Reviewerly/exHarmony huggingface dataset:
An Approach to Semantic-Aware Heterogeneous Network Embedding for Recommender Systems

Recent studies on heterogeneous information network (HIN) embedding-based recommendations have encountered challenges. These challenges are related to the data heterogeneity of the associated unstructured attribute or content (e.g., text-based summary/description) of users and items in the context of HIN. In order to address these challenges, in this article, we propose a novel approach of semantic-aware HIN embedding-based recommendation, called SemHE4Rec. In our proposed SemHE4Rec model, we define two embedding techniques for efficiently learning the representations of both users and items in the context of HIN. These rich-structural user and item representations are then used to facilitate the matrix factorization (MF) process. The first embedding technique is a traditional co-occurrence representation learning (CoRL) approach which aims to learn the co-occurrence of structural features of users and items. These structural features are represented for their interconnections in terms of meta-paths. In order to do that, we adopt the well-known meta-path-based random walk strategy and heterogeneous Skip-gram architecture. The second embedding approach is a semantic-aware representation learning (SRL) method. The SRL embedding technique is designed to focus on capturing the unstructured semantic relations between users and item content for the recommendation task. Finally, all the learned representations of users and items are then jointly combined and optimized while integrating with the extended MF for the recommendation task. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed SemHE4Rec in comparison with the recent state-of-the-art HIN embedding-based recommendation techniques, and reveal that the joint text-based and co-occurrence-based representation learning can help to improve the recommendation performance.
THE DIFFERENCE BETWEEN CYBER SECURITY VS INFORMATION SECURITY

The terms cyber security and information security are often used interchangeably, both in the academic literature and by organizations. This article aimed to identify whether these terms can be used interchangeably, analyzing the differences between the terms, based on several definitions identified in scientific articles and books, as well as definitions presented by specialized organizations. The analysis consists in determining the keywords, that define the terms, based on which conclusions were made. It has been determined that information security places a strong emphasis on the protection of information, cyber security refers to all assets that are part of cyberspace, such as end devices (including IoT), network devices, communication media and information. Cybersecurity is an umbrella term that includes: electronic communications network and computer security, physical security, infrastructure security, personnel security, hardware and application security, business process security. Through the identified results, important contributions are made both for the academic environment, as well as for governmental and non-governmental organizations, for the understanding and correct use of terms, to reduce uncertainty and to correctly approach specialist terminology, because the field of security is increasingly important, the challenges are complex and diverse.
Monetary policy regimes and price stability in the West African monetary zone

The study examined monetary policy regimes and price stability in the West African Monetary zone. This was undertaken given that countries in the zone are implementing different monetary policy regimes to achieve price stability for macroeconomic stability and economic growth, yet these have been elusive. So, the objective of the study was to examine the effect of monetary policy regimes on price stability in the West African monetary zone. The study adopted an ex-post facto research design and obtained secondary data from documents of the Central Banks of the WAMZ countries, the World Bank Development Indicator (WDI), the International Monetary Fund (IMF), and the International Financial Statistics (IFS) database for the period from 2001 to 2021. The method of data analysis involved the use of descriptive and analytical statistical tools. The estimation technique employed was the Panel Autoregressive Distribution Lag model, complemented by the Juodis, Karavias, and Sarafidis (2021) granger-causality test. The findings of the study indicate that the various monetary policy regimes being implemented in the West African Monetary Zone yielded conflicting effects on price stability. On the whole, the results show that the monetary policy rate against the money supply is more effective to achieve price stability in the West African monetary zone. Hence, the study makes the following recommendations; policymakers must ensure that an effective monetary policy is put in place to curb the persistent inflation in the zone that undermines socioeconomic development. Countries should review monetary policy rates appropriately to stimulate output growth, and, there should be cooperation between the monetary and fiscal authorities in the West African Monetary Zone to ensure smooth coordination and consistency in monetary and fiscal pursuits.
Editorial Special Issue on Application of Advanced Intelligent Methods in Vehicle to Smart Grid Communications: Security, Reliability, and Resiliency

This Special Issue was organized on the application of advanced intelligent methods in vehicle-to-smart grid communications considering the security, reliability, and resiliency perspectives. We tried to bring together the ideas from researchers and experts on both transportation and smart grid areas to tackle the technological challenges in future power systems with more electric vehicles and intelligent systems. Theoretically, vehicles consist of different electrical and electronic hardware devices, which are connected with each other through the so-called ECUs being controlled by different software tools. As a well-perceived technology, all sensing instruments located at different points in an electric vehicle will direct their data to the ECU, wherein these data are analyzed and processed, and the demanding orders are sent to the pertinent actuators. With the advance of automobile technology, the electric vehicles are not only an electric load consumer anymore, but are appearing at different parts of human life, ranging from the smart homes and smart buildings to smart grid and smart cities. Owing to the complex and interconnected cyber-physical nature of electric vehicles, they can are getting a more appealing and accessible target for cyber hackers to penetrate the entire system through car hacking. This can result in long-term effects on the security and privacy of the electric vehicle owners, smart buildings, smart grids, and smart cities of the future. Be on that, this Special Issue scope includes methods, tools, applications, and solutions for analyzing, processing, and improving the electric vehicles as a complex cyber-physical system considering its direct or indirect effects on the modern human life factors. Among the high number of submissions received, 13 articles were finally chosen to be published in this Special Issue. The core ideas and concepts of the works are summarized in the rest.
Digital Educational Content in the Learning Environment of Educational Institutions in the Context of Distance and Blended Learning in Mathematics

This article deals with the use of digital educational content in mathematics teaching.The relevance of its use in distance and blended learning is important in a difficult epidemiological situation and in wartime, not only for the organisation of learning, but also for ensuring the quality of educational services.To this end, a questionnaire was developed and an online survey of 261 teachers from all regions of Ukraine was conducted.As a result of surveying teachers who teach mathematics in primary, basic, profiled schools, vocational and higher education, we found that 99.9% of them have experience in distance or blended learning, with 94.6% using digital educational content.In this regard, the question of which digital resources are the most popular among teachers, as well as at what level they use them -develop their own content or use ready-made content, is relevant.The article also explores teachers purposes for using digital content, their awareness, and their experience with such content.According to the analysis of teachers' responses, only 16.9% constantly create interactive assignments in various online services, and 70.9% do so occasionally.The results of the survey identified the most popular software packages for creating educational content; software for preparing presentations; tools for creating graphics and infographics; software for creating and editing videos; specialized mathematical packages and tools for studying mathematics; online services for test writing; services for creating interactive exercises, games, mental maps; online whiteboards; educational platforms.Teachers were also asked to assess their level of ICT-competence, and 68.2% of teachers in Ukraine considered it sufficient.At the same time, there is a trend towards the development of ICT competences through mastering new services, in particular, software for working with multimedia information, and for this purpose 79.2% of teachers need seminars, webinars and trainings.
Interval-valued Data Analysis: A Review

Interval-valued data is observed as ranges rather than single values and is frequently observed in advanced data collection processes and machine learning techniques.The linear and non-linear regression methods were used for the interval valued data prediction (IVD) tasks, and the superiority of the non-linear methods over the other methods was proven.However, the previous literature primarily concerned with parametric linear regression models in the analysis of interval valued data.Thus, the analysis and study of interval-valued data are still under development, leaving many stones unturned and many similar methods to study and work with.This paper presents a review of the most important methods used to analyze interval-valued data to provide summary insights and evaluation.
Interval Type-3 Fuzzy Systems: A Natural Evolution from Type-1 and Type-2 Fuzzy Systems

This article discusses the natural evolution that took place from type-1 to type-2, and now recently is occurring from type-2 to type-3 fuzzy systems. Prof. Zadeh originally proposed the idea of type-1 fuzzy sets in 1965 and later of type-2 fuzzy in 1975. The goal was to model the uncertainty existing in the real world, and it is well known now that type-2 fuzzy models are better to handle the levels of uncertainty in the real-world, coming from noisy, dynamic, non-linear environments and systems. In addition, subjectivity that is handled by humans is also better represented by type-2 fuzzy sets. As a consequence, type-2 systems have been able to overcome type-1 fuzzy systems in many application areas, such as, intelligent control, pattern recognition, and diagnosis. More recently, we have witnessed the rise of the interval type-3 fuzzy sets and their utilization in control and identification of non-linear systems, showing better results than type-2 and type-1, so we expect that also this pattern will continue to other areas of application. In this article, the main differences among the concepts of type-3, type-2 and type-1 will be discussed and then an account of the existing applications of type-2 will be highlighted and finally, future areas of research will be outlined.
Recommendation Model-Based 5G Network and Cognitive System of Cloud Data with AI Technique in IOMT Applications

Recommender system provides the significant suggestion towards the effective service offers for the vast range of big data. The Internet of Things (IoT) environment exhibits the value added application services to the customer with the provision of the effective collection and processing of information. In the extension of the IoT, Internet of Medical Things (IoMT) is evolved for the patient healthcare monitoring and processing. The data collected from the IoMT are stored and processed with the cognitive system for the data transmission between the users. However, in the conventional system subjected to challenges of processing big data while transmission with the cognitive radio network. In this paper, developed a effective cognitive 5G communication model with the recommender model for the IoMT big data processing. The proposed model is termed as Ranking Strategy Internet of Medical Things (RSIoMT). The proposed RSIoMT model uses the distance vector estimation between the feature variables with the ranking. The proposed RSIoMT model perform the recommender model with the ranking those are matches with the communication devices for improved wireless communication quality. The proposed system recommender model uses the estimation of direct communication link between the IoMT variables in the cognitive radio system. The proposed RSIoMT model evaluates the collected IoMT model data with the consideration of the four different healthcare datasets for the data transmission through cognitive radio network. Through the developed model the performance of the system is evaluated based on the deep learning model with the consideration of the collaborative features. The simulation analysis is comparatively examined based on the consideration of the wireless performance. Simulation analysis expressed that the proposed RSIoMT model exhibits the superior performance than the conventional classifier. The comparative analysis expressed that the proposed mode exhibits ~3 – 4% performance improvement over the conventional classifiers. The accuracy of the developed model achieves 99% which is ~3 – 9% higher than the conventional classifier. In terms of the channel performance, the proposed RSIoMT model exhibits the reduced recommender relay selection count of 1 while the other technique achieves the relay value of 13 which implies that proposed model performance is ~4-6% higher than the other techniques.
28th NATIONAL SCIENTIFIC PRACTICAL CONFERENCE “NATURAL SCIENCE EDUCATION IN A COMPREHENSIVE SCHOOL – 2022”: CONFERENCE REVIEW

In 2022, the national scientific-practical conference “Natural science education in a comprehensive school – 2022” was held remotely (online). Such conference format was determined by the ongoing Covid-19 pandemic; therefore, the conference for the third time already, could not take place in its usual format. It is obvious that the plenary session “united” different continents – Africa (speaker Dr. Angela James from Republic of South Africa), Latin America (Dr. Solange Locatelli from Brazil), Europe (Dr. Martin Bilek from Czech Republic, Dr. Malgorzata Nodzynska-Moron from Poland, and Dr. Rita Birzina from Latvia). All plenary reports in their own way were interesting and relevant. Prof. M. Nodzynska-Moron’s report was dedicated to non-formal natural science education realities. Various practical examples were presented, non-formal natural science education experiences were analysed. Prof. M. Bilek analysed chemistry didactics issues in his report. A special attention was paid to virtual chemistry experiments, as well as to experiments using computer tools. Prof. A. James discussed the implementation of STEM programme while working with primary school teachers. Preparation and practical implementation aspects of such programme were analysed, concrete educational experiences were presented. In the first section, the reports were mainly devoted to the problems of pre-school, primary and general natural science education. A total of 9 reports were presented. In the second section, issues of natural science and technological education in gymnasium and in higher education institutions were discussed. A total of 10 reports were presented. It is interesting that in the second section, three reports of foreign researchers were presented. The topics of the research were diverse, basically various areas of natural science and technological education were discussed. It is important that the participants were active, intense discussions and sharing of experiences took place. The results of the ongoing international project “Bridge2Teach” were presented at the conference. More information about this project can be found on the project website (https://www.bridge2teach-project.eu). Various natural science education topics were presented in other reports. On the second day of the conference, a seminar was held for natural science teachers “Integrated science education in Lithuania: achievements and future perspectives” (The project is funded by the Lithuanian Science Council No. P-REP-21-8). Insights, recommendations, and conclusions of the research. Most conference reports were presented in the form of articles and published in the conference proceedings. One can find full texts in the database at: https://oaji.net/journal-archive-stats.html?number=1984&amp;year=2022&amp;issue=19320 Keywords: online platform, national conference, science education, science teachers, science pedagogy
Bigbird: Exabyte-Scale Big Data Storage and Analytics Framework in Hybrid Cloud

Implementing big data storage at scale is a complex and arduous task that requires an advanced infrastructure. With the rise of public cloud computing, various big data management services can be readily leveraged. As a critical part of Twitter's ``Project Partly Cloudy", the cold storage data and analytics systems are being moved to the public cloud. This paper showcases our approach to designing a scalable big data storage and analytics management framework using BigQuery in the Google Cloud Platform while ensuring security, privacy, and data protection. The paper also discusses the limitations of public cloud resources and how they can be effectively overcome when designing an exabyte-scale big data storage and analytics solution. Although the paper discusses the framework implementation in Google Cloud Platform, it can easily be applied to all major cloud providers.
Boolean-Valued Models and Their Applications

Abstract Boolean-valued models generalize classical two-valued models by allowing arbitrary complete Boolean algebras as value ranges. The goal of my dissertation is to study Boolean-valued models and explore their philosophical and mathematical applications. In Chapter 1, I build a robust theory of first-order Boolean-valued models that parallels the existing theory of two-valued models. I develop essential model-theoretic notions like “Boolean-valuation,” “diagram,” and “elementary diagram,” and prove a series of theorems on Boolean-valued models, including the (strengthened) Soundness and Completeness Theorem, the Löwenheim–Skolem Theorems, the Elementary Chain Theorem, and many more. Chapter 2 gives an example of a philosophical application of Boolean-valued models. I apply Boolean-valued models to the language of mereology to model indeterminacy in the parthood relation. I argue that Boolean-valued semantics is the best degree-theoretic semantics for the language of mereology. In particular, it trumps the well-known alternative—fuzzy-valued semantics. I also show that, contrary to what many have argued, indeterminacy in parthood entails neither indeterminacy in existence nor indeterminacy in identity, though being compatible with both. Chapter 3 (joint work with Bokai Yao) gives an example of a mathematical application of Boolean-valued models. Scott and Solovay famously used Boolean-valued models on set theory to obtain relative consistency results. In Chapter 3, I investigate two ways of extending the Scott–Solovay construction to set theory with urelements. I argue that the standard way of extending the construction faces a serious problem, and offer a new way that is free from the problem. Abstract prepared by Xinhe Wu. E-mail : xinhewu@mit.edu
Semi-supervised learning for sentiment classification with ensemble multi-classifier approach

Supervised sentiment analysis ideally uses a fully labeled data set for modeling. However, this ideal condition requires a struggle in the label annotation process. Semi-supervised learning (SSL) has emerged as a promising method to avoid time-consuming and expensive data labeling without reducing model performance. However, the research on SSL is still limited and its performance needs to be improved. Thus, this study aims to create a new SSL-Model for sentiment analysis. The Ensemble Classifier SSL model for sentiment classification is introduced. The research went through pre-processing, vectorization, and feature extraction using TF-IDF and n-grams. Support Vector Machine (SVM) or Random Forest for tokenization was used to separate unigram, bigram, and trigram in model generation. Then, the outputs of these models were combined using stacking ensemble approach. Accuracy and F1-score were used for the evaluation. IMDB datasets and US Airlines were used to test the new SSL models. The conclusion is that the sentiment annotation accuracy is highly dependent on the suitability of the dataset with the machine learning algorithm. In IMDB dataset, which consists of two classes, it is better to use SVM. In the US Airlines consisting of three classes, SVM is better at improving the model performance against the baseline, but RF is better at achieving the baseline performance even though it fails to maintain the model performance.
Incomplete Inpatient Medical Record Documents at Hospital

The medical record contains the patient's identity, examination, treatment, actions and other services that have been provided to patients. There were incomplete integrated patient progress notes 61% and incomplete medical resumes 73%. The research intended to analyze the aspects that caused the incompleteness of inpatient medical record documents at the Besuki Hospital. This research used a qualitative method with data collection techniques interviews, observation dan documentation. The subjects in this study were 1 head of medical records and 5 medical record officers. The result showed that the factors causing the incompleteness of inpatient medical record documents from the motivational variable were that there was still no award and punishment for medical record officers. Opportunity variables were the absence of incomplete record cards, medical record officers who have never attended any training at all, SOPs for checking the completeness of medical records missing and having never been socialized. The ability variable was that there were medical record officers who did not have a minimum education of medical recorders, the knowledge of officers is still lacking and there were officers who had work experience of less than 2 years. Abstrak: Rekam medis berisi identitas pasien, pemeriksaan, pengobatan, tindakan dan pelayanan lain yang telah diberikan kepada pasien. Berdasarkan hasil observasi diketahui bahwa terdapat ketidaklengkapan CPPT sebesar 61% dan ketidaklengkapan resume medis sebesar 73%. Penelitian bertujuan untuk mengidentifikasi dan menganalisis penyebab ketidaklengkapan rekam medis rawat inap di RSUD Besuki. Jenis penelitian adalah kualitatif, pengumpulan data dilakukan dengan wawancara mendalam, dokumentasi, dan observasi. Subyek pada penelitian ini yaitu 1 ketua rekam medis dan 5 petugas rekam medis. Hasil penelitian menunjukkan faktor penyebab ketidaklengkapan dokumen rekam medis rawat inap dari variabel motivasi yaitu masih belum ada pemberian penghargaan dan hukuman untuk petugas rekam medis. Variabel kesempatan yaitu belum adanya kartu catatan ketidaklengkapan, petugas rekam medisbelum pernah mengikuti pelatihan sama sekali, SOP kegiatan pengecekan kelengkapan rekam medis hilang dan belum pernah disosialisasikan. Variabel kemampuan yaitu terdapat petugas rekam medis yang tidak memiliki pendidikan minimal D3 Rekam Medis, pengetahuan petugas rekam medis masih kurang dan ada petugas memiliki pengalaman kerja kurang dari 2 tahun.