative training process and data generation techniques, while also addressing the issue of noisy data and iterative training introducing unwanted patterns by retraining the model on the subset of the original annotated dataset. Our experiments conclude a consistent increase of F1 score over the baseline and best iteration using this method of training and fine-tuning.
A Treasure Trove at Your Fingertips: Analysis of Contents from Europeana with Regard to their Copyright Status

Libraries, museums, archives and other institutions from more than 35 countries in Europe as well as several countries outside Europe, shared with Europeana the metadata about their heritage documents in digital form. This enabled the creation of huge integrated digital library which gives access to more than 55 million digital documents. Among these documents are books, newspapers, photographs, illustrations. Lots of documents from Europeana are freely available to use for various purposes, given that it is content in the public domain or content that has one of the Creative Commons licences or is otherwise free to use without having to pay compensation to rights holders. This kind of open access content was especially important during the Covid-19 pandemic when, for example, many teachers and school librarians could use it for the improvement of online teaching and learning. The goal of this study was to research the copyright status of contents from Europeana. The Europeana’s contents of different copyright status were analyzed with regard to the following aspects: type of media, type of right statement, country in which the content is located, institution in which the content is located. Insights were acquired using the quantitative content analysis of the data from Europeana.
Digital Technologies in Teaching Physics: An Analysis of Existing Practices

The article raises the problem of using digital technologies (DT) in teaching physics. To identify scientific and methodological results on the use of DT in teaching physics, an analysis of publications contained in the Scopus / Web of Science databases and scientific and methodological publications of Ukraine is presented. The following directions have been recorded: the use of various digital tools as tools for the study of physical processes; the use of applications to solve physical problems, the use of virtual and digital physical laboratories, the use of virtual and augmented reality to teach physics, and the use of specialized environments to model physical processes. Separately, virtual physical laboratories are noted – computer environments that allow you to simulate the behavior of real-world objects in conditions that are close to real ones. The PhET resource, which contains online laboratories to support the study of various topics of the physics course, is briefly described. Based on the results of the survey of teachers, Ukraine’s practices of using digital technologies in teaching physics are summarized. In particular, there revealed a certain teacher bias regarding using virtual physical laboratories to teach physics.
The Formation of Infomedia Literacy of Students in a Media Tournament

The article touches upon the problem of forming Info media literacy of high school students in a media tournament. The concept of info-media literacy is described. It is substantiated that the modern form of extracurricular work is a media tournament, participation in which can affect the level of info-media literacy of young people. The experience of organizing and conducting a media tournament within the framework of the grant project of the Council for International Research and Exchanges (IRX) is described, which included a number of events (Bar-camp “Online Tools”, Workshop “Media literacy tools in the study of literary works”, Workshop “Formation of skills of evaluation and interpretation of media information”, Training “Language media master” and four checkpoints). The effectiveness of the media tournament has been proven statistically. According to the results of the selection of teams for the Media Tournament, it was revealed that Ukrainian schools do not have a policy of conscious formation of information literacy of students.
Understanding the Teamwork Challenges of Software Engineering Students

Developing collaborative skills in students is nontrivial. The fact that students work in teams does not mean they become skilled in teamwork. Students face varied challenges when working in teams that harm their skill development and attitude towards teamwork. To prepare students for the collaboration-intensive workplace, we researched and designed a catalog of challenges present in the teamwork of undergraduate software engineering students on 3-month projects. We created an initial catalog of 10 challenges by examining the literature, surveying 15 teaching assistants, and coding their opinions regarding the problems faced by student teams. Using the catalog, we crafted a survey for students nearing the end of their team project to assess which challenges were present in their teamwork. We surveyed students from multiple contexts, including teams of 3, teams of 4, and teams of16 students. We analyzed 155 answers to determine the prevalence and intensity of the 10 challenges in student teams. We discuss our findings and best practices for resolving the most prevalent challenges. The catalog and recommendations are directly valuable for software engineering educators and can inform the broader community of collaborative learning researchers and instructional designers.
A Common Pentest Output Schema for Business Intelligence System Ingestion

Data-driven Business Intelligence process improvements demand proactive digital vulnerability discovery and exploitation enumeration. These risk discovery and analyses practices are commonly known as penetration tests (pentests), and have emerged as requirement for most organizations under many compliance regimes such as the global Payment Card Industry Data Security Standard (PCI-DSS) and the federal Gramm-Leach Bliley Act (GLBA) in the United States. Though a growing body of research addresses the utility of pentests as a valuable Business Intelligence method, pentest outputs are not standardized and require time-consuming generation and ingestion before they can provide inputs to Business Intelligence Systems. In this study, we examined several pentest reports and propose a standardized schema for output generation in a common format that is easy for Business Intelligence Systems to ingest. We propose that this improvement will allow more rapid delivery of accurate risk information to executives, managers, and information technology professionals. The thirteen elements of our standardized schema represent the first attempt at building a pentest dimensional model, which could further ease the translation of cybersecurity risk information into business impact analyses and organizational risk registers.
The Role of Knowledge Management in Transition to Industry 5.0

Fifth industrial revolution, or Industry 5.0 is gaining a momentum in last several years, additionally enhanced with many disruptive events in global economy. Its main keywords are sustainable, human-centric and resilient, and it should bring these benefits to industry and society. Knowledge management enables the cycle of knowledge through organization and is a human oriented dicipline that continually embraces new technologies, helping organizations to achieve their goals. This paper explains that sustainability, human-centricity and resilience are naturally supported by knowledge management. Investigation of connection of elements of Industry 5.0 action plan with knowledge management processes and technologies shows that knowledge management has an important role in achieving Industry 5.0, especially through processes of knowledge creation/acquisition and storing and supporting artificial intelligence technologies.
A Transfer Learning Method for Hate Speech Detection

In this work we explore the possibilities of using transfer learning techniques to enhance performance of hate speech detection models by relying on similar linguistic problems (e.g. toxic language detection). Multiple algorithms are trained for similar linguistic tasks on larger datasets, and the obtained models are used for getting predictions on the ETHOS dataset, which we chose as the target dataset of our work. The obtained predictions are used as sole or additional features in the subsequently performed experiments. Multiple algorithms are evaluated, including Logistic Regression, SVM, RidgeClassifier, Decision Tree, Random Forest, AdaBoost, GradBoost, Bagging. Furthermore, multiple textual representations are taken into account including Tf-Idf, Bert embeddings and BERT embeddings combined with the aforementioned additional features. Transformer-based models BERT and DistilBERT are introduced and fine-tuned on ETHOS dataset. All the obtained models are evaluated and the resulting performance metrics are compared to results obtained by the authors of the ETHOS dataset. In order to explore the remaining underlying issues, model-agnostic method LIME is used to obtain explanations for incorrect predictions.
Optimizing Vision Transformer Performance with Customizable Parameters

This paper experimentally examined the effects of changing the size of image patches and the number of transformer layers on the training time and accuracy of a vision transformer used for image classification. The transformer architecture was first introduced in 2017 as a new way of processing natural language and has since found applications in computer vision as well. In this experiment, we trained and tested fourteen versions of a vision transformer on the CIFAR-100 dataset using graphical processing units provided by Google Colaboratory. The results showed that increasing the number of transformer layers and decreasing the patch size both increased test accuracy and training time. However, learning curves generated by the models showed overfitting for very small patch sizes. Overall, changing patch size had a greater impact on accuracy than changing the number of transformer layers. The results also suggested that transformers are more resource-intensive to train than other models. We suppose that including a classification token could lead to shorter training times, but another experiment is needed to examine its influence on accuracy.
Partial SQL Query Assessment

Automated grading systems in education have been around for sixty years. They have found applications in areas such as online learning systems where virtually an unlimited number of users can test their knowledge that would not be possible to evaluate manually. Implementations within Massive Open Online Courses are a good practice in which users can do the self-testing, and get instant feedback, making the learning process more efficient. Within universities, automated grading systems allow teachers to evaluate solutions and provide feedback for thousands of submissions in a short time. This paper presents an overview of methods used in automatic SQL query evaluation systems, from early implementations when the goal was only to evaluate solutions binary, to today when they enable functionalities like partial and configurable evaluation, rich and customized feedback, learning analytics, learning pattern detection, code quality check, plagiarism detection. These methods are not exclusive, and combining different approaches makes an automated grading system more comprehensive and applicable. Automatic assessment system of SQL queries developed at Algebra University College will be presented as an example of a system which uses dynamic and static analysis, awards partial points, and gives feedback to students based on the wrong parts of their solutions.
Towards Improving Online Security Awareness Skills with Phishing and Spoofing Labs

It can be especially hard for novices to examine their online browsing habits, or to be aware of the dangers related to online security. In this paper, we describe laboratory exercises designed to improve awareness of online security, in addition to teaching cybersecurity knowledge and skills. The lab exercises were used as a part of an information security fundamentals course for IT students with none or only novice experience in the field. These lessons are suitable for both delivery in the classroom and online in a virtual laboratory environment. The lab exercises were developed using the design science research methodology (DSR). Following DSR principles, the evaluation of the developed material was done by extracting observations from student reports. The thematic analysis method was used to process the data. The evaluation of the student learning reports revealed that the labs and the course were successful in the following ways: Improving security knowledge, improving critical thinking skills, and improving security awareness. Students also gained hands-on cyber awareness skills by analysing metadata in messages. Additionally, the material was perceived as useful for future working life.
What Do We Know about Learning – Conversations with chatGPT

We are witnessing a major achievement of machine learning - appearance of several chatbots which are able to produce human-quality conversation. Appearance of such ‘fluid conversationalists’ has aroused big interest especially among teachers, since they have already passed several exams designed for humans and have been used by students in winter fall 2022 exams. Their performance is not based on linguistic research, but is an achievement of data science. This performance cannot yet be rationally explained (it is based on interplay of billions of variables), thus in the following are used conversations with the currently most visible member of this family - the chatGPT to clear some myths around these programs.
Opinion Mining on E-commerce Live Broadcast of Agricultural Products Based on WLDA-Apriori Model

With the popularization of information technology in rural society, the e-commerce live broadcast of agricultural products has provided an impetus for agricultural marketing from rural to urban areas. But as an emerging industry, it has problems in live broadcast content, marketing strategies, market supervision, industry norms and agricultural aid effects. In order to fully understand the public opinion and promote the sustainable development of this industry, this paper proposes an opinion mining method based on WLDA-Apriori model. The method combines topic model with association rule algorithm, which can further analyze the implicit semantic relations among topic words. This paper also conducts the empirical study with social media data from Weibo, and obtains eight topics the Chinese public concerned. Based on topic words and their relations, the public opinion are identified and discussed from aspects of Industry Development, Market Effect, Live Broadcast Subject and Negative Effect. By encompassing as many netizens as possible, the research scope of this paper surpasses the constraints of conventional questionnaire surveys and case studies, enhancing the generalizability of the research findings and recommendations. But this paper does not integrate and analyze social media data from multiple platforms, nor does it analyze public opinion from the sentiment level.
Detection of Shilling Attacks on Collaborative Filtering Recommender Systems by Combining Multiple Random Forest Models

Collaborative filtering recommender systems are one of the essential recommender systems and are still widely used in combination with other algorithms to make predictions for users. However, they are vulnerable to shilling attacks, and if there isn’t any detection system to prevent those attacks, original recommendations can be heavily influenced to benefit the attackers. Designing attack-resistant recommendation systems is not an easy task, and many researchers have tried to tackle that problem. In this paper, a new approach that combines multiple random forest models is proposed. Each of the random forest models is specialized in detecting one group of shilling attacks, and then all the models are combined into an ensemble model. Experimental results show that the proposed ensemble is capable of detecting attack profiles at high rate without causing significant bias in the original recommendation system.