Reviewerly/exHarmony huggingface dataset:
A Conceptual Insight Into Achieving Interoperability Between Heterogeneous Blockchain Enabled Interconnected Smart Microgrids

Blockchain technology (BCT) has emerged as a game-changer for many industries since its inception in 2008. Its application in the energy industry as blockchain enabled interconnected smart microgrids (BSMG) is on the rise as it can execute energy trading, automate the market operations, manage the grid, and facilitate real-time payments. With the increase in usage of BSMGs, different types of BCT will emerge, making the system heterogeneous in nature. BCT is also limited currently due to its scalability and low transaction rate. Interoperability between heterogeneous BSMGs can counteract the drawbacks and improve the functionality and, thereby, adoption of BCT in energy. This paper recognises the need for interoperability and thoroughly reviews the different methods of interoperability that currently exist (i.e., notary mechanism, relay or side chains, hashed time-locked contracts, and blockchain routers). Two relay mechanisms - Polkadot and Cosmos with Inter Blockchain Communication Protocol (IBC) are reviewed further to determine the usability of the protocols. Finally, a conceptual architecture of interconnection of heterogeneous BSMGs is proposed. Conceptual solution to connecting Ethereum and Hyperledger Fabric to the Cosmos Hub through IBC is explored. While interoperability between heterogeneous users is addressed in Decentralized Finance (DeFi), it has not yet been addressed in energy. A conceptual solution is provided for this research gap for the first time in energy domain.
Socio-humanitarian and technicaltechnological explorations of modern science

Interdisciplinary research of modern trends in science and its practical application
Algorithms Scheduling Applied to Microservics Architectures: A Survey Study

Microservice architecture is increasingly replacing monolithic architectures due to its numerous advantages, including ease of deployment, flexibility, and availability. The advent of containerization has further amplified the importance of microservices, making them scalable and portable… Orchestration tools rely on schedulers to control and manage containers running microservices, driving extensive research in search of suitable and optimal scheduling algorithms that minimize costs, reduce overhead, and ensure cluster performance. This paper provides an overview of several popular scheduling algorithms applied by researchers, offering insights into their methodologies through a comparative analysis. We explore the different parameters used and their impact on algorithm performance. Additionally, we delve into the advantages and drawbacks associated with each algorithm, illuminating their strengths and weaknesses. In conclusion, we discuss potential future directions, aiming to identify areas for improvement and innovation in the selection and optimization of scheduling algorithms.
Advancing Breast Cancer Diagnosis with Machine Learning: Exploring Data Balancing, Feature Selection, and Bayesian Optimization

Breast cancer represents the preeminent widespread type of cancer worldwide among women. The World Health Organization estimates that an annual total of 2.3 million new breast cancer cases are recorded. Also, breast cancer stands as the top cause of cancer mortality in the female population, claiming more than 685,000 lives by 2020. In response to the alarming spread of breast cancer and its significant impact on women's health, it has become imperative to develop innovative techniques and methods for early detection, accurate diagnosis, and effective treatment. In this perspective, the current paper suggests a comparison of several machine learning methods enhanced with data balancing, feature selection, and hyperparameter-tuning Bayesian search strategies. The dataset employed is an unbalanced set of 569 entries comprising 31 medical features associated with breast cancer. With machine learning, data balancing, feature selection, and hyperparameter optimization methods, we can make significant strides in improving the accuracy of breast cancer classification and prediction techniques. All models in our study demonstrated promising performances, exceeding 98% across all classification metrics for some of them, which will improve breast cancer diagnosis and treatment systems and offer healthcare professionals more practical resources.
SPATIAL ANALYSIS OF FIRE OCCURRENCE IN JAKARTA, INDONESIA

The occurrence of fire incidents in urban villages of Jakarta Special Capital Region significantly impacted losses, necessitating prevention and handling efforts. Therefore, this study aims to analyze the spatial influence of social and physical variables (independent variables) such as sex ratio, vulnerable age population, number of buildings, and size of slum areas on fires (dependent variable) in Jakarta Special Capital Region. The analysis area includes five municipalities of Jakarta Special Capital Region. Secondary data were obtained from Central Agency of Statistics of Jakarta Special Capital Region, maps from the official site jakartasatu.jakarta.go.id, and publication data from Government of Jakarta Special Capital Region for 2020. Furthermore, the quantitative approach in descriptive and inferential analysis, determined using Microsoft Excel and GeoDa version 1.20.0.10, was used to evaluate the spatial relationships between adjacent sub-districts. Although the regression data processing results using GeoDa were significant, the spatial regression results with Lagrange Multiplier (LM) Lag and Lagrange Multiplier (LM) error &gt; 0.05 were insignificant and significant when using the parameter 0.1. This means fire symptoms in Jakarta Special Capital Region do not have a spatial effect, contrary to the clustering observed between dependent and independent variables using Morans'I and Scatter Plots. The results of this study can aid the Jakarta provincial government in preventing and handling potential fires by restructuring slum areas to minimize the likelihood of such incidents.
Development of FASIH Application for the Badan Pusat Statistisk using Flutter Framework

One of the data collection methods used by the Badan Pusat Statistik (BPS) is Computer Assisted Personal Interviewing (CAPI). Currently, CAPI, known as FASIH, is continuously updated by BPS using the Kotlin programming language, which can run on the Android platform. It is possible that FASIH will be needed in a multiplatform form. However, there is an alternative for multiplatform application development, namely Flutter, which can be used in the development of FASIH. Nevertheless, BPS has not conducted any study on the development of the FASIH application using Flutter, hence the strengths and weaknesses of implementing this technology in FASIH application development remain unknown. Therefore, the author aims to conduct a study on the development of the FASIH application utilizing Flutter. The application development is carried out using the Rapid Application Development (RAD) Prototyping method. The resulting application is tested using black box testing and performance testing using a third-party application, Apptim. The black box testing results indicate that the application meets the functional requirements of stakeholders. In terms of performance, the Kotlin version of FASIH outperforms the Flutter version. However, Flutter has an advantage in accelerating development time. Additionally, concerning user interface development, the Flutter version of the FASIH application can run on multiple platforms. Nevertheless, further integration is required to ensure the proper functioning of the Flutter version of the FASIH application.
Comparative Analysis of Retriever and Reader for Open Domain Questions Answering on BPS Knowledge in Indonesian

Enumerators from Badan Pusat Statistik (BPS) still often encounter problems in finding solutions to cases encountered during censuses or surveys. Even though knowledge lists have been created and collected in various systems such as QA and knowledge management systems, enumerators still need to find appropriate answers from long and complex knowledge search results. On the other hand, Open-domain Question Answering (OpenQA) is capable of identifying answers to natural questions based on large-scale documents. OpenQA has main components, namely Retriever and Reader. For Retriever tasks, Dense Retrieval (DR) is proven to outperform traditional sparse retrieval such as TF-IDF or BM25. However, other research actually shows that BM25 is superior to DR in terms of accuracy. In this study, we compared DR and BM25 separately and DR+BM25 as a retriever. Additionally, we combine and evaluate several enhanced language models as Readers. In this way, a model with the best combination of Retriever and Reader can be obtained to be implemented in search systems such as QA and knowledge management systems.
Automated Laryngeal Cancer Detection and Classification Using Dwarf Mongoose Optimization Algorithm with Deep Learning

Laryngeal cancer (LCA) is a serious disease with a concerning global rise in incidence. Accurate treatment for LCA is particularly challenging in later stages, due to its complex nature as a head and neck malignancy. To address this challenge, researchers have been actively developing various analysis methods and tools to assist medical professionals in efficient LCA identification. However, existing tools and methods often suffer from various limitations, including low accuracy in early-stage LCA detection, high computational complexity, and lengthy patient screening times. With this motivation, this study presents an Automated Laryngeal Cancer Detection and Classification using a Dwarf Mongoose Optimization Algorithm with Deep Learning (ALCAD-DMODL) technique. The main objective of the ALCAD-DMODL method is to recognize the existence of LCA using the DL model. In the presented ALCAD-DMODL technique, a median filtering (MF)-based noise removal process takes place to get rid of the noise. Additionally, the ALCAD-DMODL technique involves the EfficientNet-B0 model for deriving feature vectors from the pre-processed images. For optimal hyperparameter tuning of the EfficientNet-B0 model, the DMO algorithm can be applied to select the parameters. Finally, the multi-head bidirectional gated recurrent unit (MBGRU) model is applied for the recognition and classification of LCA. The simulation result analysis of the ALCAD-DMODL technique is carried out on the throat region image dataset. The comparison study stated the supremacy of the ALCAD-DMODL technique in terms of distinct measures.
Using JavaScript for Automating Attendance Tracking in the Moodle Learning System

Problem: The article addresses the issue of automating attendance tracking for students in the context of distance learning in higher education institutions. Methodology: The paper analyzes existing methods of student attendance tracking in virtual learning environments, identifying their advantages and drawbacks. Additionally, it proposes a JavaScript-based solution for the automatic attendance tracking, generating a CSV file that is subsequently uploaded to the Moodle web server. Goal: The primary goal of the article is the development and exploration of a software tool capable of automating the process of tracking student attendance in the online educational system Moodle. Originality: The paper introduces an original approach to solving the attendance tracking issue using the JavaScript programming language, providing an innovative character to the proposed methodology. Results: The developed JavaScript script enables the automatic creation of a CSV file with student attendance data, which is then uploaded to the Moodle web server. The results demonstrate the efficiency of this method. Practical value: The work holds significant practical value for educators and administrators in educational institutions, simplifying and automating the student attendance tracking process in the virtual learning environment based on the Moodle platform.
Optimizing Path Planning for Smart Vehicles: A Comprehensive Review of Metaheuristic Algorithms

In the realm of smart vehicle navigation, both in known and unknown environments, the crucial aspects encompass the vehicle's localization using an array of technologies such as GPS, cameras, vision systems, laser, and ultrasonic sensors. This process is pivotal for effective motion planning within the vehicle's free configuration space, enabling it to adeptly avoid obstacles. The focal point of such navigation systems lies in devising a path from an initial to a target configuration, striving to minimize the path length and the time taken, while simultaneously circumventing obstacles. The application of metaheuristic algorithms has been pivotal in this regard. These algorithms, characterized by their ability to exploit initial solutions and explore the environment for feasible pathways, have been extensively utilized. A significant body of research in robotics and automation has focused on evaluating the efficacy of population-based algorithms including Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Whale Optimization Algorithm (WOA). Additionally, trajectory-based methods such as Tabu Search (TS) and Simulated Annealing (SA) have been scrutinized for their proficiency in identifying short, feasible paths among the plethora of solutions. There has been a surge in the enhancement and modification of these algorithms, with a multitude of hybrid metaheuristic algorithms being proposed. This review meticulously examines various metaheuristic algorithms and their hybridizations, specifically in their application to the path planning challenges faced by smart vehicles. The exploration extends to the comparison of these algorithms, highlighting their distinct advantages and limitations. Furthermore, the review delves into potential future directions in this evolving field, emphasizing the continual refinement of these algorithms to cater to the increasingly complex demands of smart vehicle navigation.
THE COLLABORATIVE DESIGNING OF A PERSONALIZED HYBRID LMS USING A VIRTUAL MACHINE IN A CLOUD ENVIRONMENT

A teacher currently needs to acquire a huge amount of educational information and knowledge as part of his/her practice for the purposes of teaching, self-study, publishing, research, language support and related activities. In the academic environment, so-called learning management systems (LMSs), especially Moodle in the European Union, have proven suitable for the processing of educational content under e-Learning. LMSs usually function as online platform, at the same time as a supplement to academic information systems (AIS) on the local servers of universities. These are used by a larger number of participants, so these do not always meet the pedagogical needs of the teacher in all the activities he/she does. For example, these do not allow multilingual support, multiple searches and mass processing of e-Learning content. There are a large number of articles in the scientific literature describing the design of add-on modules for LMS. The authors offer their own approach to solving the problem of processing educational information to support a virtual team's work. As part of collaborative research in the field of technology-enhanced education and e-Learning design, the international team in the V4 EDUPORT project is implementing a personalized hybrid system on the IT infrastructure consisting of a specially designed desktop application WPad or web application (PIKS) and a common shared remote computer (virtual computer on a cloud server with Windows 2022). In this paper, the researchers share their experiences gained with their universities' academic LMSs (Moodle and AIS) and compare their personalization capabilities level to the teacher level and their use in the cloud. Part of current and future research is the development of the PIKS internet application, which operates as a multi-functional communication channel for e-Learning purposes (including use for the visually impaired). .
THE DEVELOPMENT OF A MOBILE APPLICATION ISMIND FOR FORMATIVE STUDENTS’ ASSESSMENT

Nowadays, mobile learning utilizing mobile applications has become widely adopted due to its flexibility and the numerous benefits it offers. However, the availability of mobile apps in education especially for assessment is limited and still an emerging topic despite the affirmation of its positive impact on students’ learning outcomes and motivation. The complexity of the mobile application development process, which includes requirement elicitation, modeling, designing, developing, testing, and evaluating, could be a contributing factor to this issue. This study aimed to propose a guideline for designing and developing an educational mobile application through the development of an application named IsMind that focuses on formative assessment. Evolutionary Prototyping, a methodology under the agile approach of Software Development Life Cycle (SDLC) was deployed to meet the educational application requirement by integrating content, pedagogy, and technology aspects to provide better teaching and learning material. The activities within each phase of evolutionary prototyping including the models, tools, techniques, and deliverables were discussed extensively. The analysis and design phase were done using an object-oriented approach with appropriate models (diagrams) to present the functional requirement of the mobile application. Various CASE tools which accelerated the development process in various stages of software development were explained.UMLet software was employed to create a use case diagram, activity diagram, and sequence diagram. The user interface design, which followed The Eight Golden Rules of Interface, was developed utilizing Marvel software. Adobe Photoshop CS6, Adobe Illustrator CS6, Unity 3D, and Microsoft Visual Studio Code (C#) were utilized in the development process. The mobile application provides a variety of question types, such as Drag And Drop, Multiple Choice, Numbering, True And False, Matching, and Puzzle. The evaluation of IsMind indicated its appropriateness as an interactive teaching and learning media for assessment. The proposed steps for constructing this assessment educational mobile app. The proposed methodology consists of the details of phases, various activities within each phase, techniques, models, and tools for each activity as well as deliverables of each phase offer a helpful guideline for anyone interested in developing a similar application.
ASSESSING THE EFFICACY OF CLOUD SERVICES FOR DEVELOPING EDUCATIONAL PRESENTATIONS

The article examines the most popular cloud services for creating educational presentations and seeks to determine the effectiveness of their integration within the educational framework. A webometric analysis of these cloud services resulted in the identification of the top 10 resources commonly employed for presentation creation, including Prezi, Sway, Slides, PowToon, Canva, ZohoShow, Genial.ly, Emaze, Piktochart, VideoScribe, among others. Our research conducted a comprehensive comparative analysis of these platforms, utilizing web analytics tools such as Ahrefs, Moz, and Similarweb to establish their respective rankings. This approach facilitated an encompassing selection of cloud-based educational tools capable of meeting the multifaceted requirements of all stakeholders in the educational process. The analytical process unfolded across multiple stages: an initial description and assessment of key parameters of each cloud service, subsequent expert evaluations encompassing both parameter specifics and overall service quality, determination of individual parameter impact coefficients on service efficiency, service evaluations based on specific parameters, and a synthesis of both objective and subjective data to summarize outcomes. In-depth analysis was conducted on the presentation creation features inherent to each cloud service, leading to the identification of evaluation parameters encompassing functionality, usability, cost-effectiveness, unique attributes, and overall user impression. Each parameter was assigned an impact factor to gauge its significance within the overall assessment of a given cloud service. The culmination of expert evaluations culminated in the recognition of Prezi, Canva, and Google Slides as highly effective tools. This determination is grounded in their extensive functionality, provision of free accounts, and user-friendly interfaces. Analysis of domain ratings further highlighted the popularity of Sway, Canva, and Prezi, underscoring the pronounced demand for these platforms among users. The evaluation methodology proposed within this study holds potential for application to other cloud services designed for educational content creation. By fostering effective organization of educational activities across diverse subject domains, this approach contributes to the enhancement of the educational process as a whole.
Dysphemism Strategies in President Buhari’s Arise TV Interview

This paper is an attempt to check the use of dysphemism in Political Discourse. It featured the functions and aim of dysphemism, and the role it plays in enabling political actors to hide under utterance to attack face of others. It tries to give reasons for the use of dysphemism as a impoliteness strategy that tends to be direct in effect. For the purpose of answering the research questions, President Buhari’s Arise TV interview of 10th July, 2021 was analyzed using Culpeper’s impoliteness strategies to identifying the use of dysphemism, its types and functions. The outcome of the analyses showed that dysphemism as used in Political Discourse is geared towards damaging tendencies as politicians attack the face of their opponents, diminish the target while maintaining and sustaining their ‘benefits’, and lastly, justify their action and reinforce their influence in social interactions.
Analysis of Livin' By Mandiri Application Quality With Iso 25010 Method

In the increasingly advanced digital era, mobile applications have become one of the important tools in providing banking services to customers. The Livin' by Mandiri application is one of the mobile banking applications that Bank Mandiri offers its customers. This study aims to analyze the quality of the Livin' by Mandiri application. This research uses the ISO 25010 method. Data collection techniques in this study were carried out by observation and literature study. The data obtained was then systematically analyzed in accordance with the ISO 25010 framework. The results showed that the Livin' by Mandiri application has a high level of functional suitability, is reliable and stable, fast and responsive, easy to use and navigate, well maintained and portable and can be used on various devices, including smartphones, tablets and laptops.
Inheritance

A major staple of object-oriented programming is the concept of inheritance. Inheritance is when types of data (such as classes) adopt fields – such as variables and methods – from another data type.
Subject Cataloging by Norwegian Cataloging Agencies

This article reviews the practices of subject assignment by the two main Norwegian cataloging agencies serving the public library domain, Biblioteksentralen and Bokbasen, analyzing 47,235 records representing media cataloged by both agencies, published between 2012 and 2019. In addition to descriptive statistics representing these practices, we apply the Panofsky/Shatford model, previously used in the analysis of artworks and images, to distinguish aspects of these practices associated with levels of meaning. We find that Biblioteksentralen tends to use more abstract terms in their descriptions, while Bokbasen tends to use more general terms.
Mouse-Aimed Camera

Before we give the player any means of moving around, we’re going to provide them with a first- and third-person camera system that lets them turn their character and camera with the mouse. Both systems will be handled by one script, and the player will be able to press a hotkey to smoothly switch from one to the other.