Reviewerly/exHarmony huggingface dataset:
Availability and Effectiveness of Information Technology Infrastructure in RYK Secondary Schools

This educational study determines the availability and effectiveness of IT (Information Technology) infrastructure in RYK Secondary Schools. In this article, challenges faced by teachers and students in the usage of IT will be discussed, as well as the use of IT infrastructure in education and its accessibility and efficacy in teaching and learning are addressed. This study will be beneficial for the teachers and higher authorities in understanding the availability of IT infrastructure in Secondary Schools. All the students (girls and boys) from the Public Secondary Schools of RYK were considered as the population. The descriptive survey design and random sampling technique were employed to accomplish the goal. A 50-item self-structured questionnaire based on a 5-point Likert scale was utilized to collect the data from a sample of study i.e. 500 male and female students. The collected data were analyzed by using SPSS with descriptive statistics (mean, percentage, and frequency value). The findings are intended to support the hypothesis that teachers and students face significant challenges in RYK Secondary Schools due to the lack of facilities. Therefore, it is necessary to work for improvement so that the students can learn in a better way. To conclude, using technology effectively in the teaching-learning process, improving the quality and accessibility of technology, and motivating the learning environment plays a vital role in education. As a result, with the use of IT, slow learners might also take an active role, and students’ attitudes can be positively affected, and learning can be enhanced.
Innovation path of secretarial education in higher vocational schools based on internet environment and machine learning

In recent years, due to the serious deviation between supply and demand in the training process of secretarial talents, the enrollment of this major has gradually cooled down. Some vocational schools have even stopped offering secretarial specialties, while the demand for secretarial talents has not decreased. Under the influence of the wave of economic globalization, China's economy has developed in depth and achieved considerable transformation, small and medium-sized enterprises are gradually affected by this, thus enabling secretarial professionals to show their strength. In this environment, the demand for secretarial talents is becoming more and more urgent. Therefore, in the context of the Internet, this paper has established a teaching management system of based on machine learning technology, which is composed of interface layer file access interface, message interface, add task acquisition interface, debugging interface, etc. Its core business layer includes state server, routing server, push message server, instant messaging service of middleware layer, etc., and there are interactions between them. Later, design experiments to compare the core algorithms of the system, and find that the solution of Bayesian optimization is very close to the optimal parameter set, which can save a lot of time while ensuring speed and high accuracy. Based on this algorithm, the paper puts forward a new idea to realize the design of secretarial education system in vocational and technical education and improves the low efficiency of secretarial education. In this paper, machine learning technology is introduced into the field of secretarial teaching in vocational and technical education, which effectively promotes the development of secretarial education.
From Face-to-Face Learning to Virtual Learning: Students’ Perspective

This article reports the results found in an investigation which analyzed the perception that students have regarding the virtual learning process developed during the COVID-19 pandemic. The study involved 38 students from a Latin American institution whose age ranged from 13 to 15 years of age, who carried out their academic activities virtually for a school year, once the school period ended, a survey was applied to collect information on the experience in virtual classes, once the results have been analyzed, it is evident that the vast majority of students issued responses in favor of virtual teaching and that virtual classes served to develop their skills in managing technological resources, on the other hand a number of participants They did not indicate the learning experience as positive or negative. This may be related to the fact that a group of participants did not have good quality internet. In the same way, the results reflect that the students consider that not having adequate equipment and good quality internet quality are a limitation in the learning process virtual lift.
Classical Emulation of Bright Quantum States

Abstract The way to emulate classically a number of fundamental quantum mechanical experiments is shown, such as Hong‐Ou‐Mandel interference, phase measurements with NOON states and specifically structured “revivals” in Jaynes‐Cummings model using coherent states with large number of photons. It is also shown that certain classes of bright non‐classical states can be efficiently emulated by our technique independently of the average photon number of these states.
Applications

In the previous chapters, we discussed how to train high-quality topic models. How to utilize these models in real-life applications is also a challenging issue. This chapter discusses three paradigms of using topic models: semantic representation, semantic matching, and visualization.
Pre-processing of Training Data

Before training topic models, we must ensure the training data are appropriately formatted. Although existing literature rarely discusses pre-processing of training data, pre-processing plays a vital role in practice. Hence, this chapter details the tools and strategies required for pre-processing.
Blockchain Privacy Protection Based on Post Quantum Threshold Algorithm

With the rapid increase in demand for data trustworthiness and data security, distributed data storage technology represented by blockchain has received unprecedented attention. These technologies have been suggested for various uses because of their remarkable ability to offer decentralization, high autonomy, full process traceability, and tamper resistance. Blockchain enables the exchange of information and value in an untrusted environment. There has been a significant increase in attention to the confidentiality and privacy preservation of blockchain technology. Ensuring data privacy is a critical concern in cryptography, and one of the most important protocols used to achieve this is the secret-sharing method. By dividing the secret into shares and distributing them among multiple parties, no one can access the secret without the cooperation of the other parties. However, Attackers with quantum computers in the future can execute Grover’s and Shor’s algorithms on quantum computers that can break or reduce the currently widely used cryptosystems. Furthermore, centralized management of keys increases the risk of key leakage. This paper proposed a post-quantum threshold algorithm to reduce the risk of data privacy leakage in blockchain Systems. This algorithm uses distributed key management technology to reduce the risk of individual node private key leakage and provide post-quantum security. The proposed privacy-preserving cryptographic algorithm provides a post-quantum threshold architecture for managing data, which involves defining users and interaction processes within the system. This paper applies a linear secret-sharing solution to partition the private key of the Number Theory Research Unit (NTRU) algorithm into n parts. It constructs a t–n threshold that allows recovery of the plaintext only when more than t nodes participate in decryption. The characteristic of a threshold makes the scheme resistant to collusion attacks from members whose combined credibility is less than the threshold. This mitigates the risk of single-point private key leakage. During the threshold decryption process, the private key information of the nodes will not be leaked. In addition, the fact that the threshold algorithm is founded on the NTRU lattice enables it to withstand quantum attacks, thus enhancing its security. According to the analysis, the proposed scheme provides superior protection compared to currently available methods. This paper provides post-quantum security solutions for data security protection of blockchain, which will enrich the use of blockchain in scenarios with strict requirements for data privacy protection.
Secure Software Development Awareness: A Case Study of Undergraduate Developers

As ubiquitous computing becomes an increasingly inherent component of everyday life due to the rapid growth of communication technologies and globalization, threats against information systems have taken a more latent yet lethal dimension. This emergent digital security challenge has correspondingly motivated a proactive change in the software engineering process in recent decades. This change has inspired more intense research scrutiny on security as a crucial component of any software system. Moreover, in today’s virtual world of hyperconnectivity, the most significant vulnerabilities in modern information systems security are software centred. Nevertheless, research shows that software developers often lack the required knowledge and skills in secure software systems development (SSD). Such knowledge ensures that all the resultant software components of each development lifecycle are correctly implemented rather than merely following the SSD lifecycle. Also, the knowledge engenders software security consciousness as a professional attitude amongst developers. Therefore, investigating students’ awareness of SSD principles can generate insight into evolving the undergraduate software development curriculum – a path to building future career developers. The study used a voluntary online survey to recruit a sample of 76 undergraduate developers and employed a descriptive approach to data analysis. Among other findings, the study revealed that participants' perception of the threat of software vulnerability impacts their attitude towards security on online and mobile platforms. And that though over 90% of the undergraduate developers took software vulnerability threats either “serious” or “extremely serious”, this disposition did not reflect the depth of their knowledge and experience in SSD.
Varieties and Skills of Cybercrime

Several thousand organized groups, as well as gangs, are dedicated to cybercrime. The potential rewards for cybercrime can be immense, even for relatively simple crimes. The rapid advancement of technology means that cybercrime is constantly evolving, making it difficult to define and predict. While some may believe cybercrime to be the work of individual lone actors, the reality is quite different. Today, there are thousands of groups dedicated to cybercrime, attracted by its potential rewards. The pace of cybercrime globally is increasing rapidly, and resolving cybercrime is often more challenging than traditional crimes. Authorities worldwide receive thousands of complaints daily, and cybercriminals are becoming increasingly innovative, organized, and sophisticated. They work hard to uncover new vulnerabilities and avoid detection while consumers remain unaware of the risks. With the rapid expansion of ICTs, cybercriminals have unique opportunities to exploit, and the full extent of the dangers is still largely unknown.
PRAGCN: Personalized Recommendation Algorithm based on Graph Convolutional Network Technology

The recommendation system aims to provide users with personalized matching services, so as to effectively alleviate the problem of information overload in the era of big data, improve user experience, increase user stickiness, and greatly promote the development of e-commerce and other fields. However, in practical application scenarios, due to sparse data and cold start problems, it is often difficult for recommendation systems to obtain accurate recommendation results; The complex model design also leads to the unsatisfactory interpretability of the recommendation system. Therefore, how to make full use of interaction, attributes and various auxiliary information to improve the performance and interpretability of recommendation is the core problem of recommendation system. Most of the previous algorithms ignored the personality preferences in the comment data of users and products. To solve this problem, a method is proposed, which uses graph neural network to extract the association relationship, uses the advantage of graph deep learning to extract comments, extracts the preferences of users and goods, and carries out feature fusion to improve the recommendation effect. Comparative experiments were carried out in four groups of public data sets, and the effectiveness of the method was verified by ablation experiments. Experiments show that this method is better than the existing related algorithms, and can improve the recommendation effect.
Research on the Evaluation System of Personalized Teaching in the SPOC Environment

Learning evaluation is not only the supervision and feedback mechanism of teaching quality but also an indispensable part of teaching research. E-learning evaluation focuses on evaluating the learning process and learning resources, providing support services and quality assurance for E-learning, and serving teaching decision-making. The evaluation of personality factors includes the evaluation of both intelligence factors and non-intelligence factors. The former includes knowledge evaluation and intelligence evaluation, while the latter mainly includes value evaluation and willpower evaluation. Therefore, the evaluation of personality factors mainly includes a knowledge evaluation, intelligence evaluation, value evaluation, and willpower evaluation. Evaluating these factors aims to provide guidance, supervision, and feedback for teaching activities. Meanwhile, knowledge evaluation is the core content of traditional learning evaluation. Therefore, this paper proposes a personalized teaching evaluation system based on various evaluation elements in the SPOC environment, namely IVWA, aiming at the phenomenon of students' poor self-discipline in learning and rapid decline in learning activities in the SPOC practice process, to provide evaluation reference for online and offline teaching activities and improve teaching quality.
Analysis of Writing Task Components in Intermediate Korean Textbooks: Focusing on Textbooks by Function

The purpose of this study is to analyze the task components of writing textbooks developed for intermediate learners and to suggest problems and improvements. This will not only serve as a reference for future, but also help teachers or learners to select writing textbooks. Textbooks for analysis were selected from textbooks by function recently published by university-affiliated Korean language institutes. The components of the writing task were analyzed by dividing them into rhetorical and procedural components. The rhetorical components were subject, purpose, and audience, and the process components were planning, creating, organizing, expressing, and rethinking. This study is meaningful in that it analyzed the components of the writing task at the intermediate level, which were insufficient in the study, identified problems, and tried to suggest improvement plans based on this. can provide clues about it.
Forecasting Cryptocurrency Prices using Sequential and Time Series Models

The world saw a recent hype of terms, cryptocurrency, and digital assets. It was the subject of discussion. Almost everyone was fascinated by this not-so-new concept due to its numerous benefits. Those who dived deep into this area found the underlying technology that made it all possible, i.e., blockchain. This technology makes exchanges secure, recognizable, straightforward, and immutable. The most interesting aspect of blockchain is that it is decentralized, meaning it cannot be controlled by a single entity or organization which makes it more transparent. Due to these facilities, many organizations are looking for different ways to incorporate this technology into their domains. Cryptocurrency paved the way for a new form of payment system where people are no longer required to rely on third parties to ensure a secure financial transaction. Over the last decade, the field of cryptocurrency has grown at an exponential rate, with the most rapid progress coming in the last few years as a growing number of businesses worldwide have recognized the value of owning digital assets. Although having all the functionalities that work in favor of the people, the fundamental challenge to cryptographic money is its extreme volatility and implausibility. Cryptocurrency prices cannot be determined with the same degree of certainty that the stock market price can be. Therefore, this paper aims to develop a model which uses deep learning techniques that can be used to accurately predict the variations in the cryptocurrency price. Deep learning techniques such as Long Short-Term Memory, Gated Recurrent Unit, and the time series model Autoregressive Integrated Moving Average have been presented in this research. The efficacy of these models is evaluated by various losses such as Root Mean Square Error, Mean Squared Error, and Mean Absolute Error.
Pedagogical system of training future primary school teachers to use innovative technologies: methodological principles

In today's conditions of significant transformations in the social, political, economic and spiritual spheres of public life, the main task is to create a viable education system, its democratization and humanization. This, in turn, is aimed at providing every citizen with opportunities for continuous spiritual self-improvement, creative self-realization, and satisfaction of intellectual and cultural needs of the individual. Society's need for proactive, authoritative, comprehensively educated professionals capable of further development and self-study, spiritual and professional improvement actualizes the social order for the training of teachers of a new generation, capable of applying the latest achievements of pedagogical theory and practice in the process of creative self-realization, building a personal dialogue with students. The new concept of higher education envisages the development of students' ability to learn independently, consciously and responsibly, as well as the creation of the necessary conditions in educational institutions for the development of students' creative abilities, the education of a personality capable of self-development both during higher education and in further professional activities. This is why there is a need for a scientifically based design of training future youth for professional teaching, and why researchers are looking for effective ways to improve the training of specialists in higher education, to create conditions that stimulate students' professional growth. The late twentieth and early twenty-first centuries saw scientific and technological progress and the technologization of not only many industries, but also the cultural and humanitarian fields of knowledge. Today, the term "technology" is also used in the field of education. At the same time, the growing requirements for teacher training require qualitatively new theoretical and methodological approaches to training students that will allow them to master not only the basics of science and professional skills, but also new pedagogical achievements and innovative technologies in a higher education institution. Successful mastery of innovative pedagogical technologies by bachelors and masters will help them to organically engage in pedagogical activities and immediately begin practical application of scientific knowledge at school.
Lemmatizing and POS-tagging Akkadian with BabyLemmatizer and Dictionary-Based Post-Correction

We present BabyLemmatizer, a hybrid lemmatizer and POS-tagger for Akkadian, the language of the ancient Assyrians and Babylonians, documented from 2350 BCE to 100 CE. In our approach the text is first POS-tagged and lemmatized with TurkuNLP trained with human-verified labels, and then post-corrected with dictionary-based methods to improve the lemmatization quality. The post-correction also assigns labels with confidence scores to flag the most suspicious lemmatizations for manual validation. We demonstrate that the presented tool achieves a Lemma+POS labeling accuracy of 94%, and a lemmatization accuracy of 95% in a held-out test set. We also apply lemmatizer to a previously unlemmatized text corpus to test it in practice.
The Pipeline for Publishing Resources in the Language Bank of Finland

We present the process of publishing resources in Kielipankki, the Language Bank of Finland. Our pipeline includes all the steps that are needed to publish a resource: from finding and receiving the original data until making the data available via different platforms, e.g., the Korp concordance tool or the download service. Our goal is to standardize the publishing process by creating an ordered checklist of tasks with the corresponding documentation and by developing conversion scripts and processing tools that can be shared and applied on different resources.
Airline Collocation: Frequency Based Analysis with COCA as a Corpus

Collocations are words that co-occur together in any text and have definite association. The use of collocations is part of linguistic awareness which defines how language is used naturally. This study aims to analyze the collocations used in airlines by using Corpus of Contemporary American English (COCA). The data were analyzed both quantitatively and qualitatively. Quantitatively, the data were calculated regarding the frequency, topics and cluster. Qualitatively, the data were verbally analyzed, described, and discussed. The big data is taken from 1990-2019. The frequent words in area of airlines management found in COCA are used as keywords to find concordance of other words or phrases. The results of the study shows that from the three words taken from the script of flight announcement in airport ‘flight’, ‘passenger’, and ‘boarding’ it was found that the frequency risen is 73,870. The topics related to the three words of airlines found is 22,798. The clusters found from the three words by COCA is 34,006 which ‘flight’ word has the largest clusters (18,730). From the COCA analysis, the new words or idioms related to airlines can be used as teaching material in vocabulary for aviation or airlines students.
Transfer Learning Based Photovoltaic Power Forecasting with XGBoost

Photovoltaic power forecasting plays a significant role in the operation of power system with high renewables. Owing to the powerful data mining ability, the artificial intelligence learning based models have achieved impressive success in photovoltaic power forecasting. However, most of these methods cannot be directly employed in the cases where there is not sufficient historical data to train a reliable model. Fortunately, transfer learning can be utilized to address this problem by exploiting the knowledge learned from related areas with sufficient data. In this paper, a novel adaptive transfer learning framework based on extreme gradient boosting is proposed. In the proposed framework, several (one) extreme gradient boosting based models are firstly trained based on the data of source domains (target domain). Then, transferability analysis is conducted by comparing the similarity of SHAP value distribution between each of the obtained models in source domain and the one obtained in target domain. The model in the source domain with the high similarity is selected to transfer knowledge to target domain. Case studies conducted on open dataset demonstrates that the proposed framework can improve the forecasting accuracy of photovoltaic power with limited data.