Reviewerly/exHarmony huggingface dataset:
Effects of ATDE Teaching Mode during Online Teaching on Creative Thinking Ability of Learners

Abstract: Online teaching based on computer and network technology has become a vital teaching mode under the current situation of the overwhelming COVID-19 epidemic. Teachers and students who fully understand and master basic knowledge and skills of various information technologies in online learning can stimulate students’ interest in learning information technology and train their ability in information collection, processing, and use. Given the lack of face-to-face communication and deep emotional exchange, online teaching has poor performance in improving the creative thinking ability of learners. During online teaching, teachers train the innovation consciousness quality of students in online courses successfully and improve the creative thinking ability of learners by using “asking,” “thinking,” “doing,” and “evaluation,” that is, the ATDE teaching mode. In this study, a teaching experiment was carried out on 41 undergraduates admitted in 2018 who majored in civil engineering at Zhengzhou University of Industrial Technology, Henan Province, China. Students were divided into the experimental group and the control group. The experimental group adopted the ATDE teaching mode for the course Design of concrete structure, while the control group applied the traditional teaching mode. A questionnaire survey of students’ creative thinking ability was conducted for both groups. Results showed that university students’ creative, emotional attitude under the ATDE teaching mode was far higher than that under the traditional (F=6.490, p=0.015). The creative thinking performance of university students under the ATDE teaching mode was higher than that under the traditional teaching mode (F=7.468, p=0.009). The Pearson correlation coefficient between creative, emotional attitude and creative thinking performance was 0.985 in the late test stage, which was higher than in the early test stage (0.480), which reveals that the ATDE teaching mode was conducive to improving abstract course learning effect of students majoring in civil engineering. Research conclusions can provide some references to investigate the feasibility of ATDE teaching mode in universities and offer a new choice and feasible path to train students’ creative thinking ability.
Development of an Interpolation Scheme for Seismic Indexes in Lima Metropolitan, Peru

In recent years, the development of seismic networks in Metropolitan Lima, administrated by public and private institutions, has received special attention since it makes possible the quantification of different seismic indexes under the occurrence of earthquakes. Therefore, the integration of the information both from acceleration sensors and site conditions from microzoning studies allows the estimation of the possible extent of the damage in quasi-real time. In this study, the implementation of a system to evaluate seismic parameters in a uniform grid of 250 x 250 m2 resolution is reported. In this regard, peak ground acceleration (PGA) values from the available time-history records are computed and reduced to the engineering bedrock level. Then, by means of the interpolation technique called Ordinary Kriging, in which each seismic station is considered as a random variable and the correlation between a pair of such random variables depends only on the distance between their coordinates, the acceleration distribution is evaluated. Amplification factors are applied in order to finally bring the PGA up to the surface level. A quantitative evaluation of the accuracy of our results is performed using two recent earthquakes with moment magnitude larger than 5: the 2019 Mw 8.0 Lagunas earthquake and 2021 Mw 6.0 Mala earthquake. The results have reproduced to some extent the seismic response of the diverse geomorphological deposits in Metropolitan Lima and suggest the inclusion of a larger number of strong motion stations in order to reduce the estimation errors.
Estimation of Precise Heading by Segmenting the Motion of Individual Steps and LSTM

Precise estimation of heading, which is a key component of pedestrian dead reckoning for indoor positioning methods, is needed. We propose a new method that improves the accuracy of the heading estimation by processing data at a resolution finer than one step. First, the sensor data are divided into segments finer than a step by using the norm of the acceleration vectors. Then, features to predict the heading are automatically extracted from the segments by using a neural network based on long short term memory, instead of using the statistics of steps as features as is done conventionally. Our method is more accurate than the conventional one for walking in straight lines and walking on curved trajectories.
PENGEMBANGAN MODEL PEMBELAJARAN CONNECTING, ORGANIZING, REFLETING, EXTENDING (CORE) BERBASIS GOOGLE SITES SEBAGAI PEMBAHARUAN PEMBELAJARAN MATEMATIKA PADA SEKOLAH MENENGAH PERTAMA KELAS 8 MATERI BANGUN RUANG SISI DATAR

In this modern era, developments are very rapid in various fields, such as the fields of politics, economics, education, and technology. The development of technology has a very big influence, especially in the field of education. The purpose of this study was to determine the results of the development of the Connecting, Organizing, Reflecting, Extending (CORE) learning model based on Google Sites as a renewal of mathematics learning in Grade VIII Junior High Schools. Where the method used to describe and describe the data is by analyzing previous studies that have existed through the collection of references from previous journals. The results of data analysis using qualitative data analysis techniques state that in learning using Google Sites, teachers can easily add learning materials in pictures, videos, and assessments; These features can be designed with the CORE (Connecting, Organizing, Reflecting and Extending) learning model. The stages carried out are the process of taking mathematics learning online and offline accompanied by interactions between teachers and students using the comments column provided by the teacher on the google sites site page, while the interactions carried out offline are discussions or questions and answers. One of the advantages of Google sites is that it makes it easier for students to learn&#x0D;
MOTIVATIONAL FACTORS OF TEACHING UKRAINIAN EDITING TO HIGH SCHOOL STUDENTS

Background. With the variety of language norms in Ukrainian, the school faces a challenging the task of teaching students how to master effective text creation. The skill is built when schoolchildren are already aware of the motives of the actions and operations they carry out with the text: comprehension, oral and written production, transformation, and editing. However, the school curriculum in Ukrainian language envisages teaching editing as an additional activity targeted at applying the knowledge of the language norms learned in the course to text production. Purpose: The aim of the article is to determine and analyze the motivational factors for the development of the editing competence of high school students and their use as a basis for the block organization of teaching Ukrainian text editing to the 10th and 11th graders of secondary school for effective learning of language norms. Methodology. The study was conducted in November-December 2022; the participants were 10th- grade (n=25) and 11th- grade (n=24) students of specialist philology-oriented school № 98 of Kyiv. The survey included a short answer, which was mandatory for all students, and an extended optional answer (feedback) which could be provided by the schoolers who demonstrated a persistent cognitive interest in various aspects of the Ukrainian language as well as in editing. The research framework was designed by the authors of the article; its implementation involved consulting and assisting four Ukrainian language and literature teachers of the above-mentioned school: all of them are professionals with 9-16 years of teaching experience who were instructed in advance. Results and discussion. The study compared motivational factors with the items of the AMS-28 scale (Vallerand, Blais) and confirmed the significance of considering them in the process of teaching editing to high school students. Additionally, this significance was confirmed by the feedback provided by the students. The conducted research has enabled designing the block of exercises and tasks to include in the high school curriculum for developing the skills of editing. The language norms are supposed to be mastered as a result of the analysis of editing techniques. Despite the positive results of the research, further studies are needed to examine training scenarios for individual editing techniques (comparison of textual variations, dictionary analysis, text transformations, error correction) as well as determination of motivational factors that influence the process of building editing competence for specific purposes (translation, creative writing, oratorical skills).
Cross-Domain Adaptation for RF Fingerprinting Using Prototypical Networks

Radio frequency (RF) fingerprinting is a hardware feature used in Internet of Things (IoT) applications to identify wireless devices. In this paper, we propose few-shot learning (FSL) and prototypical networks (PTNs) to create a new model that can adapt to a new domain with very few labeled examples. The proposed model can mitigate the domain shift caused by changing RF environments. Experimental results show the proposed method can improve the performance of RF fingerprinting over different domains.
MetaEM: Meta Embedding Mapping for Federated Cross-domain Recommendation to Cold-Start Users

Cross-domain recommendation exploits the rich data from source domain to solve the cold-start problem of target domain. Considering the recommendation system contains some user private information, how to provide accurate suggestions for cold-start users on the basis of protecting privacy is an important issue. Federated recommendation systems keep user private data on mobile devices to protect user privacy. However, compared to federated single-domain recommendation, federated cross-domain recommendation needs to train more models, making resource-constrained mobile devices infeasible to run large-scale models. In view of this, we design a meta embedding mapping method for federated cross-domain recommendation called MetaEM. The training stage of MetaEM includes pretraining and mapping. The pretrain stage learns user and item embeddings of source domain and target domain respectively. Items embeddings are divided into common and private. The common embeddings are shared by all users, and we train a meta-network to generate private embeddings for each user. The mapping stage learns to transfer user embeddings from source domain to target domain. In order to alleviate the negative impact of users with low number of ratings on mapping model, we employ a task-oriented optimization method. We implement the MetaEM prototype on large real-world datasets and extensive experiments demonstrate that MetaEM achieves the best performance and is more compatible with complicated models compared to other state-of-the-art baselines.
Understanding Context to Capture when Reconstructing Meaningful Spaces for Remote Instruction and Connecting in XR

Recent technological advances are enabling HCI researchers to explore interaction possibilities for remote XR collaboration using high-fidelity reconstructions of physical activity spaces. However, creating these reconstructions often lacks user involvement with an overt focus on capturing sensory context that does not necessarily augment an informal social experience. This work seeks to understand social context that can be important for reconstruction to enable XR applications for informal instructional scenarios. Our study involved the evaluation of an XR remote guidance prototype by 8 intergenerational groups of closely related gardeners using reconstructions of personally meaningful spaces in their gardens. Our findings contextualize physical objects and areas with various motivations related to gardening and detail perceptions of XR that might affect the use of reconstructions for remote interaction. We discuss implications for user involvement to create reconstructions that better translate real-world experience, encourage reflection, incorporate privacy considerations, and preserve shared experiences with XR as a medium for informal intergenerational activities.
Maximum Mean Discrepancy Kernels for Predictive and Prognostic Modeling of Whole Slide Images

How similar are two images? In computational pathology, where Whole Slide Images (WSIs) of digitally scanned tissue samples from patients can be multi-gigapixels in size, determination of degree of similarity between two WSIs is a challenging task with a number of practical applications. In this work, we explore a novel strategy based on kernelized Maximum Mean Discrepancy (MMD) analysis for determination of pairwise similarity between WSIs. The proposed approach works by calculating MMD between two WSIs using kernels over deep features of image patches. This allows representation of an entire dataset of WSIs as a kernel matrix for WSI level clustering, weakly-supervised prediction of TP-53 mutation status in breast cancer patients from their routine WSIs as well as survival analysis with state of the art prediction performance. We believe that this work will open up further avenues for application of WSI-level kernels for predictive and prognostic tasks in computational pathology.
A Framework for Feature Selection using Data Value Metric and Genetic Algorithm

Most organizations analyse input data to develop an accurate description or model using the features present in the data.There have been huge amount of generated data in the big data ecosystem which demand better and efficient ways to distil high utility or value from it so as to compliment decision makers in making recommendations and decisions.Before applying classification algorithm, relevant features are selected by a suitable feature selection algorithm.Data Value Metric (DVM) is an information theoretic measure based on the notion of mutual information which has been shown to be a good metric to validate the quality and utility of data in a big data ecosystem and in traditional data.Data Value Metric (DVM) suffers from local minima and loss of diversity in the population since it is using forward selection search strategy, however, hybridizing it with Genetic Algorithm is hoped to overcome the problem of local minima as there would be a blend of evolutionary search to ensure a balance between exploration and exploitation of the search space.This paper proposed the hybrid model of Genetic Algorithm and Data Value Metric (DVM) as an information theoretic metric for quantifying the quality and utility for feature selection which can be applied to traditional data.
YOUTUBE AS INSTRUCTIONAL MEDIA IN PROMOTING EFL INDONESIAN STUDENTS’ PRONUNCIATION

&lt;p align="center"&gt;&lt;strong&gt;Abstract&lt;/strong&gt;&lt;/p&gt;&lt;p class="abstrak"&gt;Pronunciation for Indonesian EFL students is tricky and challenging due to the different phonetic systems of the student's mother tongue and the target language. Integrating YouTube into English language learning could overcome these issues. The present study reviewed previous studies about the benefits of integrating YouTube as instructional media in promoting EFL students' pronunciation. This study was library research following George's (2008) model by selecting recent publications on the topic to summarize how YouTube videos affect students' English pronunciation and highlight a research gap that could be taken by future research. The review revealed that YouTube had benefitted students in aspects of pronunciation: stress, intonation, rhythm, voice quality, gestures, vowels, and consonants, although research on the application's use for improving students' rhythm is minimal despite the availability of relevant videos. This finding implies the need for more research on improving students' rhythm using YouTube video.&lt;/p&gt;&lt;p class="abstrak"&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt;Bahasa Indonesia Abstrak&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;br /&gt;Pengucapan bahasa Inggris sangat rumit untuk pebelajar Indonesia karena perbedaan dalam sistem fonetik bahasa ibu dan bahasa target siswa. Integrasi video YouTube ke dalam pembelajaran bahasa Inggris dapat mengatasi masalah ini. Kajian ini mencermati hasil penelitian terkini mengenai integrasi video YouTube sebagai media pembelajaran untuk meningkatkan pelafalan bahasa Inggris siswa. Penelitian ini merupakan kajian pustaka menggunakan model dari George (2008), dengan melibatkan peneitian terkait untuk menghasilkan gambaran umum mengenai pengaruh video Youtube terhadap pelafalan siswa untuk menyoroti kesenjangan penelitian yang masih ada sehingga menjadi masukan untuk penelitian selanjutnya. Ditemukan bahwa Youtube meningkatkan pelafalan siswa dari tekanan, intonasi, ritme, kualitas suara, gestur, pelafalan suara vokal dan konsonan. Namun, penelitian yang menyoroti pemanfaatan Youtube untuk meningkatkan ritme dalam pelafan siswa masih terbatas, walaupun Youtube menyediakan banyak video yang relevan. Temuan ini berimplikasi pada perlunya penelitian lebih lanjut mengenai pemanfaatan video YouTube untuk mengembangkan ritme pelafalan bahasa Inggris siswa.&lt;/p&gt;
Variety, Velocity, Veracity, and Viability: Evaluating the Contributions of Crowdsourced and Professional Fact-checking

The tension between the increasing need for fact-checking and the limited capacity of fact-check providers inspired several crowdsourced approaches to address this challenge.However, little is known about how effectively crowdsourced fact-checking might perform, and there is no comprehensive framework to evaluate such fact-check providers.We fill this gap by proposing such a framework, using four dimensions (Variety, Velocity, Veracity, and Viability) to assess and compare the contributions of a crowdsourced fact-checking community and professional fact-checking sites. Our analysis shows the different focus these two types of sites have in terms of topic coverage (variety) and demonstrates that while crowdsourced fact-checkers are much faster than professionals (velocity) to answer new requests, these fact-checkers often build on the existing professional knowledge for repeated requests.In addition, our findings indicate that the accuracy of the crowdsourced community (veracity) parallels that of the professional sources; and that the crowdsourced fact-checks are perceived quite close to professionals in terms of objectivity, clarity, and persuasiveness (viability).
Are Institutional Research Data Policies in the US Supporting the FAIR Principles? A Content Analysis

Objective: The FAIR principles were created with the goal of enhancing the reusability of research data and to give guidance on how to make data Findable, Accessible, Interoperable and Reusable. In this article we explore the role of institutional research data policies in enabling and encouraging researchers at their institutions to generate FAIR data.Methods: We identified the research data policies in place for “very high research activity” institutions (as defined by Carnegie classification) in the United States. We created a list of 31 criteria, based on previous work by Davidson et al. (2019) and Briney et al. (2015), and evaluated the 40 policies using a content analysis methodology.&amp;nbsp;Results: The guiding principles and the definitions for research data in the policies support the idea that institutional policies are a potential tool for the implementation of the FAIR principles. However, our analysis indicates that they are not generally used for that purpose. Only one policy mentions FAIR. Data sharing is mentioned in half of the policies, but 11 of these only note this concept in the context of funder requirements. Access and retention sections are mostly written without considering publicly available data. Twenty-nine policies do not mention data documentation.&amp;nbsp;Conclusions: We discuss ways in which these institutional policies represent a missed opportunity to implement the FAIR principles and suggest ways policies could be modified to encourage researchers to follow them. We also discuss future research opportunities to examine how policy implementation may affect what institutional support researchers receive.
The Effects of Using Microsoft Teams on Improving EFL Learners&amp;#39; Speaking Abilities at Unaizah High School Students

Advanced technology has affected all aspects of life, including education. Technology in education has proven effective in several topics, including learning ESL/EFL. By reviewing previous research, the study aimed to explore the effects and challenges of using Microsoft Teams as an online learning tool on EFL learners at Unaizah, Saudi Arabia, from the students&amp;rsquo; point of view. To improve their speaking skills more efficiently than in traditional classrooms, a questionnaire as a descriptive approach was used to collect data. The sample consisted of 351 female students. The results indicated that learners find using Microsoft Teams has advantages in improving English speaking skills. Thus it had a positive impact. Furthermore, the study found that learners face challenges in using Microsoft Teams. Moreover, there are no statistically significant differences at the level of the significance (&amp;alpha; &amp;le; 0.05). The study recommends that educational institutions apply Microsoft Teams for its advantages.
Theoretical Background of Cryptography

Cryptography is the practice of secure communication and the study of secure communication techniques. The theoretical foundations of cryptography include concepts such as cryptography, secure key exchange, digital signatures, and authentication. These technologies are used to protect the confidentiality, integrity, and reliability of information while it is being transmitted over a network or stored in digital form. The core of encryption is the concept of an encryption algorithm, which is a set of mathematical rules used to encrypt and decrypt data. The security of a cryptographic system depends on the strength of the underlying algorithm and the confidentiality of the key used to encrypt and decrypt the data. In this paper, Cryptography concepts and modern techniques are reviewed for types of encryption algorithms and the best ones, and many types of attacks and their impact on the encryption process
Retraction: Lamine, S.; Saunders, I. Phytoremediation of Heavy-Metals-Contaminated Soils: A Short-Term Trial Involving Two Willow Species from Gloucester WillowBank in the UK. Minerals 2022, 12, 519

The journal retracts the article “Phytoremediation of Heavy-Metals-Contaminated Soils: A Short-Term Trial Involving Two Willow Species from Gloucester WillowBank in the UK” [...]
Investigating Labeler Bias in Face Annotation for Machine Learning

In a world increasingly reliant on artificial intelligence, it is more important than ever to consider the ethical implications of artificial intelligence on humanity. One key under-explored challenge is labeler bias, which can create inherently biased datasets for training and subsequently lead to inaccurate or unfair decisions in healthcare, employment, education, and law enforcement. Hence, we conducted a study to investigate and measure the existence of labeler bias using images of people from different ethnicities and sexes in a labeling task. Our results show that participants possess stereotypes that influence their decision-making process and that labeler demographics impact assigned labels. We also discuss how labeler bias influences datasets and, subsequently, the models trained on them. Overall, a high degree of transparency must be maintained throughout the entire artificial intelligence training process to identify and correct biases in the data as early as possible.