Reviewerly/exHarmony huggingface dataset:
Intelligent information extraction from government on-site inspection reports of construction projects: A graph-based text mining approach

Government inspection reports detail unsafe acts and conditions that arise on construction sites, especially front-line managers’ non-compliance practices, which are hardly identified during self-inspections. Such information serves as a valuable learning source for better construction management. However, non-compliance issue records in inspection reports are typically stored in unstructured text formats, making data analysis challenging. In response, an intelligent text mining framework integrating graph analysis and visualization is presented. The proposed framework comprises data collection and preprocessing and three levels of text analysis: word, sentence, and document. The main tasks of the word-level analysis include (1) extracting keywords using KeyBERT and (2) identifying non-compliance issue types based on community detection in a keyword co-occurrence graph. The sentence-level analysis is performed to automatically classify text data from inspection reports by determining the degree of similarity between texts and communities and assigning the most similar community to each text. The document-level analysis aims to identify the interrelations between various non-compliance issues through association rule mining and a community interaction network. The framework is validated by a total of 6,153 text data featuring non-compliance issues from 322 government on-site inspection reports in Shanghai, China. The results demonstrate that the critical word-level features of non-compliance issues can be accurately identified using KeyBert, which outperforms other state-of-the-art methods. Our approach can also automate the development of a data-driven taxonomy for non-compliance issues and the classification of the corresponding records, requiring less manual intervention than conventional text classification models.
Proposal of Knowledge Network Model Education for STEM/STEAM Education

Information and communication technology (ICT) education is important for the younger generation because of the rapid evolution of technology. Both science, technology, engineering, and mathematics (STEM) and science, technology, engineering, the arts, and mathematics (STEAM) educations are important in the twenty-first century. Why do we have to think about STEM/STEAM education for the young generation? The answer lies in both creativity for the younger generation as well as a better quality of life (QOL) in the future. ICT technology is quickly evolving, and in the future, the younger generation will have to use more ICT technology. Actually, this paradigm shift is promoted in the new era of Industrial 5.0 and Society 5.0. In this research, we investigate how can we create new knowledge using knowledge graphs in education for 21st education, Industry 5.0 or Society 5.0. We show the visualization of complex networks of keywords that is too difficult to understand. Moreover, we show the simpler network by text mining. In STEM/STEAM education, it is important for the Industry 5.0 or Society 5.0 era. Therefore, we propose knowledge network model education for STEM/STEAM education or 21st education for ICT.
Investment decisions: Comparative analysis of the performance of cryptocurrencies Bitcoin, Gold and Stocks

The purpose of the study is to compare the performance between bitcoin Cryptocurrency, S&amp;P500 stocks and Gold which can be a consideration for investors in determining investment decisions. . This research is quantitative research using comparative methods. The population and sample used in this study are the monthly closing prices of the three investment instruments that are the subject of research with saturated sampling in the period 01 January 2019 – 31 December 2022 as many as 144 data. In this study, the type of data used is secondary data in the form of historical data of instruments. Then researchers used a non-parametric statistical test, the Kruskal-Wallis’s test. The results show that there is no significant difference between Treynor and Jensen’s returns from Bitcoin, Gold and S&amp;P500. While the risk and Sharpe Ratio show that there is a significant difference. The best investment instrument in 2019-2022 is bitcoin because when viewed from the average return bitcoin is able to have the highest percentage compared to other instruments. Gold is an asset that is often considered a refuge in situations of economic uncertainty. Meanwhile, stocks in that period experienced a huge price decline, this was caused by the COVID-19 pandemic.
Text Classification of Patient Experience Comments in Saudi Dialect Using Deep Learning Techniques

Improving the quality of healthcare services is of the utmost importance in healthcare systems. Patient experience is a key aspect that should be gauged and monitored continuously. However, the measurement of such a vital indicator typically cannot be carried out directly, instead being derived from the opinions of patients who usually express their experience in free text. When it comes to patient comments written in the Arabic language, the currently used strategy to classify Arabic comments is totally reliant on human annotation, which is time-consuming and prone to subjectivity and error. Thus, fully using the value of patient feedback in a timely manner is difficult. This paper addresses the problem of classifying patient experience (PX) comments written in Arabic into 25 classes by using deep learning- and BERT-based models. A real-world data set of patient comments is obtained from the Saudi Ministry of Health for this purpose. Features are extracted from the data set, then used to train deep learning-based classifiers—including BiLSTM and BiGRU—for which pre-trained static word embedding and pre-training vector word embeddings are utilized. Furthermore, we utilize several Arabic pre-trained BERT models, in addition to building PX_BERT, a customized BERT model using the PX unlabeled database. From the experimental results for the 28 classifiers built in this study, the best-performing models (based on the F1 score) are found to be PX_BERT and AraBERTv02. To the best of our knowledge, this is the first study to tackle PX comment classification for the Arabic language.
Portable Augmented Reality installation with hologram special effect for kids’ education to learn everyday objects

The utilization of Information Technology has a great opportunity to be explored to support students in learning the materials. Presentations can help students learn new content more effectively, especially for children learning everyday objects. Learning can be made more interesting, memorable, and participatory by using Augmented Reality technology. Holograms are a hybrid of Augmented Reality and Virtual Reality, often used in amusement park rides. Generally, visitors can feel their emotions while riding the rides, and these recollections are preserved in their memories. Based on the benefits of AR and hologram special effects, researchers combined the “wow” effect of these things. They created a solution in education by using a hologram special effect and combining them with AR technology. When combined interactively with AR technology, the hologram special effect is predicted to become a good teaching approach. This solution aims to develop a portable AR installation with hologram special effects on a low budget, making it viable to apply in education and capable of helping students learn creatively.
Data security and data sharing product knowledge between organizations using blockchain technology

The digital world has developed very rapidly in the last 10 years. Data and information are everywhere and are already in digital form so that they can be accessed anytime and anywhere. Exchange of information, products, and knowledge also occurs between companies or organizations. This paper uses the Systematic Literature Review (SLR) application to determine the extent to which trends in the use of blockchain technology are applied, especially for data security such as data, information, and knowledge belonging to organizations—applying blockchain technology to share information with internal organizations or between organizations. Researchers found that data security with blockchain technology has been widely applied in various industries.
Young Generation Point of View About Non-Fungible Token as a Future Currency and Assets

This research focuses on how artists and the young generation perceive the use of NFT and their impact on the future value of currencies. It increases the author’s curiosity about the reactions of artists and the young generation to the use of NFT and its impact on future currencies. The authors also wanted to know why they are publishing digital works on NFT and whether they think NFT is the future of digital assets researchers or just a bubble. This study uses a qualitative method with semi-structured interviews with artists and the younger generation with the criteria of understanding and using NFT, understanding and not using NFT, and not understanding and not using NFT. The result that the authors get from this research is that the use of NFT has been widely used by artists and the younger generation. They say that the publication of digital works can be guaranteed. Another interesting thing is that they consider NFT to have a great opportunity as an asset in the future, where the use of cryptocurrency also affects the value of the currency in the future. However, the younger generation is not interested in NFT. They prefer NFT to be implemented as a payment system in the future.
Exploring the Potential of Pre-trained DCNN Models for Facial Emotion Detection: A Comparative Analysis

Emotion detection from people’s facial expressions is important nowadays to know how other humans feel, such as the interaction of an AI machine with humans, which is popular. One of them is an AI avatar. Sometimes these machines do not know what the human partner feels, so their decisions can be inaccurate. Here, AI avatars can be used to monitor human partners’ healthcare conditions, such as stress, depression, and anxiety that can cause suicidal death. This research aims to get the best model to detect emotion from facial expressions by comparing some DCNN pre-trained models. The pre-trained DCNN models that are used in this research are VGG16, VGG19, ResNet50, ResNet101, Xception, and InceptionV3. This research used accuracy, precision, recall, and f-1 score to evaluate all models. The result shows that the VGG19 model has the highest accuracy than other models, which is 65%. The research can conclude that the performance of a model is dependent on various factors, such as the size and quality of the dataset used for the research, the complexity of the problem that needs to be achieved, and the hyperparameters used for the dataset, while training.
Web-Based Employee Competence Database Design

One of the challenges faced in this era includes current information, namely the handling and management of big data related to employee company information. The training and development section still uses programs that are not yet integrated, which impacts the slow process of tracing the required staffing data to process staffing big data. For this reason, Web-Based Employee Competency Database Application is designed as an integrated platform that can simplify and speed up the process of managing, storing, and tracking big data related to employee data, monthly training, and planning annual training for each employee of the division. The methods used are waterfall, Unified Modelling Language diagrams, Entity Relationship Diagrams, Blackbox testing, User Acceptance Testing, and the Eight Golden Rules. The results of the research are applications that are used as integrated platforms that speed up the process of managing, storing, and tracking employee competency data with features developed, such as user profile menus, employee data, monthly training, and annual training planning. Overall, it successfully meets the specifications required by the training &amp; development section. All functions in each menu have been tested without failure according to the user’s access rights and meet the good development rules for the user.
Specification Mining over Temporal Data

Current specification mining algorithms for temporal data rely on exhaustive search approaches, which become detrimental in real data settings where a plethora of distinct temporal behaviours are recorded over prolonged observations. This paper proposes a novel algorithm, Bolt2, based on a refined heuristic search of our previous algorithm, Bolt. Our experiments show that the proposed approach not only surpasses exhaustive search methods in terms of running time but also guarantees a minimal description that captures the overall temporal behaviour. This is achieved through a hypothesis lattice search that exploits support metrics. Our novel specification mining algorithm also outperforms the results achieved in our previous contribution.
Expert System Development on Psychological Tests using a Mobile-Based 16 PF Measuring Instrument

A personality or psychological test is a measurement or assessment activity through a systematic effort to reveal certain psychological aspects of an individual. This writing is structured to determine a mobile-based online personality test using the 16PF test tool in determining employee positions. The test also includes a comprehensive measure of normal personality which has been shown to be effective because it requires an in-depth needs assessment. The choice of this research is because the role of employees is significant in a company. Employee performance depends on the placement of the employee’s position. The subjects used in this study were employees who would be placed in a job and used the 16 PF (The Sixteen Personality Factor Questionnaire) measurement method. Expert System is a system built to transfer the ability of one or several experts into a computer that is used to solve problems faced by users in a particular field. The author builds a mobile-based expert system application that is able to help recognize a person’s personality. The process of making the application uses an object-oriented methodology with Unified Modeling Language (UML) visual modeling. The results of this study indicate that the 16 PF measuring instrument can be used to place employees in a position based on their personality.
Two-Stage Superimposed Gold Mineralization in the Xiejiagou Gold Deposit, Shandong Province: Insights from Fluid Inclusions, H-O-S Isotopes, and Trace Elements

The Xiejiagou gold deposit located in the Zhaoyuan-Laizhou gold belt is composed of altered-rock-type gold mineralization and superposed auriferous quartz veins, showing unique two-stage gold mineralization. Oxygen and hydrogen isotopic analyses yielded the following results: δ18OH2O = 0.8‰ to 4.4‰ and δD = −106‰ to −85‰ for altered-rock-type mineralization, and δ18OH2O = 3.6‰ to 5.6‰ and δD = −98‰ to −89‰ for auriferous quartz-veins. Combined studies on Co/Ni, Sb/Bi and As/Ag ratios of pyrites, it can be inferred that the ore-forming fluids were dominated by magmatic water mixed with very little meteoric water. The fractured altered rocks in the ore-hosting fault zones are characterized by mylonitization, cataclastic lithification, and structural lenses, reflecting a compressional (closed) ore-forming system. In contrast, the occurrence of auriferous quartz veins in fissures of altered-rock-type orebodies and the fact that altered-rock-type ores commonly occur as breccias cemented by auriferous quartz veins that reflect an extensional (open) ore-forming system for the vein mineralization. The increase in δ34S values from stage I (5.8‰–7.2‰) to stage II (6.6‰–9.0‰) indicate that the altered-rock-type mineralization was the result of intense water–rock interaction, while the occurrence of immiscible inclusions in auriferous quartz veins demonstrates that fluid immiscibility contributes significantly to gold deposition in the vein-type mineralization. Fluid-inclusion microthermometric data indicate that the fluids for the altered-rock-type mineralization are characterized by moderate-to-high temperature (262–368 °C), and low-to-moderate salinity (4.3–10.8 wt.% NaCl equivalent). In contrast, halite-bearing inclusions are found in auriferous quartz veins, and its fluids are characterized by moderate-to-high temperature (290–376 °C) and moderate-to-high salinity (5.1–41.9 wt.% NaCl equivalent). From early stages (I and II) to the late stage (III), homogenization temperature and high temperature element (W, Sn, and Mo) concentrations in pyrite first decrease and then increase, δ34S values and metallization-related element (Au, Ag, and Bi) concentrations in pyrite first increase and then decrease. Therefore, it can be inferred that the two distinct types of gold mineralization in the Xiejiagou gold deposit may be two separate mineralization events. The presence of magnetite in the auriferous veins suggests an increase in oxidation state during the vein mineralization. Importantly, the Xiejiagou gold deposit preserves two types of mineralization in a single deposit and uniquely records a metallogenic transition from a compressional, reduced environment to an extensional, oxidized environment, as a result of a regional stress field transition that occurred in the Zhaoyuan-Laizhou gold belt at ca. 120 Ma.
University Students Using Mobile Application as Self-Directed Learning Tool for Learning Chinese

This research aims to examine about using mobile application as self-directed learning tool for learning Chinese. We chose Manga-Mandarin mobile application as the learning source. The participants were Bina Nusantara University students from different majors and joined in this research voluntary. As the facilitators we provided the learning plan in three weeks length of study. We interviewed the participants to gather the data about how the process of their self-directed learning and their experiences using the mobile application. We found out that all of the participants had completed the self-directed learning process and most of the participants were aligned with Garrison self-directed learning three dimensions, self- management, self-monitoring, and motivation. Most of the participants also felt using mobile application to self-learning Chinese was a valuable experience.
Age estimation through facial images using Deep CNN Pretrained Model and Particle Swarm Optimization

There has been a lot of recent study on age estimates utilizing different optimization techniques, architecture models, and diverse strategies with some variations. However, accuracy improvement in age estimation studies remains a challenge due to the inability of traditional approaches to effectively capture complex facial features and variations. Therefore, this study investigates the usage of Particle Swarm Optimization in Deep CNN models to improve accuracy. The focus of the study is on exploring different feature extractors for the age estimation task, utilizing pre-trained CNN models such as VGG16, VGG19, ResNet50, and Xception. The proposed approach utilizes PSO to optimize the hyperparameters of a custom output layer for age detection in regression. The PSO algorithm searches for the optimal combination of model hyperparameters that minimize the age estimation error. This study shows that fine-tuning a model can lead to improvements in its performance, with the VGG19 model achieving the best performance after fine-tuning. Additionally, the PSO process was able to find sets of hyperparameters that were on par or even better than the initial hyperparameters. The best result can be seen in VGG19 architecture with loss of 86.181, MAE of 6.693, and MAPE of 38.462. Out of the twelve experiments conducted, it was observed that the utilization of Particle Swarm Optimization (PSO) offered distinct advantages in terms of achieving better results for age estimation. However, it is important to note that the execution time for these experiments was considerably longer when employing PSO.
The Influence of Technology to Hand Writing Chinese Character Ability

Nowadays students mostly used a pinyin input method to study Chinese, which had adverse effects on their Chinese character writing skills. The research method using qualitative method, where the researcher gave two types of tasks to 58 students, some of whom used the pinyin input method and some who did not. According to the findings, there are 67.25% of the students (38 out of 58) making errors when writing by hand. These errors included 47.44% of errors related to similarities between Chinese characters, which could be categorized into three types: word formation similarities (5%), character component similarities (15.25%), and single letter similarities (27.11%). Additionally, 25% of the errors were sound equation errors, such as “�” as “_f” (“shì”). Furthermore, 27% of the errors were related to the formation of new Chinese characters. When the students used the pinyin input method, the percentage of errors decreased significantly to 25.86%, most of the errors made using this method were due to word selection errors (60%) and vowel errors (40%). The research suggests that using the pinyin input method may have a negative impact on students’ motivation to write Chinese characters, which, in turn, can affect their handwriting skills.
Beevirale Multimedia Website for Distance Learning Introduction

In the world of education, schools and colleges are not allowed to conduct in-person or often offline classes at campuses during the COVID-19 pandemic. All teaching and learning processes should be done online from home. An alternative to this policy is to use distance learning (PJJ) methods. PJJ is now conducted online (on the network) through internet-based media. At the University, PJJ is conducted using a learning management system (LMS). The method for this research procedure used to determine the impact of web-based learning in drafting courses used the ADDIE model research approach and the result of student reaction to web-based learning media is that learning media are not just tools, they must be able to enhance students’ curiosity about the material to make it available to others. Learning can be done in a variety of ways, not just the traditional way. Due to the technology used, the media is accessed using only the Internet network, so users can access any device with an Internet network without consuming storage space on the device, WEB-based learning media related to the development of ICT-based learning media technology can be used as learning media for online and offline lectures.
Zooming in on Dewey, Democracy, and Subjectivity in Postdigital Education

Abstract Digital tools, such as video conference technology, are currently transforming people’s behaviour, social relations, and learning processes in higher education. But the digital tools used in teaching and learning are also part of a dynamic capitalism. This article focuses on democracy and subjectivity in higher education and outlines a critical approach in this context. I revisit John Dewey’s philosophy to reflect on digital tools and the goals of education in a democratic society. Published more than a century ago, Dewey’s seminal book Democracy and Education is relevant as a constructive approach to combining learning, experience, habits, and tools. Dewey argued that dualisms between mind and matter, knowledge and nature, undermine a democratic society. For Dewey, a society would require communication and the sharing of experiences whilst education would require democratic aims beyond the individual. Yet the contemporary challenges include a ‘digital gaze’ where the technology makes subjects more visible and observed. Foucault’s analytics of power thus becomes a constructive supplement to Dewey’s focus on participation and modes of inquiry to fully examine subjectivity and democracy in postdigital higher education.