Reviewerly/exHarmony huggingface dataset:
Improving the Breast Cancer Image Classification using Autoencoders and CNN

Breast cancer is the most common disease among female rather than male, affecting 2.1 million women every year.Globally more than 70,000 women die from breast cancer every year.Deep learning architectures such as Convolution neural networks are mostly used for image classification.They fit well in classifying breast cancer images also.Several feature extraction mechanism have been already available.The CNN is also used for the feature extraction and for the image classification.For improving the classification accuracy, the Convolution auto encoders are used to extract the features and the output of the auto encoders are fed into the Convolution neural network.The objective of the work is to improve the classification accuracy by combining the feature extraction mechanism such as auto encoders along with the Convolution neural network for various types of breast cancer images like Histology images, Mammogram images and Sonogram images.
Modifying result states in Turkish

This study investigates the properties of a durative modifier that exclusively targets result states in Turkish. We call this modifier RSM, standing for Result State Modifier. What is peculiar about RSM is that it can express the actual duration of a result state, as well as a non-actual duration for an actual result state. Our goal is to describe the conditions under which these two interpretations are licensed and to provide a compositional analysis of how these interpretations are derived. We argue that dispositions play a key role in the way RSM can describe a non-actual duration for an actual result state.
Span Fine-tuning for Pre-trained Language Models

Pre-trained language models (PrLM) have to carefully manage input units when training on a very large text with a vocabulary consisting of millions of words.Previous works have shown that incorporating span-level information over consecutive words in pre-training could further improve the performance of PrLMs.However, given that span-level clues are introduced and fixed in pre-training, previous methods are time-consuming and lack of flexibility.To alleviate the inconvenience, this paper presents a novel span fine-tuning method for PrLMs, which facilitates the span setting to be adaptively determined by specific downstream tasks during the fine-tuning phase.In detail, any sentences processed by the PrLM will be segmented into multiple spans according to a pre-sampled dictionary.Then the segmentation information will be sent through a hierarchical CNN module together with the representation outputs of the PrLM and ultimately generate a span-enhanced representation.Experiments on GLUE benchmark show that the proposed span fine-tuning method significantly enhances the PrLM, and at the same time, offer more flexibility in an efficient way.The code is available at https://github.com/BAORONGZHOU/spanfine-tuning.
EXPLORING THE RELATIONSHIPS BETWEEN EARLY CHILDHOOD IN-SERVICE TEACHERS' CLASSROOM MANAGEMENT AND SELF-REGULATION SKILLS

&lt;p&gt;Self-regulation skills allow individuals to control emotions, behaviors, and thoughts. Teachers' management of their behaviors, emotions, and thoughts could impact their classroom management too. Thus, teachers' self-regulation and classroom management relationships were analyzed in the study. To date, emotional regulation was studied, but self-regulation has not been studied in classroom management context. The study was designed as a quantitative study. Two hundred forty-four early childhood in-service teachers participated in the study. Correlation tests were used to analyze gathering data. The study result shows that self-regulation skills and classroom management are not correlated. On the other hand, there is a relationship between all three sub dimensions of self-regulation (self-monitoring, self-evaluation, self-reinforcement) and classroom management skills. The study result suggests that the early childhood teachers' self-reinforcement is positively correlated with classroom management skills. In addition, self-evaluation and self-monitoring are negatively correlated with classroom management. The study recommends that the teachers could increase their classroom management skills with internal or external rewards. Furthermore, the teachers can use some standard grade scales to observe whether they have misconceptions about classroom management strategies objectively.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0957/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
A Study on Lecture Method about "Teaching Method of Moral Class" in Teaching Course "Method of Teaching Morals": Lecture Type or Participatory Type?

本研究では，教職課程の大学生217名を対象として，教職科目「道徳の指導法」の中の「道徳授業の教授法」の回において，「考え，議論する」道徳授業の実施を目標とした複数の異なる講義を行い，受講者の受講前後の道徳授業理解について検討した。講義に先立ち，道徳授業理解に関する尺度を作成し，最尤法・バリマックス回転を用いた因子分析を実施した。その結果，「教授法」，「教具」，「多様性」，「ジレンマ」の4因子が得られた。続いて，5条件（参加型・教材説明なし・4人討論，参加型・教材説明あり・4人討論，参加型・教材説明あり・2人討論，参加型・教材説明なし・2人討論，講義型）に対し，受講前後に本尺度を実施した。受講前の得点を統制し，共分散分析を実施した結果，条件により，尺度得点に差は見られなかった。しかし，いずれの条件も，受講前よりも受講後において高得点となっていた（教授法：F（1, 212）＝144.0，p＜.001；教具：F（1, 213）＝139.2，p＜.001；多様性：F（1, 215）＝66.0，p＜.001；ジレンマ：F（1, 216）＝75.7，p＜.001）。以上の結果より，講義種類による道徳授業理解に差はないが，受講により理解が深まっていることが示唆された。
Development of Bus and Line Control Method for Short-Circuit Current Reduction Using Genetic Programming

In this study, genetic programming (GP) is used for optimizing bus and line separation methods to reduce the short-circuit current. Expanding power systems led to intensive electric power and more transmission lines, reducing the grid impedance. This increases the short-circuit currents that occur during failure, and it is impossible to continue developing higher-capacity breakers to accommodate such short-circuit currents. Therefore, the short-circuit currents must be managed systematically through busbar separation and line separation. However, there are countless possible bus and line separation schemes for power systems. Furthermore, to comply with power-system reliability standards, no lines or transformers should be overloaded after such controls are applied. This paper proposes the use of GP to optimize the bus and line separation methods for obtaining a solution. The solutions are limited to methods that can be implemented in real power systems, reducing the convergence probability and optimization time. The proposed technique is useful for designing power systems with consideration of the short-circuit current.
THE EFECTIVENESS OF USING AUTHENTIC READING MATERIALS TOWARD STUDENTS’ READING COMPREHENSION MASTERY

Abstract&#x0D; A difficult reading material has become one of the student problems in comprehending the text. The application of using Authentic Reading Materials is needed for students. This research aims to know the effectiveness of using Authentic Reading Materials towards the students reading comprehension mastery. This experimental research used quantitative method and queasy experimental design. The research conducted at the sixth grade of English Education Department of Nahdlatul Ulama Lampung University which has the population 8 classes. The sample used in this research is 20 students as experimental class and 20 students as control class. The researcher uses purposive sampling as technique sampling. Based on the result of T test formula, tratio is 4,61 and ttable is 2,44. It means that tcount &gt; ttable. The result of this research indicates that using Authentic Reading Materials is effective towards the students reading comprehension mastery at the sixth grade of English Education Department of Nahdlatul Ulama Lampung University
Multi Objective Enhancement Method for Embedded Software Quality of e-Commerce Platform Based on Firefly Algorithm Optimization

Embedded software is the basic guarantee for the safe and stable operation of e-commerce platform. In order to improve the embedded software quality of e-commerce platform, a multi-objective enhancement method based on firefly algorithm optimization is proposed. Based on the two-dimensional strengthening model of embedded software duration cost of e-commerce platform, the application effect of embedded software is quantified by setting multi-level index system, so that the application effect of embedded software is included in the strengthening target. On this basis, the multi-objective strengthening model of embedded software duration, cost and application effect is constructed. The firefly algorithm is used to solve the multi-objective enhancement model, and the firefly algorithm is optimized by constraint priority non inferior ranking. The optimal compromise solution is selected according to the fuzzy membership degree in fuzzy mathematics. Experimental results show that the proposed method can significantly improve the quality of embedded software of e-commerce platform.
DISTANCE TEACHING AND PRESENCE TEACHING TOWARDS A POSSIBLE AND USEFUL INTEGRATION

The pandemic emergency seems to have extinguished many residual reservations regarding distance learning, but it has created a consensus that is all in all fragile, because it is non-critical and more charged with assimilative pressures. The problem is not establishing when, where and whether distance learning or face-to-face training should be promoted. The problem is to understand why and to specify, jointly, with which paradigms it is necessary to operate. It is a question of tackling problems that have remained on the sidelines for too long, in order to understand what digital can offer to ordinary teaching and what from ordinary teaching can also be useful for distance teaching. We will stop on two issues only. On the one hand we will try to enhance the logic of the reticularity and composability of knowledge; on the other hand, we will focus on curricular systems, showing how each of these two aspects can benefit both distance and face-to-face teaching and, above all, how it can benefit their possible integration. And this also in order to be ready for the next emergency. Ready, while hoping it won't come.
The general design of the automation for multiple fields using reinforcement learning algorithm

&lt;p&gt;Reinforcement learning is considered as a machine learning technique that is anxious with software agents should behave in particular environment. Reinforcement learning (RL) is a division of deep learning concept that assists you to make best use of some part of the collective return. In this paper evolving reinforcement learning algorithms shows possible to learn a fresh and understable concept by using a graph representation and applying optimization methods from the auto machine learning society. In this observe, we stand for the loss function, it is used to optimize an agent’s parameter in excess of its knowledge, as an imputational graph, and use traditional evolution to develop a population of the imputational graphs over a set of uncomplicated guidance environments. These outcomes in gradually better RL algorithms and the exposed algorithms simplify to more multifaceted environments, even though with visual annotations.&lt;/p&gt;
Supplemental Material: A tale of two Tweefonteins: What physical correlation, geochronology, magnetic polarity stratigraphy, and palynology reveal about the end-Permian terrestrial extinction paradigm in South Africa

(1) U-Pb CA-ID-TIMS Analytical Methods and Analyses, (2) Magnetic Polarity Methods and Stratigraphic Results, and (3) Descriptions of Fine-grained Lithofacies.
Efektivitas Media E-Paper Sebagai Wahana Produktifitas Meningkatkan Kemampuan Menulis Cerita Moral

Learning literature in the digital form makes instilling character values in students easier. Learning to write moral stories as a potential alternative to imitating good behavior may foster literary appreciation through the publication of the results of writing moral stories. This study examined the effectiveness of e-paper in improving the ability to write moral stories and students’ productivity in learning. The type of this study was quantitative-descriptive research using a pre-experimental design, specifically in the form of a one-group pretest-posttest design. In this study, data were collected using a written test and documentation. The results showed that (1) e-paper was effective in improving the ability to write moral stories. (2) the learning process involved e-paper as a medium for student productivity in writing moral stories, as reviewed from students’ learning outcomes published in the mass media.
Early Prediction of Neoplasms Using Machine Learning: A Study of Electronic Health Records from the Ministry of National Guard Health Affairs in Saudi Arabia

The early detection and treatment of neoplasms, and in particular the malignant, can save lives. However, identifying those most at risk of developing neoplasms remains challenging. Electronic Health Records (EHR) provide a rich source of "big" data on large numbers of patients. We hypothesised that in the period preceding a definitive diagnosis, there exists a series of ordered healthcare events captured within EHR data that characterise the onset and progression of neoplasms that can be exploited to predict future neoplasms occurrence. Using data from the EHR of the Ministry of National Guard Health Affairs (MNG-HA), a large healthcare provider in Saudi Arabia, we aimed to discover health event patterns present in EHR data that predict the development of neoplasms in the year prior to diagnosis. After data cleaning, pre-processing, and applying the inclusion and exclusion criteria, 5,466 patients were available for model construction: 1,715 cases and 3,751 controls. Two predictive models were developed (using Decision tree (DT), and Random Forests (RF)). Age, gender, ethnicity, and ICD-10-chapter (broad disease classification) codes as predictor variables and the presence or absence of neoplasms as the output variable. The common factors associated with a diagnosis of neoplasms within one or more years after their occurrence across all the models were: (1) age at neoplasms/event diagnosis; (2) gender; and patient medical history of (3) diseases of the blood and blood-forming organs and certain disorders involving immune mechanisms, and (4) diseases of the genitourinary system. Model performance assessment showed that RF has higher Area Under the Curve (AUC)=0.76 whereas the DT was less complex. This study is a demonstration that EHR data can be used to predict future neoplasm occurrence.
What to expect from a set of itemsets?

Dealing with redundancy is one of the main challenges in frequency based data mining and itemset mining in particular. To tackle this issue in the most objective possible way, we introduce the theoretical bases of a new probabilistic concept: Mutual constrained independence (MCI). Thanks to this notion, we describe a MCI model for the frequencies of all itemsets which is the least binding in terms of model hypotheses defined by the knowledge of the frequencies of some of the itemsets. We provide a method for computing MCI models based on algebraic geometry. We establish the link between MCI models and a class of MaxEnt models which has already known to be used in pattern mining. As such, our research presents further insight on the nature of such models and an entirely novel approach for computing them.
Personalized Recommender System for Arabic News on Twitter

Reading online news is the most popular way to read articles from news sources worldwide.Nowadays, we have observed a mass increase of information that is shared through social media and specially news.Many researchers have proposed different techniques that focus on providing recommendations to news articles, but most of these researches focused on presenting solution for English text.This research aimed to develop a personalized news recommender system that can be used by Arabic newsreaders; to display news articles based on readers' interests instead of presenting them only in order of their occurrence.To develop the system we have created an Arabic dataset of tweets and a set of Arabic news articles to serve as the source of recommendations.Then we have used CAMeL tools for Arabic natural language processing to preprocess the collected data.After that, we have built a hybrid recommender system through combining two filtering approaches: First, using a content-based filtering approach to consider the user's profile to recommend news articles to the user.Second, using collaborative filtering approach to consider the article's popularity with the support of Twitter.The system's performance was evaluated using two evaluation metrics.We have conducted a user experimental study of 25 respondents to perform an assessment to get the users' feedbacks.Also, we have used Mean Absolute Error (MAE) metrics as another way to evaluate the system accuracy.Based on evaluation results we found that hybrid recommender systems would recommend more relevant articles to users compared to the other two types of recommender system.
Optimasi peningkatan kualitas air sumur gali melalui penggunaan tawas (Aluminium Potassium Sulfate) terhadap Escherichia coli

Background: well water provide water that comes from a layer of soil that is relatively close to the ground surface, therefore it is easily exposed to contamination through seepage. In general, seepage comes from human excreta, latrines or latrines and animals, as well as from well waste itself, either because the floor or the sewerage are not impermeable to water. The condition of construction and the way the well water is drawn can also be a source of contamination, for example wells with open construction and drawing water with a bucket. One of the fecal contamination bacteria found in well water is Escherichia coli. Alum (Aluminum sulfate) is one of anti-microbial.Objective: To determine the effect of alum with concentrations of 5%, 10% and 15% on the amount of Escherichia coli in well waterMethod: explanatory research (explanatory research) which is to explain the relationship between variables through comparison and hypothesis testing, using a quasi experimental design method with the experimental design used in this study is Completely Randomized Design with 3 repetitions.Results: The results of the ANOVA test on the content of E. coli with various concentrations obtained a P value of 0.000 an alpha value of 0.05, indicating that there was a significant difference in the observed concentrations.Conclusion: Alum with concentrations of 5%, 10% and 15% affected the number of Escherichia coli in well water. The higher the concentration of alum, the better in inhibiting the growth of E.coli bacteria in well water.Conclusion: Alum with concentrations of 5%, 10% and 15% affected the number of Escherichia coli in well water. The higher the concentration of alum, the better in inhibiting the growth of E.coli bacteria in well water.
“The Fact is That Everything He is Saying So Far is Simply a Lie” Expressive Truthmaking in the Political Debate

The Fact is That Everything He is Saying So Far is Simply a Lie" Expressive Truthmaking in the Political Debate* This study investigates language use in political debates.It takes a closer look at linguistic markers of expressivity and truthmaking on word level in a presidential debate between Donald Trump and Joe Biden.The results show clear differences between these two debaters.Trump uses expressive language more extensively than Biden.Examples of markers that Trump uses more often than Biden are reinforcing: very, much, super, totalizing: all, every, never, mental: know, think, want, verbal: tell, talk, say, hypothetical: if, could, would, and self-reference: I, me.Examples of markers that Biden uses more often that Trump are reinforcing: more, totalizing: none, mental: feel verbal: ask, hypothetical: should, and self-reference: my.All in all, Trump uses these kinds of linguistic resources both more often and in lager clusters.There is also a difference between Trump and Biden in their choice of markers from the same group, for example between know (Trump) and feel (Biden), and between tell (Trump) and ask (Biden).The methodological results show that our current constellation of linguistic markers is effective as an analytical tool and highlights the importance of expressivity, in relation to empirical and logical truth, as a crucial component of truthmaking in political debates.
Peer review declaration

All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Double-blind • Conference submission management system : Manual • Number of submissions received : 112 • Number of submissions sent for review : 110 • Number of submissions accepted : 103 • Acceptance Rate (Number of Submissions Accepted/Number of Submissions Received X 100) : 91% • Average number of reviews per paper : 2 • Total number of reviewers involved : 70 • Any additional info on review process : Contact person for queries : Name : Prof. Tarik Talib Issa AL-Omran Affiliation: University of Baghdad, Iraq Email : eduglobal@pamconf.com
Interdisciplinarity and Universality of Foreign Languages Education

Transformative potential of the knowledge economy of the XXI century, establishment of networked society, emergency digitization due to the COVID-19 pandemic measures have imposed elaborate interdisciplinary and transdisciplinary demands on the marketability of Liberal Arts university graduates' skills and competences, upon entering the workforce. The study is focused on the in-depth diagnostics of the development of multipurpose orientation, universality and interdisciplinarity of skillsets for students of European (English, Spanish, French, Italian, German) and Oriental (Mandarin Chinese, Japanese) Languages major programs through the span of educational activities in the time-frame of COVID-19 quarantine measures of March 2020 to January 2021. A computational framework of foreign languages education interdisciplinarity is introduced in the study. The survey analysis is used to evaluate the dimensions of interdisciplinarity, universality and transdiciplinarity, informed by the interoperability of soft skills and digital communication skills for Foreign Languages Education across contrasting timeframes and stages of foreign languages acquisition and early career training.
Performance analysis of hybrid cryptosystem - A technique for better security using blowfish and RSA

Security is the most concerned topic in this today’s world of Information and Communication Technology. Single layer security can be breached easily so hybrid cryptographic system has been introduced. Hybrid system can be made by combining two or more algorithms of similar or dissimilar types. Here, combing of Blowfish algorithm along with RSA algorithm is performed. After combination their performance analysis is done based on five parameters namely Throughput, Encryption time, Decryption time, Total Execution time and Plain text size to cipher text size ratio with different key size of Blowfish algorithm ranging from 32 bit-448 bit. Among these 5 different hybrid cryptosystem we found that the Blowfish RSA system with key size 448 bit has better performance than all other bit size.