Reviewerly/exHarmony huggingface dataset:
Reconstructed SqueezeNext with C-CBAM for offline handwritten Chinese character recognition

Background Handwritten Chinese character recognition (HCCR) is a difficult problem in character recognition. Chinese characters are diverse and many of them are very similar. The HCCR model consumes a large number of computational resources during runtime, making it difficult to deploy to resource-limited development platforms. Methods In order to reduce the computational consumption and improve the operational efficiency of such models, an improved lightweight HCCR model is proposed in this article. We reconstructed the basic modules of the SqueezeNext network so that the model would be compatible with the introduced attention module and model compression techniques. The proposed Cross-stage Convolutional Block Attention Module (C-CBAM) redeploys the Spatial Attention Module (SAM) and the Channel Attention Module (CAM) according to the feature map characteristics of the deep and shallow layers of the model, targeting enhanced information interaction between the deep and shallow layers. The reformulated intra-stage convolutional kernel importance assessment criterion integrates the normalization nature of the weights and allows for structured pruning in equal proportions for each stage of the model. The quantization aware training is able to map the 32-bit floating-point weights in the pruned model to 8-bit fixed-point weights with minor loss. Results Pruning with the new convolutional kernel importance evaluation criterion proposed in this article can achieve a pruning rate of 50.79% with little impact on the accuracy rate. The various optimization methods can compress the model to 1.06 MB and achieve an accuracy of 97.36% on the CASIA-HWDB dataset. Compared with the initial model, the volume is reduced by 87.15%, and the accuracy is improved by 1.71%. The model proposed in this article greatly reduces the running time and storage requirements of the model while maintaining accuracy.
Pengaruh Media Pop Up Book berbasis Multisensori terhadap Kemampuan Bahasa Anak Usia Dini di Taman Kanak-Kanak Perwati

The purpose of this study was to find out whether the learning method using Multisensory-based Pop Up Book media affects the language skills of Early Childhood at Perwati Kindergarten. The method used is a quantitative research method. The sample in this study totaled 22 children, consisting of 11 children in class B1 and 11 children in class B2. Data collection techniques using structured observation. Based on the results of research data analysis conducted, it was found that the language skills of early childhood in the experimental class using multisensory-based pop up book media were higher than the control class. The pretest average of the experimental class was 11.91 and that of the control class was 10.91. Meanwhile, the average post-test for the experimental class was 18.76 and for the control class was 16.73. Based on the pre-test and post-test data, the average score for the experimental class was 6.46, while the average score for the control class was 5.9. Based on the sig (2-tailed) value is 0.02 &lt;0.5. Thus there is a significant difference between the control class and the experimental class. So that it can be terminated Ha is accepted and H0 is rejected. With the results of the study it can be concluded that the use of multisensory-based pop up book media has an effect on early childhood language skills
Artificial intelligence for oral cancer diagnosis: Data quality and image acquiring

• AI could assist in oral cancer diagnosis, more studies have been conducted in recent years. • Quality of the collected data can directly affect the Convoluted Neural Network diagnosis performance. • A standardized protocol for image capture is needed.
Lived Experiences of Public School Teachers in Teaching Computer System Servicing in the New Normal

To effectively teach many specialized disciplines, learning by doing is essential.Computer System Servicing (CSS) is one of the skilled subjects where hands-on activities play a major role in ensuring the transfer of learning.The most effective method to teach this subject is for teachers to have direct supervision of their students.However, during the time of COVID-19 pandemic, there was a sudden shift from face-to-face instruction to online distance learning which led to many challenges for teachers, and they were forced to adjust to this new normal mode of education.The purpose of this qualitative study is to assess teachers' health conditions and identify effective instructional methods and online learning tools in teaching Computer System Servicing via online distance learning from the lived experiences of 8 public school teachers in Marikina City.The participants were chosen through a purposive sampling method, and a validated interview questionnaire was used as an instrument.Moreover, the themes of this study were determined using Giorgi's method of analysis.Based on the findings, (7) seven themes emerged from the lived experiences of teachers which are: (1) changes in teachers' routines during the pandemic; (2) factors that affect teachers' mental health; (3) teachers' emotional response to the pandemic; (4) decrease in social activities; (5) teachers' response to the challenges of online delivery of lessons; (6) utilizing interactive online tools and platforms in teaching CSS courses online; and (7) a sense of dissatisfaction with teachers' teaching strategies and resources.It is concluded that the pandemic had significant effects on teachers' well-being; their teaching experiences have been described as challenging; the use of interactive technologies is effective for practical applications of core competencies of CSS; they were not satisfied with their online delivery of instructions due to lack of available resources; and there is a need for the revision of existing CSS self-learning modules.
Survival Prediction with Extreme Learning Machine, Supervised Principal Components and Regularized Cox Models in High-Dimensional Survival Data by Simulation

Mortality risks of important diseases such as cancer can be estimated using gene profiles which are high-dimensional data obtained from gene expression sequences. However, it is impossible to analyze high-dimensional data with classical techniques due to multicollinearity, time-consuming processing load, and difficulty interpreting the results. For this purpose, extreme learning machine methods, which can solve regression and classification problems, have become one of the most preferred machine learning methods regarding fast data analysis and ease of application. The goal of this study is to compare estimation performance of risk score and short-term survival with survival extreme learning machine methods, L2-penalty Cox regression, and supervised principal components analysis in generated high-dimensional survival data. The survival models have been evaluated by Harrell’s concordance index, integrated Brier score, F1 score, kappa coefficient, the area under the curve, the area under precision-recall, accuracy, and Matthew’s correlation coefficient. All results showed that survival extreme learning machine methods that allow analyzing high-dimensional survival data without the necessity of dimension reduction perform very competitive with the other popular classical methods used in the study.
Cultural content setting of middle school English textbooks integrating China traditional cultural elements

The article discusses the cultural content setting strategy of middle school English teaching materials with China traditional cultural elements. Integrating traditional culture into ELT (English Language Teaching) can not only improve students' understanding of local traditional culture, but also help students improve their English level.
Introduction

There is only one way to learn “R”, download “R” (and “R-studio”, which makes life simpler) and start typing. Simply type anything and you will learn by doing. Well, probably you will need a helping hand, but the message is that you need to practice using “R”, a lot!
Research on Investor Sentiment and Stock Market Performance Based on Text Mining of Online Stock Forums

This study examines the relationship between investor sentiment, derived from online stock forum posts on the East Money website, and the closing price and trading volume of the Shanghai Stock Exchange Composite Index (SHCI).The findings reveal that different types of investor sentiment exert distinct effects on the stock market.Negative sentiment, identified through text mining of stock forum posts, demonstrates consistent predictive power for the SHCI's closing price.Moreover, changes in the proportions of positive and neutral sentiment, also extracted from stock forum posts, exhibit an immediate impact on the SHCI's trading volume, with a one-day lag.Thus, the study establishes the significant role of investor sentiment, extracted from stock forum posts, in predicting stock market performance
Attitudes of Al-Ahsa fans towards the tourism media content in the Qatar World Cup 2022

This study aimed to know the attitudes of Al-Ahsa fans towards the tourism media campaigns for the World Cup Qatar 2022. It explores their attitudes towards the media content and explores the extent of their satisfaction with these campaigns. The study sample consisted of (385) people. The study followed the descriptive survey method, and an electronic questionnaire was used as a tool for collecting the data. The study found that the vast majority of the fans from Al-Ahsa Governorate believed that the media campaigns held during the Qatar World Cup 2022 highlighted the tourist attractions in Al-Ahsa Governorate in a professional manner, and the campaign organizers succeeded in choosing the appropriate time to broadcast these campaigns to the public and attract them to Al-Ahsa Governorate. The majority also reported that the Social Media Platforms and traditional media tools were rich with these campaigns that highlighted the Al-Ahsa and its cultural and historical landmarks, and contributed to the revitalization of the economy of Al-Ahsa Governorate.
Detection of Fake Reviews Using Machine Learning Techniques

Online marketing generates vast amounts of digital information, which is then used to advertise millions of goods and services. Finding the best services or goods that meet the need is, therefore, difficult. Customers make decisions based on evaluations or opinions expressed by third parties. In this cutthroat environment. For the past few years, some businesses have employed writers to fabricate favorable reviews of their services or goods or unfairly critical reviews of those of their rivals in order to increase the number of false reviews. Review sites are increasingly having to deal with the distribution of false information that either benefits or hurts particular firms. Different types of opinion spam are used to deceive both human readers and automated sentiment analysis and opinion mining systems. Different strategies have been put forth as a result, allowing for the evaluation of the user-generated content's credibility. The current paper provides a quick introduction to the examination of opinion mining and the review-centric attributes that have been suggested for use in machine learning techniques that use graphical methods to identify bogus reviews. Along with a review of the related literature and a discussion of it.
Developing Emergency Location-Based Services Applications using Agile Methodology

Mobile phone applications have gained tremendous attention over the past few years due to an increase in mobile phone downloads and revenue. However, the more applications are produced, the more applications fail. Mobile application statistics indicate that the main drivers of application failure include high consumer/consumer expectations, inadequate marketing efforts, and technical problems resulting from developers failing to use appropriate methodologies. Some of these applications may not be important, but failures in mobile applications used by civil protection to provide important information in times of crisis, such as location-based services, are not acceptable in any way. This paper provides a framework that uses the Scrum methodology to develop a mobile application to alert citizens to risks and manage the team to improve collaboration, coordination, and communication between the team (s) to get quick feedback from users to address rapid developments in context and achieve desired goals.
Kajian Literature: Implementasi Perpustakaan Digital di Perguruan Tnggi

This study aims to describe the implementation of digital libraries in universities. This research is a descriptive study with a literature review method. Five articles were selected that discussed the differences and similarities in user satisfaction between undergraduate and postgraduate students regarding the existence of digital libraries. This study discusses similarities, dissimilarities, provides views, compares and summarizes selected articles. It was found that there are differences and similarities in user perceptions in the use of higher education digital libraries, factors that influence user satisfaction with digital libraries such as system quality, information, and services, digital library affinity, perceived ease and satisfaction felt by users. The five selected articles stated that in a group of academics in which there were undergraduate and postgraduate students had different perceptions of digital libraries. Undergraduate students tend to emphasize speed of access, while postgraduate students prioritize the validity and up-to-date of an information source to meet their needs for information. The results of this study are expected to be a material consideration for policy makers in planning the development of digital libraries so that they are right on target so that their utilization can be maximized
INTEGRATED SYSTEM IN FORECASTING STOCKS OF GOODS USING THE EXPONENTIAL SMOOTHING METHOD

Determining the stock of goods is mandatory for a company of buy and sell goods, especially when there are many types of goods to sell. One of the companies that means is PT. Capella Dinamik Nusantara. The company is one of the largest Honda motorcycle distributor companies in Riau. PT. Capella Dinamik Nusantara also sells and purchases Honda motorbike spare Parts for its customers. Considering that there are quite a lot of types of Honda motorbikes and for one Honda motorbike there are already a lot of spare Parts, a system is needed that is able to store all motorbike data and spare Parts, and if possible can help predict the stock of items needed. This research aims to create a decision support system that can help PT. Capella Dinamik Nusantara in predicting the amount of spare part stock needed. The method used in this system is the Exponential Smoothing method with the programming language used is the PHP language which makes this system a Web-based system that is used locally.
Capturing Formality in Speech Across Domains and Languages

The linguistic notion of formality is one dimension of stylistic variation in human communication.A universal characteristic of language production, formality has surface-level realizations in written and spoken language.In this work, we explore ways of measuring the formality of such realizations in multilingual speech corpora across a wide range of domains.We compare measures of formality, contrasting textual and acoustic-prosodic metrics.We believe that a combination of these should correlate well with downstream applications.Our findings include: an indication that certain prosodic variables might play a stronger role than others; no correlation between prosodic and textual measures; limited evidence for anticipated inter-domain trends, but some evidence of consistency of measures between languages.We conclude that non-lexical indicators of formality in speech may be more subtle than our initial expectations, motivating further work on reliably encoding spoken formality.
The Effects of Input Type and Pronunciation Dictionary Usage in Transfer Learning for Low-Resource Text-to-Speech

We compare phone labels and articulatory features as input for cross-lingual transfer learning in text-to-speech (TTS) for lowresource languages (LRLs).Experiments with FastSpeech 2 and the LRL West Frisian show that using articulatory features outperformed using phone labels in both intelligibility and naturalness.For LRLs without pronunciation dictionaries, we propose two novel approaches: a) using a massively multilingual model to convert grapheme-to-phone (G2P) in both training and synthesizing, and b) using a universal phone recognizer to create a makeshift dictionary.Results show that the G2P approach performs largely on par with using a ground-truth dictionary and the phone recognition approach, while performing generally worse, remains a viable option for LRLs less suitable for the G2P approach.Within each approach, using articulatory features as input outperforms using phone labels.
Secure and Transparent Elections: Exploring Decentralized Electronic Voting on P2P Blockchain

Voting is one of the methods used by humans to determine decisions in a case. Along with the development of technology, conventional voting also follows the transformation in the adaptation of the current digital era. There is an electronic version of this voting which is often called e-voting (electronic voting). Of course, the application of technology in voting will raise public opinion about concerns about maintaining network privacy and security in e-voting. Of course, security problems can be overcome with technology that is currently being utilized optimally in various fields of life, namely blockchain technology. In enhancing e-voting security, blockchain will be used in P2P networks. The application of the blockchain system in e-voting will produce a safe e-voting system so that it can reduce the percentage of forgery of votes in the e-voting system. Implementing a blockchain-based e-voting system will involve 3 elements, namely cryptography, software engineering theory, and voting theory. In realizing blockchain-based e-voting, several stages are needed. (1) vote counterfeiting is avoided by adjusting the voting recording model based on ledger technology, (2) in terms of authentication, this technology is equipped with elliptic curve cryptography (ECC) based user model design, (3) providing a model designed for voters to change their vote before the specified time limit. The integration of these three designs is an important requirement in the e-voting process, proving and verifying a blockchain-based e-voting system in a P2P network that has been designed on linux.
Design and implementation of Elasticsearch-based intelligent search for home shopping platform

The search function of shopping software is essential, and good recommendations can increase the user's desire to buy. The fuzzy search and image similarity search proposed in this paper is a new retrieval method built on Elasticsearch, which can speed up the search and improve the retrieval's correctness. Its support for various complex texts dramatically facilitates the development of this project. This search type is used in home shopping software to improve the user's comfort significantly. The text is developed and designed based on the Golang language, whose high concurrency and excellent library functions help implement the functionality extensively. The user side is presented as a WeChat applet, which lowers the threshold of use and increases the dependency of users. With Elasticsearch's support for multiple languages and its unique vector search and text embedding features, the system can train models such as Contrastive Language-Image Pretraining (CLIP) and Natural Language Processing (NLP) on different images and languages, improving the search's accuracy. For the model generated by the training, vector search is performed to achieve the purpose of the search, and finally, the search results are returned to the front-end applet page for exhibition.
Fusing Fine-Grained Information of Sequential News for Personalized News Recommendation

In this paper, we propose a novel method that fuses Fine-grained Information of Sequential News for personalized news recommendation (FISN). FISN comprises three primary modules: news encoder, clicked news optimizer and user encoder. The news encoder uses fine-grained information to learn accurate news representations. The clicked news optimizer introduces multi-headed self-attention and positional encoding techniques to optimize the clicked news representation. The user encoder uses news-level attention to learn user representations. Extensive experimental results demonstrate that FISN outperforms many baseline approaches in terms of metrics for real datasets.
The Influence of Job Search Activities and Evaluation of Shortlist of Possible Jobs on Application Intention

The Covid-19 pandemic has had a considerable impact on several world industrial sectors, the health industry is forced to work harder to suppress the spread of the Covid-19 virus.Not only the industrial sector that accepts, but also the economic sector accepts the limits of normal business activities.Several countries have activities both nationally and internationally or commonly known as Lockdown.Many countries have experienced an economic crisis until they experienced a recession, but there are also several countries that have survived the economic crisis due to the Covid-19 pandemic until they do not experience a recession.However, this pandemic has actually become an encouragement for job seekers, besides that there must be interest in adjustments, but also the consequences of the new system implemented by the company to continue working with limited activities due to the Covid-19 pandemic.The population used in this study are job seekers who are of working age and preparing to enter the world of work.The sample used was 100 respondents and analyzed using path analysis and the help of the third version of the SmartPLS application.The results showed that all variables had a positive and significant effect on the decisions proposed at the job vacancy provider company.
Service Quality and Risk Analysis-Based Maintenance Policy Selection for Rail-Transport

In recent years, maintenance activities have become more complex along with the trends of automation in various industries.It implies maintenance being a dependent factor to service quality, one of which is urban rail transportation that already adhered to the grade of automation.In addition to its complexity, maintenance is also a major contributor in the cost-of-service delivery, reaching 15% of the total operating costs for urban rail services in Indonesia.Maintenance itself was a risk-based business process considering potential failures of technical equipment that affect equipment availability or service revenue indirectly.Previous studies recognized that the increased expectation of service quality and risk of maintenance activities are still linear to maintenance costs.Furthermore.most companies only follow OEM (Original Equipment Manufacturer) rather than develop their own strategy.Several studies have been conducted to determine maintenance strategies but have not considered the quality and risk of failure comprehensively.Therefore, this study aims to design a model of determining maintenance policies through priority arrangement of maintenance activities based on the risk of failure and its impact on service quality.Data are obtained from service quality attributes survey and stakeholder interview of urban rail company.Using House of Quality (HoQ) the importance of each maintenance activity is identified and prioritized subsequently based on potential failure and detection capabilities using FMEA (Failure Mode Effect Analysis).The result shows priority indexing on each maintenance activity.Afterwards those priority can be utilized as the main criteria for determining maintenance and inventory policy.