Reviewerly/exHarmony huggingface dataset:
Fv-AD: F-AnoGAN Based Anomaly Detection in Chromate Process for Smart Manufacturing

Anomaly detection for quality prediction has recently become important, as data collection has increased in various fields, such as smart factories and healthcare systems. Various attempts have been made in the existing manufacturing process to improve discrimination accuracy due to data imbalance in the anomaly detection model. Predicting the quality of a chromate process has a significant influence on the completeness of the process, and anomaly detection is important. Furthermore, obtaining image data, such as monitoring during the manufacturing process, is difficult, and prediction is challenging owing to data imbalance. Accordingly, the model employs an unsupervised learning-based Generative Adversarial Networks (GAN) model, performs learning with only normal data images, and augments the Fast Unsupervised Anomaly Detection with GAN (F-AnoGAN) base with a visualization component to provide a more intuitive judgment of defects with chromate process data. In addition, anomaly scores are calculated based on mapping in the latent space, and new data are applied to confirm anomaly detection and the corresponding location values. As a result, this paper presents a GAN architecture to detect anomalies through chromate facility data in a smart manufacturing environment. It proved meaningful performance and added visualization parts to provide explainable interpretation. Data experiments on the chromate process show that the loss value, anomaly score, and anomaly position are accurately distinguished from abnormal images.
The Impact of a Pre-Questioning Technique on Students' Reading Comprehension at a Bengkulu State Junior High School

The purpose of this study was to determine the achievement of students' reading ability scores after using the pre-question and answer technique. The method used in this research is a quasi-experimental research method. The population of this research is the eighth-grade students of a Junior High School in Bengkulu. The sample consisted of 20 students in class VIII A as the experimental class and 20 students in class VIII C as the control class. The experimental class was taught using pre-questioning techniques and the control class was taught using discovery learning. The instruments used in this study were pre-test and post-test on descriptive text. The results of this study showed differences in learning outcomes of 82.75 for the experimental class and 80.00 for the control class. The data were analyzed using the t-test formula. sig value. (2-tailed) is 0.000 0.05. It can be concluded that the Null Hypothesis (H0) is rejected and the Alternative Hypothesis (Ha) is accepted. The results of this study have shown that the use of pre-questioning techniques can have a positive influence on improving students' reading comprehension in the eighth grade of SMPN 06 Seluma in the 2021/2022 academic year.
Speaking Skills Japanese Language Learning for the Intermediate Students Through Media Website <i>Bandung no Kankouchi</i>

This study aims to examine the implementation of learning Japanese language verbal skills through Bandung no Kankouchi website as a media of learning for intermediate students in Department of Japanese Language Education.Bandung no Kankouchi is a website-based media that contains information about tourism and culture in the city of Bandung presented in Japanese language.The website is designed and developed by the lecturers and students of the Department of Japanese Language Education Department at one university in Bandung.This media can be used as a guide for Japanese people who will be travelling to the city of Bandung and can also be used as a medium for learning for Japanese language learners.This study used a class action research method which was carried out four times in the course Chukyu Kaiwa-2.The participants of this study were 66 level-2 students.Students are free to choose a theme from the website to present in the form of a video.Based on the analysis, the study found that the students' ability to speak Japanese had increased, and their presentation themes varied.The students' ability to write narratives in Japanese had improved significantly.The acquisition of new vocabulary and grammar used follows the rules of correct Japanese grammar.Most students found this website very interesting and effective as a media for teaching and learning Japanese.
Japanese Language Learning Through Folklore Themed Instagram

Due to the pandemic, learning and teaching activities are done online, including Japanese language education.The existence of advanced technology is needed for the sake of effective learning and teaching activities.Currently, social media also used as learning medium, and one of them is Instagram.There are many Instagram accounts which can be used for Japanese language learning medium.The purpose of this research is to analyze Japanese language learners' response of Instagram as language learning medium.The data in this study was obtained by distributing questionnaires to twenty high school students who learn Japanese or people who want to learn Japanese from the basic level.The questionnaire results showed that the respondents prefer interesting Japanese language learning medium such as Instagram which has many pictures, colorful designs, and the consistency of material uploaded.According to the respondents, Instagram is effective as a learning medium.
Application of tropical semiring for matrix factorization

Matrix factorization methods employ standard linear algebra, i.e. linear models, for recommender systems. With the introduction of the tropical semiring, we can achieve non-linearity. We review algorithms that use the tropical semiring for matrix factorization and provide their strengths and limitations. We show that the tropical matrix factorization yields better results than non-negative matrix factorization for the synthetic data created by the underlying process of the tropical semiring.
PROJECT-BASED LEARNING BERBANTUAN E-LEARNING: PENGARUHNYA TERHADAP HASIL BELAJAR

The purpose of this research is to find out the effect of PBL assisted by e-learning on the learning outcomes of understanding concepts, the effect of self-efficacy on the learning outcomes of conceptual understanding, and the effect of PBL assisted by e-learning and self-efficacy together (simultaneously) on the learning outcomes of conceptual understanding. students in following the current lecture process increasingly rely on information technology where one of them is in obtaining lecturing material to achieve high concept understanding learning outcomes. So the support of learning models using ICT e-learning assistance as well as the readiness of students, seft-efficacy owned by students in the following lectures is the basis of this research.&#x0D; This research uses a quantitative approach, a sample of 38 respondents from IAIN FTIK students in Samarinda, the data collected were analyzed with multiple regression. The results of the study there is the effect of PBL assisted by e-learning on the learning outcomes of understanding concepts, there is the effect of self-efficacy on the learning outcomes of concept understanding proven to be significant, and the influence between PBL assisted by e-learning and self-efficacy together (simultaneously) on the learning outcomes of understanding concept. Influence is supported by the strength of the learning model, the power of e-learning (google classroom), class interaction and group collaboration, material and media as well as the environment which influence and significantly influence the learning outcomes of concept understanding, because both the PBL and Self-Efficacy variables have advantages to be applied in e-learning assisted learning, according to the current era of ICT development.
A Configurable Monitoring, Testing, and Diagnosis System for Electric Power Plants

The specific equipment, installation and machinery infrastructure of an electric power system have always required specially designed data acquisition systems and devices to ensure their safe operation and monitoring. Besides maintenance, periodical upgrade must be ensured for these systems, to meet the current practical requirements. Monitoring, testing, and diagnosis altogether represent key activities in the development process of electric power elements. This work presents the detailed structure and implementation of a complex, configurable system which can assure efficient monitoring, testing, and diagnosis for various electric power infrastructures, with proven efficiency through a comprehensive set of experimental results obtained in real running conditions. The developed hardware and software implementation is a robust structure, optimized for acquiring a large variety of electrical signals, also providing easy and fast connection within the monitored environment. Its high level of configurability and very good price-performance ratio makes it an original and handy solution for electric power infrastructures.
Влияние на обучението от разстояние в електронна среда върху някои аспекти на стиловете на учене при учениците от горна училищна възраст

The article presents the results of a limited sample study that is aiming at establishing the impact of distance learning in an electronic environment upon the learning styles of high school students. The author's questionnaire was developed by sharing the idea of setting the parameters in identifying the learning style according to the model of R. Felder and B. Soloman. The presentation of the collected data focuses on one of the possible dimensions in the presentation of the preferences of the learners – or the information processing. It is established that the conditions created almost equally, favour the satisfaction of the preferences of both active and reflective types of learners’ preferred learning styles are satisfied when implementing distance learning in an electronic environment.
Breast Dense Tissue Segmentation with Noisy Labels: A Hybrid Threshold-Based and Mask-Based Approach

Breast density assessed from digital mammograms is a known biomarker related to a higher risk of developing breast cancer. Supervised learning algorithms have been implemented to determine this. However, the performance of these algorithms depends on the quality of the ground-truth information, which expert readers usually provide. These expert labels are noisy approximations to the ground truth, as there is both intra- and inter-observer variability among them. Thus, it is crucial to provide a reliable method to measure breast density from mammograms. This paper presents a fully automated method based on deep learning to estimate breast density, including breast detection, pectoral muscle exclusion, and dense tissue segmentation. We propose a novel confusion matrix (CM)—YNet model for the segmentation step. This architecture includes networks to model each radiologist’s noisy label and gives the estimated ground-truth segmentation as well as two parameters that allow interaction with a threshold-based labeling tool. A multi-center study involving 1785 women whose “for presentation” mammograms were obtained from 11 different medical facilities was performed. A total of 2496 mammograms were used as the training corpus, and 844 formed the testing corpus. Additionally, we included a totally independent dataset from a different center, composed of 381 women with one image per patient. Each mammogram was labeled independently by two expert radiologists using a threshold-based tool. The implemented CM-Ynet model achieved the highest DICE score averaged over both test datasets (0.82±0.14) when compared to the closest dense-tissue segmentation assessment from both radiologists. The level of concordance between the two radiologists showed a DICE score of 0.76±0.17. An automatic breast density estimator based on deep learning exhibited higher performance when compared with two experienced radiologists. This suggests that modeling each radiologist’s label allows for better estimation of the unknown ground-truth segmentation. The advantage of the proposed model is that it also provides the threshold parameters that enable user interaction with a threshold-based tool.
PLCOjs, a FAIR GWAS web SDK for the NCI Prostate, Lung, Colorectal and Ovarian Cancer Genetic Atlas project

Abstract Motivation The Division of Cancer Epidemiology and Genetics (DCEG) and the Division of Cancer Prevention (DCP) at the National Cancer Institute (NCI) have recently generated genome-wide association study (GWAS) data for multiple traits in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Genomic Atlas project. The GWAS included 110 000 participants. The dissemination of the genetic association data through a data portal called GWAS Explorer, in a manner that addresses the modern expectations of FAIR reusability by data scientists and engineers, is the main motivation for the development of the open-source JavaScript software development kit (SDK) reported here. Results The PLCO GWAS Explorer resource relies on a public stateless HTTP application programming interface (API) deployed as the sole backend service for both the landing page’s web application and third-party analytical workflows. The core PLCOjs SDK is mapped to each of the API methods, and also to each of the reference graphic visualizations in the GWAS Explorer. A few additional visualization methods extend it. As is the norm with web SDKs, no download or installation is needed and modularization supports targeted code injection for web applications, reactive notebooks (Observable) and node-based web services. Availability and implementation code at https://github.com/episphere/plco; project page at https://episphere.github.io/plco
New COPIM Scoping Report Published on Archiving and Preserving Open Access Monographs

Work Package 7 of the COPIM Project has released their Scoping Report, identifying and examining the key challenges associated with archiving and preserving open access monographs, particularly those published by small and scholar-led presses.
Whatsapp in the Indonesian Online EFL Learning Milieu: How Do the Students Engage?

WhatsApp has been extensively used by many English as a Foreign Language (EFL) teachers in Indonesia as a near synchronous online learning platform. This program enables users to make announcements, exchange ideas and learning materials, and participate in online debates. Some recent publications inform its utilization in learning language, yet little discusses how students engage in the learning activities. Therefore, to fill this void, the present study aims to investigate the students' behavioral engagement during online learning using WhatsApp. To collect the data, this study employed observation and semi-structured interviews. An observation was completed in one class at a state university in Tasikmalaya, West Java, Indonesia to perceive how the students responded to the teacher's instructions during the learning process. Meanwhile, the interviews were conducted with nine students in a similar class. The data were analyzed using thematic analysis. The findings revealed that students engaged behaviourally by (1) posing questions or comments politely, (2) using emoticons and emojis as politeness reinforcements, and (3) confirming the unclear information behind the scene. Implications for teaching and further studies are discussed.
Camera Modes in Multi-user Virtual Reality Applications in Industry 4.0

This paper describes four scenarios of cooperation between a user in virtual reality and a user using interaction through computer peripherals. User cooperation is used in applications for Industry 4.0 in the field of employee training or in the deployment of digital twin companies. Four scenarios are devoted to the possibility of displaying scenes and the use of avatars in virtual reality using outside in tracked headsets and a user using a monitor display with control via a keyboard and computer mouse. Within the scenarios, we define the VR view, the use of a free camera, the character mode, and the automatic orbit camera, the movement of which depends on the position and orientation of the user in space in virtual reality. We show the applications of individual scenarios in a case study of user training in the field of mining and production in industrial enterprises. In the tests, we compared the increase in productivity of employees after training with the use of assisted VR in a multiuser environment.
On states of quantum theory

In this paper the generalized quantum states, i.e. positive and normalized linear functionals on $C^{*}$-algebras, are studied. Firstly, we study normal states, i.e. states which are represented by density operators, and singular states, i.e. states can not be represented by density operators. It is given an approach to the resolution of bounded linear functionals into quantum states by applying the GNS construction, i.e. the fundamental result of Gelfand, Neumark and Segal on the representation theory of $C^{*}$-algebras, and theory of projections. Secondly, it is given an application in quantum information theory. We study covariant cloners, i.e. quantum channels in the Heisenberg and the Schr\"{o}dinger pictures which are covariant by shifting, and it is shown that the optimal cloners can not have a singular component. Finally, we discuss on the representation of pure states in the sense of the Gelfand-Pettis integral. We also give physical interpretations and examples in different sections of the present work.
Human Face Mask Identification using Deep Learning with OpenCV Techniques

According to the WHO, COVID 19 virus is spread by respiratory droplets and personal contact. To prevent the transmission of this virus, the use of masks and social isolation is recommended. Because COVID 19 virus droplets may fall on any surface, the more crucial of the two measures is to wear a mask. It is vital to keep track of who is and is not wearing a mask. To comply with regulatory requirements, a mask recognition system capable of recognizing any kind of mask as well as masks in a variety of configurations inside video streams has been developed. To detect masks from images/video streams, a deep learning approach and the Python TensorFlow, Keras, and Pytorch packages are utilized. The suggested technology is capable of distinguishing persons who use masks from those who do not.
Estrategia de gestión del aprendizaje para el fortalecimiento de la argumentación de hechos en estudiantes de educación secundaria

The general objective of this research was to design a learning management strategy to strengthen the argumentation of facts in first year high school students of the I.E. Horacio Zeballos Gámez. The methodology used was a quantitative approach of applied type, with descriptive scope and with a non-experimental design of transversal mode. The information was obtained from the survey applied to the teachers of the school under investigation. The results show that only 16% of students are at the achieved level; it is understood that 8 out of 10 students need to strengthen their ability to argue facts because they are at a process or beginning level with respect to this skill. Based on the results, a proposal for learning management strategies(monitoring and feedback) was developed to promote the development of factual argumentation processes in the area of SocialSciences to improve the learning of first grade high school students, based mainly on the Theory of Constructivism, on the contributions of Piaget and Vigotsky, as well as on Ausubel's Meaningful Learning, also considering the current digital environment and reality.
THE ABILITY TO READ RESEARCH ARTICLES: PROFILE OF STUDENTS IN THE BIOLOGY EDUCATION PROGRAMS' OF UIN WALISONGO SEMARANG

This study aims to determine the profile of students of the undergraduate Biology Education program at UIN Walisongo Semarang. This study is descriptive qualitative with a survey method. The number of participants was 96 students of the Biology Education Program from different years. The instruments used were reading scientific articles and tests of the ability to read scientific articles. Data in the form of test answers, given a score and analyzed using the formula mean and percentage, then categorized. The results showed that the student's ability to read research articles was categorized as very low (Counterargument (15.67%) and Refutation (1.8%)), low (Motive (35.62%), Support (23.85), and Implication (28.17)), moderate (Novelty, 52.92%), and high (Objective (75.59%) and Main Conclusion 76.55%)). This research concludes that the ability to read scientific articles of undergraduate biology education students still needs improvement by applying learning methods/models or giving an assignment that can improve the ability to read scientific articles.
Handcrafted vs. Learned Features for Automatically Detecting Violence in Surveillance Footage

For many years, methods for detecting violence in video data used features designed by humans to extract visual information from input frames for composing feature vectors and then applied machine learning techniques to assign labels to them. Recently, Deep Learning methods are highly evidenced for this task since they can automatically learn image features. Furthermore, they usually overcome the accuracy rates obtained by classical methods based on handcrafted features. This work evaluates learned and handcrafted features for classifying video frames as 'violence' or 'non-violence'. Our results showed that learned features can not always be claimed superior since some violent scenes are only detected by handcrafted features.
Formative Assessment of College English Autonomous Learning Based on Fuzzy Comprehensive Evaluation Algorithm

With the rapid development of computer networks and multimedia technology, independent e-learning has become an integral part of modern education. However, the allocation of time and space for teaching and learning, the lack of instruction and guidance necessary for self-directed learning for students, and the development of e-learning are becoming increasingly problematic. Because of this, this thesis uses guided evaluation to determine the impact of improving the quality of Student learning. Firstly, under the background of college English web-based autonomous learning, this paper constructs a formative assessment index system and then combines the formative assessment with fuzzy comprehensive evaluation algorithm to form a formative assessment of college English web-based autonomous learning. Fuzzy comprehensive evaluation is an important application of analyzing complex fuzzy systems using the principle of fuzzy transformation. The simulation uses the online learning English behavior of college students to carry out an example evaluation. The results show that students can find their own shortcomings in network autonomous learning, which can be improved in later learning.