Reviewerly/exHarmony huggingface dataset:
Decision Support System for Selecting the Best Cryptocurrency Mining Machine Using the Multifactor Evaluation Process Method

Cryptocurrency mining is a process carried out using a specialised computer network in order to obtain new crypto assets. The cryptocurrency mining business in today's digital era is increasingly in demand by netizens. Many netizens run cryptocurrency mining businesses to generate new cryptocurrency assets that can be traded on the cryptocurrency market to earn huge profits. In doing cryptocurrency mining itself, it is necessary to be careful in choosing the cryptocurrency mining machine used to get the maximum profit. In this study, researchers proposed the Multifactor Evaluation Process as a decision support system method used to simplify the process of selecting the best cryptocurrency mining machine. The results of this study show that the best cryptocurrency mining machine that is most recommended to use is Cheetah Miner F5I (0.2120), followed by the alternatives iBeLink DSM7T Miner (0.2072), Bitfury RD4 (0.2016), Aladdin T1 16T (0.2016), and Obelisk SC1 Dual (0.1800).
Selection of Digital Investment Instruments Applying the Multi-Objective Optimization by Ratio Analysis Method

Choosing a digital investment instrument to make digital investments is not easy because behind the expected profits are also accompanied by balanced risks. Therefore, not a few novice investors are confused to determine the choice of digital investment instruments that are most appropriate for use in the long term. In this study the authors applied the decision support system method (Multi-Objective Optimization by Ratio Analysis) to facilitate the decision-making process in choosing digital investments for novice investors. The results of this study indicate that Alternative A4 (Trading) with a value of 0.17097 has the highest value compared to other alternatives, so Alternative A4 (Trading) is the most recommended digital investment instrument for use by novice investors.
Extending the Docstone to Enable a Blockchain-based Service for Customizable Assets and Blockchain Types

Document management ensures the accurate creation and storage of records within diverse organizations, and the process has been significantly enhanced by digitization. In this sense, blockchain has been relevant for meeting document management solution requirements. Nonetheless, there are still challenges in efficiently implementing blockchain infrastructure in organizations. In this context, this article extends previous studies that propose DocStone, an architecture designed to provide and facilitate the integration of blockchain-based assets for the registration and verification of client applications from different domains. In terms of contributions, this solution allows us to configure parameters to create customizable asset templates and select blockchains through a developer-friendly application programming interface. In this study, we discuss and evaluate an adaptation of Docstone’s architecture to support new assets and smart contracts in a custom way and a mechanism that automatically generates mapped IPFS directories for each smart contract. We performed an empirical study evaluating the deployment of smart contracts and data write and read operations using different types of assets. Moreover, we analyzed design decisions for decentralized applications to build DocStone. Notably, the study’s findings reveal that NFTs have higher deployment latency times than documents in Sepolia and Alfajores. However, documents have the lowest latency time on both networks for write and read requests. Alfajores was the blockchain that stood out the most, with the lowest latency of operations.
Evaluation of Optical Character Recognition (OCR) Systems Dealing with Misinformation in Portuguese

The performance of OCR techniques is highly dependent on the application context and the language being processed. Studies focused on languages such as Pt- Br and specific contexts are still scarce. Thus, in this work, we present an extensive analysis of the performance of OCR systems, specifically in the Brazilian Portuguese language, in the context of detecting misinformation spread through images on social platforms. To do this, we build a synthetic dataset considering texts from a Pt- Br fact-check labeled data and common patterns of images frequently shared on social media and messaging apps. Our results reveal the influence of analyzed image aspects on OCR accuracy highlighting those with the greatest impact. Further, we report a considerable variation among the evaluated OCR systems in terms of performance.
Ensemble Learning for Improved Cervical Cancer Classification in Pap Smear Images

The death rates for women are significantly influenced by cervical cancer. In order to reduce the possible effects of developing this condition, early detection and prompt treatment are essential. One of the most effective ways to detect the existence of cervical cancer is through the analysis of Pap smear pictures. The current paper describes a cooperative method for detecting cervical cancer in Pap smear images using deep learning models. We use two transfer learning models, such as MobileNetV3 and VGG19 augmented with additional layers, to capture data-specific features. To integrate the outputs of these models, we provide a special ensemble technique that focuses on decreasing the error values between the observed and ground-truth results. In the current studies, we found that VGG19 and MobileNetV3 had individual accuracies of 98.5% and 98.2%,respectively. However, when the advised ensemble technique was used, overall performance increased to 99.3%. This accomplishment shows the potency of our ensemble technique and outperforms the performance of the individual models. When compared to cutting-edge techniques, the suggested model shows competitive results. With our method, you can identify cervical cancer in Pap smear imagesfrom beginning to conclusion.
HIGH-QUALITY EDUCATION FOR UNIVERSITY STUDENTS

Education always plays an essential role in shaping and developing the personality of the young generation at any stage of human development. It can be said that the solid foundation of general education is based on the formation and development of human personality, and higher education is considered one of the necessary tasks toward the goal of improving the quality of human beings—comprehensive quality for the young generation. The article analyzes the current situation and factors affecting the quality of education activities for students at University. From there, propose solutions to improve the quality of education at the University.&#x0D; Keywords: Quality Education, Student, University.
Classification of Google Play Application Using Decision Tree Algorithm on Sentiment Analysis of Text Reviews

The study begins with a comprehensive background, examining the significance of classifying Google Play Store applications using user reviews. The problem statement revolves around the need for an efficient approach to classify application reviews to their sentiments, such as the user’s downloaded application not working as intended, and it can take time for users to read every single review. The project's objectives are to study the classifying approach of Google Play Store application reviews using the Decision Tree algorithm, develop a prototype of a classifying application program, and evaluate the accuracy model of the review classification program. To achieve these objectives, the methods employed involve data preprocessing and implementing the Decision Tree (DT) algorithm for classification. The classification model is trained and tested using various split ratios, and the optimal depth for the DT is determined through parameter tuning to achieve the best accuracy. Key results indicate that the developed prototype effectively classifies Google Play Store application reviews with an overall accuracy of 84.88%. This study has successfully achieved its objectives in creating a working Google Play application classification program. The classifier’s accuracy and user-friendly interface make it a valuable tool for developers and users.
Article Review/ The Tectonic Evolution of Erbil Area (Ne Iraq) as a Part of the Northeastern Margin of the Arabian-Nubian Plate Throughout Albian-Early Eocene

The Albian-Early Eocene tectonic evolution of the Zagros Foreland Basin of the High Folded Zone in the Iraqi Kurdistan region has been studied based on the recent foreland basin system concept. This study showed that during the cretaceous (Albian – Cenomanian) there was a geodynamic shift from a passive margin to a foreland basin system phase, not to an active margin phase as mentioned in the previous studies. The advance of the continental margin of the Arabian-Nubian Plate (ANP) toward the subduction zone imposed a tectonic load leading to the form of a flexural wave. The consequences of the last tectonic event were reflected by the continuous deposition of the Balambo Formation. in a foredeep depozone. This happened concomitantly with a flexural emergence of the continental shelf further to the west forming a forebulge depozone represented by the deposition of reefal facies of the Qamchuqa Formation. These geodynamic changes are considered here to represent the Megasequence boundary between the Tectonic Megasequence (TMS) of AP8 and AP9. The last sequence of the passive margin was the Qamchuqa Formation Which is called the Pre-Orogenic Carbonate Platform and was separated from the Bekhme Formation by regional unconformity (Megasequence boundary). The latter is represented by the Lower Sequence of the foreland basin system, which is called the Syn-Orogenic Carbonate Platform (SCP). During the Middle Campanian, the Zagros foreland basin started with the underfilled stage, and it was entirely clear by the end of the Cretaceous comprising a broad threefold subdivision of depositional realms that translated into three stratigraphic units known as trinity underfilled units of foreland basin system. The lower carbonate unit, the pelagic and hemipelagic unit, and the upper flysch clastic unit are represented by Bekhme, Shiranish, and Tanjero Formations respectively. All of these units were superimposed during basin depocenter migration and became younger toward the southwest in front of progressing the orogenic wedge toward the Arabian Craton. During the late Maastrichtian to Paleocene – Early Eocene, the Zagros foreland basin underwent a transition from underfill to fill the stage. Deep exhumation of the orogenic wedge and ophiolite assemblage had supplied the clastic sediment of the Kolosh Formation into the basin covering the foredeep and most of the forebulge and backbulge depozones.
Collaborative Filtering for Steam Games Recommendation

This research explores collaborative filtering with Singular Value Decomposition (SVD) and Pearson correlation to provide game recommendations on the Steam digital distribution service dataset. Collaborative Filtering aims to leverage user preferences to identify similar gaming patterns. This can be done through SVD and Pearson correlation algorithms. SVD reduces the dimensionality of the game rating matrix, while Pearson correlation measures the similarity between users and games. The proposed method generates personalized recommendations based on user preferences and opinions of similar gamers. Evaluation metrics include Mean Average Error (MAE) and Root Mean Square Deviation (RMSE). Results demonstrate the effectiveness of CF with SVD and Pearson correlation in delivering relevant and personalized game suggestions on Steam based dataset.
Domain Information Learning For Multi-Domain Knowledge Graph Link Prediction

The concept of instance changes with domains in a multi-domain knowledge graph. Methods for Link prediction typically focus on embedding the instances in the knowledge graph to infer missing links. However, multi-channel graph exists as multi-concept entities in which embedding entities and relations on an instance level may not be able to satisfy complete concepts. In addition, the methods with the usage of outer information are biased by the domain information. To address the issues, we propose a new bi-directional domain learning method to learn the representation of instances on a concept level with latent domain information in the multi-domain knowledge graphs. It consists of bi-directional sampling, multi-domain extension, and multi-channel attention to learn and extract concepts from domain knowledge graphs. The experimental results of link prediction show that our approach performs well by meeting the required criteria.
Blockchain Adoption in Saudi Manufacturing: The Conceptual Model and Research Hypotheses.

The increasing popularity of blockchain technology has drawn attention to its potential benefits across various industries. However, a notable gap exists in the literature concerning adopting blockchain technology in the Saudi manufacturing sector. This research aims to address this gap by exploring the factors influencing the adoption of blockchain technology from a theoretical standpoint. To achieve this, a comprehensive review of the existing literature on blockchain technology adoption was conducted, formulating a research model based on the Technology-Organisation-Environment (TOE) framework. The research model considers the crucial impact factors that influence adoption intentions, encompassing technological aspects (such as relative advantages, compatibility, and complexity), organisational factors (including top management support and organisational readiness), and environmental considerations (such as current regulations and government support). By examining these factors, this study sheds light on the specific dynamics surrounding blockchain technology adoption in Saudi Arabian manufacturing, providing valuable insight for strategic decision-making in Saudi Vision 2030.
Collaborative, Code-Proximal Dynamic Software Visualization within Code Editors

Software visualizations are usually realized as standalone and isolated tools that use embedded code viewers within the visualization. In the context of program comprehension, only few approaches integrate visualizations into code editors, such as integrated development environments. This is surprising since professional developers consider reading source code as one of the most important ways to understand software, therefore spend a lot of time with code editors. In this paper, we introduce the design and proof-of-concept implementation for a software visualization approach that can be embedded into code editors. Our contribution differs from related work in that we use dynamic analysis of a software system's runtime behavior. Additionally, we incorporate distributed tracing. This enables developers to understand how, for example, the currently handled source code behaves as a fully deployed, distributed software system. Our visualization approach enhances common remote pair programming tools and is collaboratively usable by employing shared code cities. As a result, user interactions are synchronized between code editor and visualization, as well as broadcasted to collaborators. To the best of our knowledge, this is the first approach that combines code editors with collaboratively usable code cities. Therefore, we conducted a user study to collect first-time feedback regarding the perceived usefulness and perceived usability of our approach. We additionally collected logging information to provide more data regarding time spent in code cities that are embedded in code editors. Seven teams with two students each participated in that study. The results show that the majority of participants find our approach useful and would employ it for their own use. We provide each participant's video recording, raw results, and all steps to reproduce our experiment as supplementary package. Furthermore, a live demo of our tool is available online. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https.//code.explorviz.dev We invite other researchers to extend our open-source software. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> https://github.com/ExplorViz Video URL: https://youtu.be/3qZVSehnEug
Extracting Unique Discussions of Interests for Entrepreneurs and Managers in a Set of Business Tweets without Any Human Bias

This study proposes a framework for extracting unique discussions of the interests of managers and entrepreneurs on Twitter (X). By unique discussions of interests, we mean those that are more tweeted by these communities but rarely by public people. These discussions can be facts and/or sentiments related to some topics. Since this is a subjective problem, human intervention can lead to bias in the results. Therefore, we propose an unsupervised method with zero information about the context since prior knowledge stems from human intervention. Consequently, there is no real ground truth. To retrieve such discussions of interests, first, unique tweets (discussions) are identified in two stages. In the first stage, a scoring algorithm is proposed that gives a score to each tweet of a specific year and tweets are sorted based on their scores. Different sets of tweets are selected based on their scores and considered automatically created ground truths. In the next stage, an unsupervised convolutional neural network trained on the created ground truth is used for the classification of tweets of other years (whether they are unique to these communities). Finally, latent Dirichlet analysis is applied to the detected unique tweets to give the most common interest topics discussed by these communities. Experimental analysis is performed on tweets from 2017-2019. The results reveal these communities’ attitudes and highlight interesting common and different topics discussed between managers and entrepreneurs; some of them can be difficult for humans to predict in advance. The proposed approach is applicable to any community.
Historical and cultural analysis of ideas on the formation of aesthetic culture of the individual

The article highlights some issues related to the formation of aesthetic culture of the individual through the historical and cultural prism. The analysis of philosophical ideas on the category of culture and aesthetics of the individual from the ancient era to the present is chronologically presented, which led to the derivation of basic research positions. The definition of the concept of aesthetic culture of the individual is presented, which is an instrument of holistic and conscious perception of beauty in art and reality, the ability to creative self-expression inherent in man. Attention is focused on the exclusivity of this property of personality as a natural and culturally appropriate human need, but which requires conscious, purposeful and systematic formation with prospective development. The author emphasizes that the primary goal in the formation of aesthetic culture is to rely on the identification of artistic and aesthetic characteristics of the individual with their prospective development to the level of aesthetically appropriate human ability to master the surrounding reality. It is assumed that such a level is manifested both in the integrated development of all segments of the aesthetic consciousness of the individual and in the development of skills and abilities of active transformative activity in art, work, life, and socio-cultural processes. It is substantiated that the main task of forming aesthetic culture is not only to expand the range of artistic perception, the number of books read, musical pieces heard, etc. but also to consciously generate feelings, spiritual growth of the individual, regulation and correction of behavior. In this context, the tasks of aesthetic education are specified, which are the development of sensory (sensual culture), the education of aesthetic tastes and value judgments, and the development of artistic creativity.
Public first aid education model design study based on user experience

Background Presently, China’s first aid penetration rate remains relatively low, leaving ample room for improvement in the existing first aid education model. Given its role as a service for the general public, public first aid education must thoroughly consider the learning needs and experiences of the public when designing the teaching mode. Methods Semi-structured interviews were employed to gather detailed insights into participants’ experiences in the first aid learning process. Subsequently, NVivo was utilized to analyze the interviews and identify specific design strategies. Additionally, a 7-point scale questionnaire was employed to assess the intervention effects of music familiarity and the simulation of teaching aids on users’ willingness and confidence in learning. Building upon the design strategy, a “feedback device + app” approach was proposed. Finally, user satisfaction was evaluated through a scale questionnaire. Results The use of familiar music had a significant positive effect on participants’ willingness and confidence to learn, while users’ fear of teaching aids had no effect on willingness and confidence. The user experience-based first aid education model can better meet the public’s learning needs for first aid knowledge and skills. Conclusion This study proposes a first aid education model based on user experience design methodology, which optimizes the public’s self-learning experience by evoking positive emotions while circumventing negative emotions. The educational model was recognized by users in terms of design concepts and is expected to help increase first aid prevalence in the future.
Integrating Quantum and Satellites: A New Era of Connectivity

Quantum satellite networking is central to enabling quantum key distribution (QKD) and quantum-state transfer. Delving into the synergistic relations of quantum technology and satellite communications, we elucidate on continuous variable and discrete variable QKD schemes, and the strategic selection of satellite orbits for optimal QKD. The prospects and possibilities of the integration of software-defined networking into this paradigm showcases a fusion of traditional and quantum communication methods. In the context of continuous variable quantum systems, a comparative discussion on Gaussian and non-Gaussian paradigms is presented, emphasizing their distinct characteristics crucial for satellite-assisted quantum networks. Moreover, we underscore the role of quantum computing in bolstering the security of satellite information networks, emphasizing the importance of delay reduction for the connection of Internet of Things (IoT) devices to satellite networks. Lastly, we propose potential domains ripe for future exploration in this burgeoning field.