Reviewerly/exHarmony huggingface dataset:
Yan Zhao Folk Art: Challenges and Future Perspectives

Yan-Zhao folk art is an indispensable part of Chinese traditional culture and plays an important role in inheriting Chinese culture. The translation work of it is also crucial. This article briefly introduces the Yan-Zhao culture, as well as the developing history of Yan-Zhao paper-cutting and tea art, and introduces the characteristics of these two folk arts. It also looks the translation situation of these two folk arts, mainly focusing on the plight and challenges they faced in translation. Through case analysis, some difficulties in translation work can be identified, such as the difficulty of translating unique cultural symbols, significant differences between Chinese and foreign cultures, and the difficulty in conveying the implicit content of folk culture and art. It aims to raise awareness among learners about these issues and propose methods to address these challenges. In order to improve the translation of this type of folk art, the article explores the opportunities that can be seized in the current translation of folk arts, aiming to combine these opportunities with ideas and suggestions for solving translation problems. Finally, the article concludes that when translating content with cultural features, translators can deepen their understanding of their own culture, select appropriate foreign language vocabulary for translation, and utilize tools such as the internet and artificial intelligence to better derive translation solutions, ensuring accurate conveyance of the original text's meaning and achieving effective cross-cultural communication.
PKM Mengurangi Limbah Kain Perca Melalui Pelatihan Tapestry dan Patchwork di UMKM Rumah Karya Disabilitas Pelangi Kasih Kabupaten Lumajang

SLB Kristen Pelangi Kasih has a garment business that produces various kinds of products such as bed sheets, bed covers, tote bags and souvenirs. However, they cannot process the patchwork waste they produce. The purpose of this community service (CS) is to provide assistance in managing patchwork waste into products that are ready for sale so as to provide additional income. Furthermore, the ABCD method is used by CS team to solve the problems being faced: discovery, dream, design, define, and destiny. The final stage is an evaluation of all the activities that have been carried out. The results showed that a joint agreement to solve the problems being faced was carried out at the planning stage by looking at the potential possessed by SLB Kristen Pelangi Kasih. The trainings designed, determined, and carried out together had a real impact on the students' work that was sold. In addition, the teachers and staff involved have also begun to reorganize their business management and have begun to explore the market place to market them. Finally, all parties hope that this impact will take place in a sustainable manner to achieve the goals that are expected together.
Data Reconstruction of Wireless Sensor Network Based on Graph Signal

The environmental and other factors can cause data missing in power systems; thus, data reconstruction is of great significance. In this paper, we model the observed signal as time-varying signal based on graph signal processing (GSP) and establish an optimization problem with the objective of minimizing the error between the true signal and the reconstructed signal at the sampling points and improving the smoothness of the reconstructed signal. To solve the optimization problem, Taylor series expansion is performed on the Hessian inverse matrix of the objective function, and truncated Taylor series is used as an approximation of the Hessian inverse matrix. In the simulation, the algorithm proposed in this paper is compared with the gradient descent algorithm, and the result shows that the proposed algorithm converges faster and the reconstructed signal is more accurate.
Using facial EMG to track emotion during language comprehension: past, present, and future

Beyond recognizing words, parsing sentences, building situation models, and other cognitive accomplishments, language comprehension always involves some degree of emotion too, with or without awareness. Language excites, bores, or otherwise moves us, and studying how it does so is crucial. This chapter examines the potential of facial electromyography (EMG) to study language-elicited emotion. After discussing the limitations of self-report measures, we examine various other tools to tap into emotion, and then zoom in on the electrophysiological recording of facial muscle activity. Surveying psycholinguistics, communication science, and other fields, we provide an exhaustive qualitative review of the relevant facial EMG research to date, exploring 55 affective comprehension experiments with single words, phrases, sentences, or larger pieces of discourse. We discuss the outcomes of this research, and evaluate the various practices, biases, and omissions in the field. We also present the fALC model, a new conceptual model that lays out the various potential sources of facial EMG activity during language comprehension. Our review suggests that facial EMG recording is a powerful tool for exploring the conscious as well as unconscious aspects of affective language comprehension. However, we also think it is time to take on a bit more complexity in this research field, by for example considering the possibility that multiple active generators can simultaneously contribute to an emotional facial expression, by studying how the communicator’s stance and social intention can give rise to emotion, and by studying facial expressions not just as indexes of inner states, but also as social tools that enrich everyday verbal interactions.
Spatial Versus Graphical Representation of Distributional Semantic Knowledge

Spatial distributional semantic models represent word meanings in a vector space. While able to model many basic semantic tasks, they are limited in many ways, such as their inability to represent multiple kinds of relations in a single semantic space and to directly leverage indirect relations between two lexical representations. To address these limitations, we propose a distributional graphical model that encodes lexical distributional data in a graphical structure and uses spreading activation for determining the plausibility of word sequences. We compare our model to existing spatial and graphical models by systematically varying parameters that contributing to dimensions of theoretical interest in semantic modeling. In order to be certain about what the models should be able to learn, we trained each model on an artificial corpus describing events in an artificial world simulation containing experimentally controlled verb–noun selectional preferences. The task used for model evaluation requires recovering observed selectional preferences and inferring semantically plausible but never observed verb–noun pairs. We show that the distributional graphical model performed better than all other models. Further, we argue that the relative success of this model comes from its improved ability to access the different orders of spatial representations with the spreading activation on the graph, enabling the model to infer the plausibility of noun–verb pairs unobserved in the training data. The model integrates classical ideas of representing semantic knowledge in a graph with spreading activation and more recent trends focused on the extraction of lexical distributional data from large natural language corpora.
The complexity of the grading system in Turkish higher education

Based on the academic performance grades of university students, various high-stakes decisions are made, including determinations of pass/fail status, the awarding of diplomas, and eligibility for placement in graduate education programs. According to the criteria used, the types of assessment are divided into two assessment, criterion-referenced assessments and norm-referenced assessments. When the grading system of state universities in Turkish higher education is examined, it has been observed that some universities use criterion-referenced assessment, some use norm-referenced assessment, and some use both assessment systems. The purpose of this research is to examine whether inter-university grading systems show significant concordance in the context of university students' letter grades or not. In other words, it is to reveal whether there are skew in the grading systems of public universities. In this context, 250 individuals were simulated in a way that their class/group achievement level would show a normal distribution. Among the public universities in the 2021-2022 Academic Performance Ranking of Universities (URAP), four state universities were determined in the first quarter, second quarter, third quarter, and last quarter. The letter grades of each student's academic success grade in the relevant universities were determined and it was examined whether there was a significant concordance between the letter grades of the students. In the study, it was concluded that in the context of university students' letter grades, inter-university grading systems generally do not show significant concordance. The findings are expected to contribute to the work of the Council of Higher Education and the University Education Commissions.
PENINGKATAN AKTIVITAS DAN HASIL BELAJAR MATEMATIKA MENGGUNAKAN MODEL PROBLEM POSING PADA SISWA KELAS V SD

This research started with the problem of teachers rarely asking questions, lack of activity in learning, students rarely asking opinions and students rarely asking questions in learning. This results in low student activity and learning outcomes. This research is classroom action research with a qualitative and quantitative approach. The research subjects were 35 class V students at SDN 105/II Rambah. In its implementation, this research consists of two cycles, each cycle consisting of planning activities, implementing actions accompanied by observation and reflection in each cycle. The results of this research can be seen from the students' visual, oral and mental activities. In cycle I the results of this research were focused on activities, namely (1) making experiments/questions (2) asking and answering questions (3) discussing (4) expressing opinions (5) solving problems, obtained an average in cycle I of 91%. Meanwhile, in cycle II, meeting I averaged 97.6% and meeting II 98%. This can also be seen in the completeness of student learning outcomes in cycle I, namely 20 students (57%) who completed, and 15 students (43%) who did not complete. Meanwhile, in cycle II student learning outcomes increased, namely 30 students (85%) who completed, and 5 students (15%) who did not complete. Based on the results of this research, it can be concluded that the Problem Posing model can improve student activity and learning outcomes in class V.
Experimental Demonstration of High‐Performance Physical Reservoir Computing with Nonlinear Interfered Spin Wave Multidetection

Reservoir Computing Wataru Namiki, Daiki Nishioka, Takashi Tsuchiya, Kazuya Terabe, and co-workers experimentally demonstrate high-performance reservoir computing on the basis of chaotic interference of spin waves in an yttrium iron garnet single crystal (see article number 2300228). The subject computing system achieves excellent performance when used for hand-written digit recognition, second-order nonlinear dynamical tasks, and nonlinear autoregressive moving average (NARMA).
AutomatedFace Mask Detection Using Pretrained CNN

In recent times, the use of face masks has emerged as a critical subject. Automated facial mask detection can curb the transmission of the COVID-19 virus and SARS-VIRUS within communal areas by identifying individuals who are not utilizing masks. In this work, a pretrained Convolutional Neural Network (CNN) with model ResNet-50, which is initially trained on the Image Net competition data, is utilized. This model is augmented with a 300-linear layer network and fine-tuned on a dataset that comprises 1,000 facial images. During the evaluation of the validation dataset consisting of approximately 800 face images, the model achieved an impressive 99% accuracy. Its primary objective is to discover if an individual is wearing a facial mask using a cropped image of their face. By leveraging such advanced technologies, we can contribute significantly to public health and safety measures in the ongoing battle against COVID-19 and SARS-VIRUS.Keywords:CNNCOVID-19Face Mask Detection (FMD) SARSDOI: 10.22401/ANJS.26.4.12*corresponding author email: farah.saad@nahrainuniv.edu.iqThis work is licensed under a Creative Commons Attribution 4.0 International License1.Introduction COVID-19 (CO ronaVIrus Disease of 2019) and SARS(Severe Acute Respiratory Syndrome) are two viral respiratory illnesses caused by coronaviruses, both of which have had important global impacts on public health [1]. These contagious diseases emerged in different time frames but share similarities in their mode of transmission and clinical presentation, prompting the search for effective non-pharmaceutical strategies to mitigate their spread [2]. One essential strategy to reduce the transmission of respiratory droplets, which can carry the viruses, is the widespread adoption of face masks in public spaces. However, monitoring and ensuring compliance with mask-wearing practices in crowded areas can be challenging, particularly in densely populated regions. Automatic face mask detection (FMD) systems powered by advanced technologies offer a potential solution to this challenge [3]. Convolutional Neural Networks (CNNs) and deep learning techniques have demonstrated remarkable success in computer vision tasks, including image classification [4]. In this context, utilizing pretrained CNN models, such as ResNet-50, has shown promising results for identifying faces wearing or not wearing masks. In this work, we explore applying a pretrained ResNet-50 CNN model fine-tuned on a well-balanced dataset of 12,000cropped photos of faces with and without masks. The goal is to create an accurate and reliable FMD (Face Mask Detection) system that can automatically identify individuals who are not wearing masks in public spaces. Through this work, we aim to contribute to the expanding realm of research on FMD as a crucial tool to prevent the spread of respiratory illnesses. The successful implementation of such technology has the ability to play a significant role in safeguarding public health, improving safety measures, and aiding in the ongoing battle against COVID-19 and SARS in various community settings.
SecNLP: An NLP classification model watermarking framework based on multi-task learning

The popularity of ChatGPT demonstrates the immense commercial value of natural language processing (NLP) technology. However, NLP models like ChatGPT are vulnerable to piracy and redistribution, which can harm the economic interests of model owners. Existing NLP model watermarking schemes struggle to balance robustness and covertness. Typically, robust watermarks require embedding more information, which compromises their covertness; conversely, covert watermarks are challenging to embed more information, which affects their robustness. This paper is proposed to use multi-task learning (MTL) to address the conflict between robustness and covertness. Specifically, a covert trigger set is established to implement remote verification of the watermark model, and a covert auxiliary network is designed to enhance the watermark model’s robustness. The proposed watermarking framework is evaluated on two benchmark datasets and three mainstream NLP models. Compared with existing schemes, the framework not only has excellent covertness and robustness but also has a lower false positive rate and can effectively resist fraudulent ownership claims by adversaries.
Assistance and application of skewer smoothing and sharpening machine technology to increase the productivity of skewer craftsmen

Pasangsari Village, situated in Windusari District, stands out for its severe poverty levels, necessitating swift intervention to alleviate these challenges by leveraging its potential. Brigasan Kidul Hamlet, located within Pasangsari Village, is primarily inhabited by farmers. Despite its economic reliance on agriculture, the hamlet, positioned at an altitude of 100 meters above sea level, is densely covered with bamboo trees. Notably, bamboo serves as a crucial resource for the production of skewers, contributing to the local economy. The skewer industry, owing to its zero-waste production process, stands as a key factor in enhancing both capacity and product quality. The objective of this community service initiative is to enhance the quality and quantity of skewer products through technological implementation, thereby increasing the income of craftsmen in Brigasan Hamlet. The methods employed in this community engagement include socialization, the introduction of smoothing and sharpening machine technology, reinforcing administrative and institutional aspects, as well as mentoring, monitoring, and evaluation. The program's outcomes encompass an improvement in the quality and quantity of skewer products, coupled with the implementation of simple bookkeeping practices that facilitate the monitoring process.
Assistance with cost management and digital promotion for the 'Fatih' photocopy business

Cost management is essential for the Fatih Photocopy business because daily data collection on raw materials and supplies is conducted. The primary goal of this community service is to impart knowledge about cost management and how to implement cost management formats and digital promotions for the Fatih Photocopy business. The program involves training and hands-on practice. The outcome of this program is an improved understanding of cost management formats among partners, enabling them to generate cost reports and enhance their digital promotion skills by maximizing the potential of social media.
Investigating the contrasts between ground-based and building-based CORS

The study aims to compare ground-based and building-based CORS in terms of stability, signal reception, environmental influences, maintenance, and data quality. The objective is to provide insights into the advantages and limitations of each type, aiding in better decision-making for CORS installations.
Analisis Waktu Peluruhan Dini dan Waktu Dengung pada Ruang Teater

In the stage of acoustic measurement of the room, a review of the research object, namely the Salihara Black Box Theater, is required. The review is conducted to obtain the necessary materials for the planning of the data collection format, such as the condition of the room when empty, examining the materials composing the surface of the room walls, as well as re-measuring the dimensions of the room and its equipment. The measurement of the room dimensions is also necessary for the empirical calculation of the Sabine reverberation time and for reproducing a model of the research object room. Moreover, it is used to consider the determination of the measurement points later on. All parameters are measured within the frequency range of 125 Hz to 4,000 Hz and taken from the perspective of comparing each receiving point. For the reverberation time (T30), values ranging from 0.6 seconds to 0.99 seconds are obtained. For the early decay time (EDT), values ranging from 0.45 seconds to 0.94 seconds are obtained. For clarity (C80), values ranging from 1.66 dB to 11.24 dB are obtained. For definition (D50), values ranging from 38.09% to 84.1% are obtained.
Rhythmic Rhetoric: Analyzing Linguistic Subversion in Nigerian Hip-hop’s Portrayal of Internet Scam

Nigerian hip-hop artists utilize linguistic techniques to navigate cultural and societal boundaries. While past research on Nigerian hip-hop has delved into its use of English variations, portrayal of women, and the framing of identities and beliefs, there has been limited exploration of the language’s role in depicting internet fraud. This study adopts an applied linguistic lens and an interdisciplinary methodology to analyze the portrayal of internet fraud in tracks by six renowned Nigerian hip-hop artists: Okafor Golden Chinedu (X Busta), Mikel Mint (Jupitar), Patrick Nnaemeka Okorie (Patoranking), Babatunde Olusegun Adewale (Modenine), Abolore Adegbola Akande (9ice), and Olumide Edwards Adegbulu (Olu Maintain). Drawing from the principles of Moral Disengagement and Lexical Semantics, the research elucidates how these artists linguistically navigate societal norms when addressing theme of internet fraud.
The Influence of Academic Performance and Service Quality on Loyalty through Student Satisfaction at the Institut Ciencia Saude Timor-Leste

The millennial era has made tough competition in the world of education even more competitive. Many fields of education are feeling the competition today. Therefor there are many promising opportunities for an educational institution to meet the needs of the educational institution itself and for the students themselves to understand all the sources of existing needs. Therefor, this research was conducted to determine the effect of academic performance and service quality on loyalty through student satisfaction. The object of this research are students who are undergoing or active as students who are educated at the Institut Cienca Saude Timor-Leste. The objects of this research are students at the Institut Ciencia Saude Timor-Leste. Data was collected using a questionnaire that was filled out by respondents in semester two to semester five. The results of data processing show that academic performance has a direct effect on loyalty. Academic performance affects student satisfaction directly. Service Quality affects student satisfaction directly. Student satisfaction directly affects Loyalty. Meanwhile, academic performance affects loyalty through student satisfaction. In addition, Service Quality affects Loyalty through student satisfaction. It can also be concluded that academic performance influences loyalty. The suggestions put forward in this study are to increase student loyalty, it must increase academic performance and service quality in the education curriculum.
Exploration of the Application of Artificial Intelligence in the Intelligent Evaluation System of Information and Innovation Education

With the rapid development of artificial intelligence technology, its application in the field of education is becoming increasingly widespread. The Xinchuang Education Intelligent Evaluation System, as an important tool for educational informatization, integrates artificial intelligence technology into it, which helps to improve the quality of education, achieve personalized education and intelligent evaluation. This article provides an overview of the development history, main technologies, and application advantages of artificial intelligence technology, and explores the architecture design, key technology selection, and module division of the intelligent evaluation system for information and innovation education. At the same time, the application of artificial intelligence in the intelligent evaluation system of information and innovation education was analyzed, such as automatic scoring and evaluation, learning content recommendation and personalized learning, intelligent assisted teaching and learning, and student learning behavior analysis. Finally, corresponding solutions were proposed to address the challenges faced by artificial intelligence in the intelligent evaluation system of information and innovation education, such as data privacy and security issues, fairness and bias issues, human-computer interaction and user experience issues.