Reviewerly/exHarmony huggingface dataset:
Web Application as a Link Organizer for Educators, Staffs, and Students

Current digital transformation encourages the improvement of online learning process development through the internet networks with social media platforms, learning applications, and websites. The evolution of creativities and innovations in various fields support the creation of various new learning applications that must be known and used by educators, staffs, and students in a university. In addition, online webinars and online learnings have also started to become a new routine or daily activity in the world of education. Every learning application, online webinar, online learning, and other online media generally has a special link where each user must at least remember, record, save, or search for the link through search engines or copy from email or social media chat so that at any time they can access it. The problem is that sometimes due to busy work and age, we often forget to remember these links. Sometimes we remember and record it, but need more time to type it in its long sentence or URL. There are even special links that cannot be searched using the search engines Google, Yahoo, and so on. This will certainly be an obstacle and quite a hassle for educators and education staff, especially if the links that must be accessed are very large, tens or even hundreds of them. This research provides a solution to the above problems by designing a web application that functions as a link organizer that makes it easier for educators, staffs, and students to store learning application links and other important links so that when needed, they just open the web and click the link. which has been saved. This study method such as observation, system design, interface design, application design, and testing of a web application as a link organizer. The results provide positive future works that can be further developed and refined.
Critical Literacy in an EFL Setting: Lecturers’ Perception

This study intended to determine lecturers' understanding of critical literacy, how they applied it in reading class, and their reasons for continuing to teach critical literacy to students. This study involved five English lecturers as the subjects of the study. The researchers used various techniques to obtain data, namely interviews, documentation, and observation. The collected data were analyzed qualitatively using the interactive data analysis model. This study found that the lecturers' have different ways of defining critical literacy. However, their understanding of critical literacy aligns with the concept of critical literacy in general. Furthermore, lecturers' understanding influences their strategies to promote critical literacy in their English as a foreign language class. Thus, they applied various teaching strategies to ensure that the students could improve their critical literacy. In addition, they also have strong reasons for promoting critical literacy sustainably. Detailed findings are discussed in this article.
A Study on the Purpose of Patent Application by Companies : Focus on Chinese Companies

본 연구는 기업의 특허출원 목적을 파악하는데 있다. 이에 선행연구 고찰을 통해 특허출원 목적을 파악하고, 전문가 델파이를 통해 10개 요인을 선정하고 이를 중국기업의 특허담당 실무자들을 대상으로 설문조사를 실시하였다. 분석결과, Improve the technological image of your company, For use in negotiation: cross licensing, joint venture, Improve the situation in R&D cooperations, Enhance Reputation, Measure performance, Aacquire venture capital, Prevent patent infringement suits, Licensing Revenue, Blocking or prevent competitors' patenting and application activities, Prevent copying or protect own technology from imitation 순으로 중요한 것으로 나타났다. 본 연구결과는 기업에서 특허를 출원할 때 우선적으로 고려해햐 할 요인이 무엇인지 파악하고 적용하는데 있어 실무적 가이 드라인으로 활용가능할 것으로 기대한다.
Readability Assessment of Advanced English Textbooks: A Corpus-Linguistic Study

As a quantitative index of the ease of reading text, readability has been used as an effective manner to assess the difficulty of reading textbooks. Based on a corpus consisting of 40 texts from two sets of advanced English textbooks published by two top-tier publishing houses in China in the same year, this paper employs three readability formulas (i.e. FRE, FKGL and LR) to examine their readability trends and differences in readability. The results show that: 1) the readability of both book sets is low, 2) no significant differences are found in three readability indices, 3) some of LR subindices, such as deep cohesion and connectivity, show an opposite tendency to the overall readability. It is claimed that: 1) both book sets are fairly difficult to read; 2) they are interchangeable in the teaching process without the absolute difference in authoritativeness; 3) the increasing trend of overall readability is moderated by controlling some subindices deliberately to keep the balance between text-reading difficulty and practical demands. This study not only benefits scholars but also teachers to evaluate and improve English textbooks.
The Problem of Artificial Intelligence in Contemporary Philosophy

The importance of the topic of the article is due to the fact that nowadays there is a need to understand that the creation of artificial intelligence is associated with the understanding and study of natural intelligence. The problem of defining artificial intelligence is largely reduced to defining intelligence in general: is intelligence monolithic or does this concept define a set of different abilities? To what extent intelligence can be created? Is it possible to create computers with intelligence? These and other questions have not yet been answered by the contemporary science, but these questions have greatly contributed to the formation of tasks and methodologies that form the basis of the theory and practice of contemporary artificial intelligence in philosophy. The purpose of the article is the need to study and substantiate the indicators of overall success in the development of artificial intelligence systems; proving that human psychoreality is an open system of nonlinear type; proving the effectiveness of logo and psycho-machines in the development of artificial intelligence in philosophy. The article gives a theoretical justification for the concepts of “artificial intelligence” and “logo and psycho machines”; the conditions for the effectiveness of the development of artificial intelligence in our time are highlighted, as the scientific direction of artificial intelligence is young, and its structure and range of issues are not so clearly defined. It is now possible to implement formal systems of reasoning in the machine and test their sufficiency for the manifestation of reasonableness in practice.
Analysis of Mandarin Syntactic Errors of Indonesian Learners at Elementary Level

This research discusses Mandarin syntactic errors of Indonesian learners, performed at the elementary stage. It aims to categorize errors and identify them. The researchers follow Corder’s steps in error analysis. This paper considers four categories of errors: omission, addition, replacement, and disorder. Finally, the researchers seek to explain why the students commit such errors. The results of the data analysis show that most of the syntactic errors are classified as omission errors and disorder errors. These results indicate that the participants relied heavily on their native language to use the target language. Furthermore, their errors reveal that lack of Chinese grammatical knowledge and L1 interference may be significant reasons for their errors. To reduce syntactic errors for students at the elementary stage, teachers should pay more attention to comparing Mandarin and their native language and provide comprehensible input.
Development of Employee Attendance Management Information System During the Covid-19 Pandemic Based on Website using QR Code and PHP Native

Hero Web Design is a small-scale company that is growing, the company is engaged in technology. Attendance activities at Hero Web Design still use the conventional method, every day employees carry out attendance activities manually. This results in fraud, loss, and damage to the processed data because they are not integrated. As a result, with the emergence of these problems the company could not develop. With the problems that Hero Web Design has, here an online attendance information system is made with a QR-Code and face detection using the Scrum method. In this study using the Scrum method using 3 Sprints, each Sprint has a process of not more than one month. The data sources used in this study are observational data in the form of attendance management activities and report management. The second data source is interview data in the form of an answer from a question-and-answer discussion with Hero Web Design. The third data source is literature study data in the form of supporting journals that have similar research. A website-based attendance management information system with a program working process using the PHP programming language, javascript, CSS, and bootstrap framework. The results of the information system that has been created will be tested using the black box method. This method aims to check for missing or incorrect functions, interfaces, performance, program initialization and output errors, data structures, or database access errors. Furthermore, from making this system, it is hoped that it can be used as a remote or online attendance without having to come face to face and overcome the problems found in Hero Web Design.
Error Control Begins to Shape Quantum Architectures

The overhead of error correction presents a serious challenge to scaling up quantum computing and may produce unexpected winners.
Unawareness of Common Errors in English and Its Impact on Human vs. Machine Translation Into Arabic

This study investigates the rate of unawareness of common errors in English and its impact on human vs. machine translation into Arabic. It analyzes the reaction of human and machine translators when encountering English sentences with common errors, and its impact on translation process. Translators are readers in the first place, they have to comprehend what they read in the SL in order to convey it in the TL. In order for the ST message to be correctly conveyed into the TL, detecting errors and correcting them before translating is a necessity. The study is conducted through presenting 40 SL texts and evaluating their 680 TL texts translated by 14 MA students of translation and 3 online machine translators. Results show that the rate of unawareness of common errors is 51% for human translators and 79% for machine translators.
Error Analysis of Form Four KSSM Arabic Language Text Book in Malaysia

Text books are essential in foreign language learning. Students depend mainly on text books as the source of information, enrichment activities, and reinforcement exercises in learning. Hence, preparation of text books of quality is important to ensure students receive the correct language information and are motivated in learning. Teacher teaching activities will also be facilitated with help from quality text books. This study aims at investigating errors in Arabic language text books for the fourth form used in the KSSM curriculum in Malaysia and recommending the appropriate corrections required. In conducting analysis, the error analysis approach recommended by al-Rājiḥī (1995) and Ṭu‘aimah (2004) were used. This combined approach introduced by these two scholars recommends four steps in language error analysis, namely, identifying, describing, interpreting, and implementing correction and treatment of errors. Findings of the study show that 70 language errors were detected in the unit studied. The errors were categorised as: 32 (46%) grammar errors, 16 (23%) spelling errors, 14 (20%) morphology errors, and eight (11%) lexical errors. This high number of errors demands that educators be more aware of language aspects in preparing text books, especially text books aimed at language learning.
COMPONENTS OF MODERN TEACHER`S READINESS TO PERSON ORIENTED TRAINING AND THE WAYS OF THEIR FORMING

The article is devoted to the issue of formulating the necessary conditions for the formation of the readiness of future teachers to implement the main provisions of the person oriented paradigm in teaching practice. The main professionally significant qualities and pedagogical values of a person oriented teacher are highlighted. The author of the article found out that the key role in the organization of person oriented training of future teachers is played by the possibility of choosing their own learning way. This contributes to the individualization of student learning according to their needs and capabilities. In the context of scientific research the mechanism of organizing professional teacher training is characterized and the necessity to change its main emphasis is emphasized. A factor-criterion model for assessing the teacher`s readiness for person oriented learning and a qualitative characterization of the levels of readiness of future teachers for person oriented teaching are proposed. Five levels are defined: pre-professional, initial, basic, constructive and systemic. The author stated that the teacher, who implements personal oriented education, should gain such experience himself, feel the need to rethink the values of education in a new way.
Design of Someone's Character Identification Based on Handwriting Patterns Using Support Vector Machine

Image processing has a fairly broad scope and is rich in innovation. Today, image processing has developed with various reliable methods in almost all aspects of life. One of the uses of technology in the field of image processing is biometric identification. Biometric is a system that utilizes specific data in the form of individual physical characters in the process of identifying and validating data. There is also a biometric attribute that will be developed in this study is handwriting. The handwriting pattern of each individual has a different character and uniqueness so that it can be used as an identity. The uniqueness of this handwriting will be studied with the aim of recognizing a person's character or personality. If someone's personality data has been obtained, this can help the process of recruiting prospective employees in a company by simply reading from handwriting patterns. Handwriting can be studied by combining the science of Psychology so that it can provide output in the form of a person's characteristics or personality. This research will be developed using the multi class Support Vector Machine (SVM) classification. The preprocessing stage in the form of binarization, thinning and data extraction will also greatly affect the reliability of the system. Simulations with variations of variables and parameters are expected to obtain optimal accuracy.
Preventing Cyber-Fraud in Nigeria’s Banking System Using Fraudaeck-AI

Preventing Cyber-Fraud in Nigeria's Banking System Using Fraudaeck-AI (Fraud analysis environment for cyber-fraud check) is an in-depth analysis of artificial intelligence (AI) assisted identity verification and authentication systems to prevent cyber fraud in Nigeria's banking system. The Fraudaeck (fraud analysis environment for cyber-fraud check) is a machine learning model developed to learn the interconnected subsystems of the communication network and the banking applications and how they function seamlessly to provide customers with ease and comfort of banking even on the go. An investigation revealed how this created a vulnerability in the system, allowing malicious software attacks such as the Xenomorph Trojan to gain access to the system, paving the way for cyber fraudsters—"Yahoo-boys"—to gain access. This paper proposes a solution to cyber-fraud during electronic banking transactions by using an artificial neural network model called Fraudaeck, which can be interfaced with both the telecommunication protocols and the banking application to detect network intrusion, customer identity theft, and prevent cyber-fraud to obtain hitch-free banking activities in Nigeria.
Performance Comparison of Deep Learning Algorithm for Speech Emotion Recognition

One of the problems in Speech emotion recognition is related to time series data, while the feedforward process in neural networks is unidirectional where the results from one layer are directly channeled to the next layer. This kind of feedforward process cannot store past data. Thus, if Deep Neural Network (DNN) is used for Speech emotion recognition, some problems arise, such as the speech rate of the speaker. DNN cannot analyze the existing acoustic patterns and so cannot map different levels of speech rate. Another method that can take input at once while retaining relevant data in the previous process is the Recurrent Neural Network (RNN). This paper presents the characteristics of the RNN method consisting of LSTM and GRU techniques for Speech emotion recognition using the Berlin EMODB dataset. The dataset is divided into 80% for training and 20% for testing. The feature extraction methods used are Zero crossing Rate (ZCR), Mel Frequency Cepstral Coefficients (MFCC), Root Mean Square Energy (RMSE), Mel Spectrogram, and Chroma. This study compares the CNN, LSTM, and GRU algorithms. The classification results show that the CNN algorithm gets better results, namely 79.13%. Meanwhile, LSTM and GRU only got an accuracy of 55.76% and 55.14%, respectively
Memory Augmented Lookup Dictionary based Language Modeling for Automatic Speech Recognition

Recent studies have shown that using an external Language Model (LM) benefits the end-to-end Automatic Speech Recognition (ASR). However, predicting tokens that appear less frequently in the training set is still quite challenging. The long-tail prediction problems have been widely studied in many applications, but only been addressed by a few studies for ASR and LMs. In this paper, we propose a new memory augmented lookup dictionary based Transformer architecture for LM. The newly introduced lookup dictionary incorporates rich contextual information in training set, which is vital to correctly predict long-tail tokens. With intensive experiments on Chinese and English data sets, our proposed method is proved to outperform the baseline Transformer LM by a great margin on both word/character error rate and tail tokens error rate. This is achieved without impact on the decoding efficiency. Overall, we demonstrate the effectiveness of our proposed method in boosting the ASR decoding performance, especially for long-tail tokens.
Giant excitonic magneto-optical Faraday rotation in single semimagnetic CdTe/Cd_{1-x}Mn_{x}Te quantum ring

Magnetic tuning of the bound exciton states and corresponding giant Zeeman splitting (GZS) between {\sigma}^{+} and {\sigma}^{-} excitonic transitions in CdTe/Cd_{1-x}Mn_{x}Te quantum ring has been investigated in the Faraday configuration for various concentrations of Mn^{2+} ions, using the variational technique in the effective mass approximation. The sp-d exchange interaction between the localized magnetic impurity ions and the delocalized charge carriers has been accounted via mean-field theory with the inclusion of a modified Brillouin function. The enhancement of the GZS, and in turn, the effective g-factor with the application of an external magnetic field, is strikingly manifested in type-I - type-II transition in the band structure, which has been well explained by computing the overlap integral between the electron and hole, and the in-plane exciton radius. This highlights the extraordinary magneto-optical properties, including the giant Faraday rotation and associated Verdet constant, which have been calculated using single oscillator model. The oscillator strength and exciton lifetime have been estimated, and are found to be larger than in the bulk diluted magnetic semiconductors (DMS) and quantum wells, reflecting stronger confinement inside the quantum ring. The results show that the DMS-based quantum ring exhibits more extensive Zeeman splitting, which gives rise to ultra-high Verdet constant of 2.6 \times 10^{9}rad/Tesla/m, which are a few orders of magnitude larger than in the existing quantum systems and magneto-optical materials.
A Study on a User-Controlled Radial Tour for Variable Importance in High-Dimensional Data

Principal component analysis is a long-standing go-to method for exploring multivariate data. The principal components are linear combinations of the original variables, ordered by descending variance. The first few components typically provide a good visual summary of the data. Tours also make linear projections of the original variables but offer many different views, like examining the data from different directions. The grand tour shows a smooth sequence of projections as an animation following interpolations between random target bases. The manual radial tour rotates the selected variable's contribution into and out of a projection. This allows the importance of the variable to structure in the projection to be assessed. This work describes a mixed-design user study evaluating the radial tour's efficacy compared with principal component analysis and the grand tour. A supervised classification task is assigned to participants who evaluate variable attribution of the separation between two classes. Their accuracy in assigning the variable importance is measured across various factors. Data were collected from 108 crowdsourced participants, who performed two trials with each visual for 648 trials in total. Mixed model regression finds strong evidence that the radial tour results in a large increase in accuracy over the alternatives. Participants also reported a preference for the radial tour in comparison to the other two methods.
Logic Mill -- A Knowledge Navigation System

Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.
Approaching Peak Ground Truth

Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the biomedical domain, annotations are subjective and suffer from low inter- and intra-rater reliability. Since annotations only reflect one interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of PGT is introduced. PGT marks the point beyond which an increase in similarity with the \emph{reference annotation} stops translating to better RWMP. Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, four categories of PGT-aware strategies to evaluate and improve model performance are reviewed.