Reviewerly/exHarmony huggingface dataset:
PREPARATION OF LECTURERS FOR RESEARCH-BASED LEARNING: EUROPEAN EXPERIENCE

The article deals with preparation of lecturers for research-based teaching in the framework of professional development programs at European universities. It has been noted that the development of professional competencies among lecturers, necessary for such teaching, is determined by 2 factors: the orientation of the institutional policy and corporate culture of a higher educational institution towards the introduction of a holistic system of teaching based on research, as well as professional development programs at university aimed at improvement of existing professional competencies and acquisition of the new ones. The article analyzes the experience of University College London (UK) and the University of Gohenheim (Germany) in preparation of lecturers for research-based teaching. It has been confirmed that these higher educational institutions have implemented a holistic research-based teaching system, as well as extensive professional development programs for lectures to prepare them for such teaching, including not only trainings, but also individual consultations and seminars, mentoring support from more experienced colleagues, manuals, scientific resources, online directories, etc. The article presents actual examples of introduction by lecturers of a holistic research-based teaching system into the educational process at all educational levels.
Perbandingan Efektivitas Mengunyah Buah Apel (Malus Sylvestris Mill) dan Buah Semangka (Citrullus Lanatus) Sebagai Self-cleansing Terhadap Perubahan Indeks Debris Pada Siswa Kelas VII SMPN 30 Kota Padang

Debris is a leftover of food that found on the surface of an individual's teeth. Uncontrolled debris can cause dental caries. Eating fruits that contain fiber and water can reduce debris in the oral cavity, because it can increase salivary secretion during mastication. So that it can help the self-cleansing effect in the mouth. Objective: The purpose of this research was to find out the differences in the effectiveness of chewing apple (Malus sylvestris mill) with watermelon (Citrullus lanatus) as a self-cleansing in decreasing debris index in grade VII students of SMP 30 Kota Padang. Methods: This research used quasi-experimental design with pre and post-test design techniques that used purposive sampling with a 37 samples of people and consisted of two treatment groups. Self-administered questionnaire used to collect the information from respondent. Then, the data was analyzed by computerized analysts. Results: The average debris index before and after chewing apples has decreased by 1.170 with a standard deviation of 0.302. Then, the average debris index before and after chewing the watermelon decreased by 0.612 with a standard deviation of 0.17, according to the Wilcoxon test showed the p value of is 0,000 (p &lt;0.05). Conclusion: There are significant differences from the results of the debris index before and after consuming apples and watermelons in the two treatment groups. In the Mann Whitney test showed a value of p = 0,000 (p &lt;0.05)&#x0D;
In with the old, in with the new: machine learning for time to event biomedical research

Abstract The predictive modeling literature for biomedical applications is dominated by biostatistical methods for survival analysis, and more recently some out of the box machine learning approaches. In this article, we show a presentation of a machine learning method appropriate for time-to-event modeling in the area of prostate cancer long-term disease progression. Using XGBoost adapted to long-term disease progression, we developed a predictive model for 118 788 patients with localized prostate cancer at diagnosis from the Department of Veterans Affairs (VA). Our model accounted for patient censoring. Harrell’s c-index for our model using only features available at the time of diagnosis was 0.757 95% confidence interval [0.756, 0.757]. Our results show that machine learning methods like XGBoost can be adapted to use accelerated failure time (AFT) with censoring to model long-term risk of disease progression. The long median survival justifies and requires censoring. Overall, we show that an existing machine learning approach can be used for AFT outcome modeling in prostate cancer, and more generally for other chronic diseases with long observation times.
ERC-20R and ERC-721R: Reversible Transactions on Ethereum

Blockchains are meant to be persistent: posted transactions are immutable and cannot be changed. When a theft takes place, there are limited options for reversing the disputed transaction, and this has led to significant losses in the blockchain ecosystem. In this paper we propose reversible versions of ERC-20 and ERC-721, the most widely used token standards. With these new standards, a transaction is eligible for reversal for a short period of time after it has been posted on chain. After the dispute period has elapsed, the transaction can no longer be reversed. Within the short dispute period, a sender can request to reverse a transaction by convincing a decentralized set of judges to first freeze the disputed assets, and then later convincing them to reverse the transaction. Supporting reversibility in the context of ERC-20 and ERC-721 raises many interesting technical challenges. This paper explores these challenges and proposes a design for our ERC-20R and ERC-721R standards, the reversible versions of ERC-20 and ERC-721. We also provide a prototype implementation. Our goal is to initiate a deeper conversation about reversibility in the hope of reducing some of the losses in the blockchain ecosystem.
La competencia matemática de futuros maestros antes y después del confinamiento por la Covid-19

INTRODUCTION. Due to the pandemic, teachers and students have changed their way of teaching and learning. METHOD. To see if the competence in school mathematics in future teachers has varied before and after the confinement by Covid-19, a sample of four groups of students has been taken, two from the 2019/20 academic year, and another two from the 2021/22 academic year, belonging to the bilingual group and to a normal group who passed the sixth grade test to assess mathematical competence. RESULTS. The results indicate a considerable decrease in this two-year period in mathematical competence in Numbers, Measurement, Geometry and Uncertainty, and also in the three cognitive processes, the lowest being Knowing and reproducing. The differences are significant, while there are none between the results of the bilingual group and those of the normal group. CONCLUSIONS. The quarantine period and the online teaching format received have been able to influence the mathematical competence of future teachers. INTRODUCCIÓN. Debido a la pandemia profesores y alumnos han cambiado su forma de enseñar y de aprender. MÉTODO. Para ver si ha variado la competencia en matemática escolar en futuros maestros antes y después del confinamiento por la Covid-19, se ha tomado una muestra de cuatro grupos de alumnos, dos del curso 2019/20, y otros dos del curso 2021/22, pertenecientes al grupo bilingüe y a un grupo normal a los que se les pasó la prueba de sexto de primaria de evaluación de la competencia matemática. RESULTADOS. Los resultados indican un descenso considerable en este periodo de dos años en la competencia matemática en Números, Medida, Geometría e Incertidumbre, y también en los tres procesos cognitivos, siendo el que más baja Conocer y reproducir. Las diferencias son significativas, mientras que no las hay entre los resultados del grupo bilingüe y los del grupo normal. CONCLUSIONES. El periodo de cuarentena y el formato de enseñanza online recibido han podido influir en la competencia matemática de los futuros maestros.
TransKD: Transformer Knowledge Distillation for Efficient Semantic Segmentation

Semantic segmentation benchmarks in the realm of autonomous driving are dominated by large pre-trained transformers, yet their widespread adoption is impeded by substantial computational costs and prolonged training durations. To lift this constraint, we look at efficient semantic segmentation from a perspective of comprehensive knowledge distillation and consider to bridge the gap between multi-source knowledge extractions and transformer-specific patch embeddings. We put forward the Transformer-based Knowledge Distillation (TransKD) framework which learns compact student transformers by distilling both feature maps and patch embeddings of large teacher transformers, bypassing the long pre-training process and reducing the FLOPs by >85.0%. Specifically, we propose two fundamental and two optimization modules: (1) Cross Selective Fusion (CSF) enables knowledge transfer between cross-stage features via channel attention and feature map distillation within hierarchical transformers; (2) Patch Embedding Alignment (PEA) performs dimensional transformation within the patchifying process to facilitate the patch embedding distillation; (3) Global-Local Context Mixer (GL-Mixer) extracts both global and local information of a representative embedding; (4) Embedding Assistant (EA) acts as an embedding method to seamlessly bridge teacher and student models with the teacher's number of channels. Experiments on Cityscapes, ACDC, NYUv2, and Pascal VOC2012 datasets show that TransKD outperforms state-of-the-art distillation frameworks and rivals the time-consuming pre-training method. The source code is publicly available at https://github.com/RuipingL/TransKD.
Constructing Robust Emotional State-based Feature with a Novel Voting Scheme for Multi-modal Deception Detection in Videos

Deception detection is an important task that has been a hot research topic due to its potential applications. It can be applied in many areas, from national security (e.g., airport security, jurisprudence, and law enforcement) to real-life applications (e.g., business and computer vision). However, some critical problems still exist and are worth more investigation. One of the significant challenges in the deception detection tasks is the data scarcity problem. Until now, only one multi-modal benchmark open dataset for human deception detection has been released, which contains 121 video clips for deception detection (i.e., 61 for deceptive class and 60 for truthful class). Such an amount of data is hard to drive deep neural network-based methods. Hence, those existing models often suffer from overfitting problems and low generalization ability. Moreover, the ground truth data contains some unusable frames for many factors. However, most of the literature did not pay attention to these problems. Therefore, in this paper, we design a series of data preprocessing methods to deal with the aforementioned problem first. Then, we propose a multi-modal deception detection framework to construct our novel emotional state-based feature and use the open toolkit openSMILE to extract the features from the audio modality. We also design a voting scheme to combine the emotional states information obtained from visual and audio modalities. Finally, we can determine the novel emotion state transformation feature with our self-designed algorithms. In the experiment, we conduct the critical analysis and comparison of the proposed methods with the state-of-the-art multi-modal deception detection methods. The experimental results show that the overall performance of multi-modal deception detection has a significant improvement in the accuracy from 87.77% to 92.78% and the ROC-AUC from 0.9221 to 0.9265.
Domain Adaptive Semantic Segmentation via Entropy-Ranking and Uncertain Learning-Based Self-Training

Dear Editor, This letter develops two new self-training strategies for domain adaptive semantic segmentation, which formulate self-training into the processes of mining more training samples and reducing influence of the false pseudo-labels. Particularly, a self-training strategy based on entropy-ranking is proposed to mine intra-domain information. Thus, numerous false pseudo-labels can be exploited and rectified, and more pseudo-labels can be involved in training. Meanwhile, another novel self-training strategy is developed to handle the regions that may possess false pseudo-labels. In detail, a specific uncertain loss, that makes the network automatically decide whether the pseudo-labels are true, is proposed to improve the network optimization. Consequently, the influence of false pseudo-labels can be reduced. Experimental results prove that, compared with the baseline, the average mIoU performance gain brought by our method can attain 4.3%. Extensive benchmark experiments further highlight the effectiveness of our method against existing state-of-the-arts.
LWComicGAN: A Lightweight Method for Realizing Scene Animation

The style transfer algorithm was originally proposed to solve the generation problem of art paintings. In recent years, the generation of animation style images has gradually become a hot research direction. The content presented in many animation film and television works is fascinating. In order to satisfy people's desire to turn real scenes into animation scenes and reduce the workload of animation producers, a Light Weight Animation Generated Adversarial Network (LWComicGAN) is proposed, which can reduce the amount of parameters and enable low-memory devices to complete network training. An optional instance layer normalization function is designed to adapt the input of each layer, and an optional instance layer residual block is proposed. The LWComicGAN algorithm uses the objective function of WGAN-GP and other various loss functions as the total loss, and also considers the gradient penalty mechanism in discriminator. The former guarantees the generation quality of all aspects of the image, and the latter guarantees the stability of the training process. The effectiveness of the proposed algorithm is verified after animation transfer experiments of realistic landscapes and characters, and we have produced an ink painting dataset and completed ink animation style transfer.
THE EFFECT OF ADDITIONAL IRON SAND ON THE CRYSTAL SIZE AND STRAIN OF ZnO

This study observed the effect of the addition of iron (Fe) doping on the characteristics of Zinc Oxide (ZnO) nanoparticles.Zn(1-x)Fe(x) with various concentration of x = 0, 2, 4, 6, 8, and 10% was prepared by coprecipitation method using Sodium Hydroxide and Hydrogen Chloride. The effect of adding Fe doping on ZnO was characterized using X-ray Diffraction (XRD) to determine the crystal structure, size, and strain. The analysis result confirmed the absence of additional impurity peaks in the ZnO nanoparticles. Fe doping affects the Zn lattice structure. X-ray diffraction (XRD) study confirms the crystalline Hexagonal structure. Crystal size analysis using Debye Scherrer, UDM, and Halder-Wagner method obtained varying crystal sizes with variations in the concentration of Fe ranging from 19.89-44.72 nm. The crystal size and strain obtained from the Debye Scherrer method are smaller
Domain Vocabulary Alignment using AML and LogMap

Introduction: In the context of the Semantic Web, interoperability among heterogeneous ontologies is a challenge due to several factors, among which semantic ambiguity and redundancy stand out. To overcome these challenges, systems and algorithms are adopted to align different ontologies. In this study, it is understood that controlled vocabularies are a particular form of ontology. Objective: to obtain a vocabulary resulting from the alignment and fusion of the Vocabularies Scientific Domains and Scientific Areas of the Foundation for Science and Technology, -FCT, European Science Vocabulary -EuroSciVoc and United Nations Educational, Scientific and Cultural Organization -UNESCO nomenclature for fields of Science and Technology, in the Computing Sciences domain, tobe used in the IViSSEM project. Methodology: literature review on systems/algorithms for ontology alignment, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses -PRISMA methodology; alignment of the three vocabularies; and validation of the resulting vocabulary by means of a Delphi study. Results: we proceeded to analyze the 25 ontology alignment systems and variants that participated in at least one track of the Ontology Alignment Evaluation Initiative competition between 2018 and 2019. From these systems, Agreement Maker Light and Log Map were selected to perform the alignment of the three vocabularies, making a cut to the area of Computer Science. Conclusion: The vocabulary was obtained from Agreement Maker Light for having presented a better performance. At the end, a vocabulary with 98 terms was obtained in the Computer Science domain to be adopted by the IViSSEM project. The alignment resulted from the vocabularies used by FCT (Portugal), with the one adopted by the European Union (EuroSciVoc) and another one from the domain of Science &amp; Technology (UNESCO). This result is beneficial to other universities and projects, as well as to FCT itself
Developing Quizizz-Based Learning Media on Digestive System Materials

It requires media innovation in appropriate learning to assist students in comprehending the subject taught so that the intended learning outcomes can be achieved. It motivates schools and teachers to innovate in education relatively quickly. The study aimed to determine the responses and appropriateness of material and media experts as well as teachers and students to the Quizizz media for learning the developed Digestive System teaching materials. The study was conducted using the research and development (R&amp;D). The participants in this study and the content used was on the Digestive System. With a score of 96%, the study concluded that the material experts to the Quizizz media in online learning of digestive system teaching materials for XI class met the very good category. The media experts indicated that the developed learning media met 94.5% for being highly usable. The percentage of teachers who responded to the used media was 94.4%, while students' responses to the use of Quizziz were very good, reaching 91.5%.
Pengaruh Motivasi Belajar dan Kecerdasan Emosional Terhadap Hasil Belajar Siswa Kelas Tinggi SD Negeri 01 Nansabaris Padangpariaman

The Influence of Learning Motivation and Emotional Intelligence on Student Learning Outcomes. This study aims to reveal 1) the effect of learning motivation, 2) the effect of emotional intelligence on learning outcomes, and 3) the effect of learning motivation and emotional intelligence on high grade students' learning outcomes on student learning outcomes. This type of research is a correlational study. Sampling in this study used a total sampling technique. Collecting data on learning motivation and emotional intelligence were collected through a questionnaire. The data analysis technique used to test the hypothesis is simple and multiple correlation analysis and significant test using t-test. , t-count&gt;t-table at the 0.05 level of significance. (2) Furthermore, the statistical results of emotional intelligence on learning outcomes are obtained tcount = 5.3 and ttable = 1.67, (3) Then the statistical results for learning motivation and emotional intelligence on learning outcomes are obtained fcount = 20.48 and ftable = 3.32, fcount&gt;ftable. This means that there is an influence of learning motivation and emotional intelligence on student learning outcomes at SD Negeri 01 Nansabaris Padangpariaman
Literature review as a key step in research processes: case study of MA dissertations written on EFL of Saudi context

Purpose The aim of this study is to find out the most common types of literature review and the accuracy of citing information related to topic in question among Saudi English as a Foreign Language (EFL) postgraduate students at Al-Baha University. This study also aims at revealing the quality of the literature review written by researchers. Design/methodology/approach This qualitative study used content analysis to investigate 15 unpublished Master of Arts (MA) dissertations written on EFL of Saudi context. They were analyzed qualitatively using criteria modified from Snyder's (2019) model which is considered a potential method for making theoretical and practical contributions of literature review. Findings The findings of the study showed that students favored the systematic review over the integrative. Additionally, data showed that students were lacking in paraphrasing and organizing cited information coherently and appropriately. Moreover, students' performance was better in design, conduct, and data abstraction and analysis criterion, whereas they seemed rather weak in structuring and writing the review criteria. Originality/value The significance of the study is to provide researchers with methodological guidance and reference to write a comprehensive and appropriate literature review. Based on the findings, this study concluded with some implications that aim to assist researchers in carrying out their studies professionally. Furthermore, the findings provide decision-makers in higher education institutions with important practical implications. In light of the study's findings, it is suggested to carry out further research investigating postgraduate students to find out their perceptions and attitudes regarding the quality standards of scientific research writing and the paraphrasing strategies.
NIRF Ranking 2021: A Webometric Analysis of Top 10 University Websites of India

This learning is agreed out for analyzing the inside of university websites ranking NIRF (National Institutes RankingFramework) 2021 in India.A checklist was calculated and analysis of webmetric which examines the domain, domain age, parameter analysis of web impact factors and Alexa traffic rank of websites etc was done.Further, it was observed that all the university websites have used various small tools in their website-like SEO tools.Findings revealed that the "Amrita Vishwa Vidyapeetham" university website has the oldest domain registered on August 14, 1998, and "Indian Institute of Science university" website has the highest Domain Authority score 62 with first position among all Universities.It was found from the study that three universities from Karnataka achieved place in NIRF ranked top 10 universities of India.
Performance comparison of classifiers on twitter sentimental analysis

Twitter sentimental analysis is the way to examine polarity in tweeted opinions. The computational process involves implementing machine learning classifiers to categorize the tweets into positive, negative and neutral sentiments. To identify a suitable classifier for the task is a prime issue. In this paper we have presented the performance comparison of base classification techniques like Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbour and Logistic Regression on analysis of tweets. The results thus obtained show Logistic Regression analyze tweets with highest accuracy rate of 86.51% and the least performer comes out to be K-Nearest Neighbour with an average accuracy rate of 50.40%.