Reviewerly/exHarmony huggingface dataset:
A Comparative Study of English Writing Strategies Used by English Majors and Non-English Majors from the Perspective of Self-regulated Learning

The purposes of this study were to identify the self -regulated learning strategies students use when learning writing skills, to find out the hindering factors students face to practice self-regulated learning strategies and to explore the strategies used by writing skills instructors to encourage their students' use of self -regulated learning strategy. The students self regulated learning strategies questionnaire and writing skills instructors' semi-structured interviews were the instruments to collect the data. The questionnaire that was adapted from Barnard, Lan, To, paton, & Lai, (2009) was designed based on the review of the related literature and it consisted of 27 closed-ended items arranged thematically as time management, self evaluation, attention control, self motivation and help seeking and 2 open-ended items (concerning the challenges that students face when learning writing skills courses and about what specific strategies they need from their writing skills instructors to help them effective self regulative learner). The participants of the study were selected using available and purposive sampling technique. The data collected from the participants using self -regulated learning strategies questionnaire were analyzed via percentage, and the result showed that students are good at using time management (but deficient in using their non-lecture time & their arranged plan), self motivation (though they showed resistance not to work on more writing practice tasks), social support, and attention control strategies (in some extent) but in meta-cognitive/self evaluation strategies, they were not found good. The result also showed that lack of awareness among students on what strategy to choose and how to use it, lack of interest among students to work cooperatively with mates, lack of more concentrated practice on writing tasks, learners' resistance to more writing tasks given by their teachers, teacher dominated corrective feedback provision to every activity, and lack of sufficient time to teach what strategy students need to use are some of hindering factors. The courses' instructors were also found using explicit corrective feedback, and explicitly explaining strategies called direct instruction, and student centered instructions like group work. Finally, this study had suggested writing skills instructors and EFL students that they should use more strategies like guided and independent practice, should minimize an explicit corrective feedback that means use progress feedback, and use reflective practices. In addition, further research into the EFL students' use of self regulated learning strategies against different variables is also recommended.
AI BASED REALTIME FACE MASK DETECTION

The world has not yet fully Recover from this pandemic and the vaccine that can effectively treat Covid-19 is yet to be discovered. Wearing a face mask will help prevent the spread of infection and prevent the individual from contracting any airborne infectious germs. Done by evaluation of the classification results by analysing real-time streaming from the Camera. Using Face Mask Detection System, one can monitor if the people are wearing masks or not. Here HAARCASACADE algorithm is used for image detection. Collating with other existing algorithms, this classifier produces a high recognition rate even with varying expressions, efficient feature selection and low assortment of false positive features. HAAR feature-based cascade classifier system utilizes only 200 features out of 6000 features to yield a recognition rate of 98-100%. According to this motivation we demand mask detection as a unique and public health service system during the global pandemic COVID-19 epidemic. The model is trained by face mask image and non-face mask image. KEYWORDS Face mask detection Face edges (FMD) CNN- Convolutional Neural Network
Exploratory study of a system to reduce information overload and tunnel vision in homicide investigations

In homicide investigations, the growing availability of data results in an increasing amount of information and Persons of Interest (PoIs) that can be collected and incorporated during an investigation. This might result in information overload and increased tunnel vision during a homicide investigation. In this paper, we designed a system to support homicide investigations in such a way that it reduces information overload and tunnel vision. For evaluation purposes, we built a prototype that was filled with a fictional homicide investigation. A user study indicated that criminal investigators experienced a significantly low level of information overload and tunnel vision using the prototype. Moreover, the results showed acceptable usability and verbal statements indicated a largely positive attitude towards the prototype. This research clearly shows the opportunity to use interface design artefacts to support the prevention of information overload and tunnel vision.
Combatting Hate in New Zealand: The Problems with Hate Crime Legislation and the Importance of Non-Criminal Alternatives

This article discusses the Royal Commission of Inquiry's recommendation to reform New Zealand's hate crime legislation following the Christchurch terror attack. New Zealand currently uses a sentencing enhancement provision that has faced much criticism for being unable to reflect the serious nature of hate-motivated offending. It is also poorly enforced. The Commission recommended replicating the United Kingdom's approach by creating separate hate crime offences. This article argues that this is not the most productive way to combat hateful conduct and achieve the Commission's broader goal of social cohesion. Evidence from the United Kingdom suggests that many of the intended benefits of separate offences do not eventuate in practice. This article considers that criminalisation is not the best way to address hateful conduct generally. Hate crime laws risk being counter-productive and are unlikely to change societal attitudes. The conclusion is that it would be more beneficial to focus on non-criminal anti-hate responses, such as education. These alternative anti-hate methods will be more likely to address the root causes of hostility, prevent the development of hateful attitudes and thus reduce the frequency of hate-motivated offending in New Zealand.
INTERCULTURAL COMMUNICATION BETWEEN BATAKNESE STUDENTS AND SUNDANESE STUDENTS AT PADJADJARAN UNIVERSITY

Intercultural communication is inevitable when entering the continuity of student life. Students from various ethnicities gather in one place to gain knowledge at the University. Padjadjaran University is one of the most popular universities in Indonesia. This study aims to determine how the intercultural communication between Sundanese and Batak ethnic students at Padjadjaran University. The theory used in this research is the process of intercultural communication and barriers to intercultural communication. This study uses a qualitative method. This study uses a phenomenological approach. The subjects in this study were 2 ethnic Sundanese students and 2 ethnic Sundanese students from Padjadjaran University and the object of this research was how the process of intercultural communication was and what were the barriers to intercultural communication. This study uses a collection technique, semi-structured in-depth interview. The results of this study show that intercultural communication of language is an important thing for the process of intercultural communication because the language itself is also an obstacle when conducting intercultural communication where students of ethnic Sundanese have difficulty regarding the Batak language on the other hand, ethnic Batak students have difficulties regarding the Sundanese language.
Vocational Education Platform Based on Block Chain and IoT Technology

Due to the poor effect of the traditional vocational education platform, it is difficult to meet the requirements of the current Internet education platform. To solve this problem, this paper designs a vocational education platform system based on blockchain and Internet of Things (IoT) technology. First, the system access control strategy based on blockchain technology is introduced into the teaching system so as to improve the security of the teaching system. Then, in order to solve the single point of failure and efficient transmission problems of access control in the IoT environment, this paper proposes an access control strategy based on main side chain cooperation. This strategy introduces a side chain to expand the blockchain and improve the data carrying capacity of the blockchain. To meet the requirements of high-speed data transmission, this paper designs an access control model suitable for the main side chain blockchain structure. Experimental results show that compared with other algorithms, the data request speed of the system in this paper is faster, and the storage space is smaller. Therefore, the proposed technology based on blockchain and IoT can meet the use requirements of vocational education platforms.
Polymerization of Poly Methyl Methacrylate Using Emulsion Method and H2O2 as Initiator

This study aims to study the effect of the amount of initiator (H2O2) on the viscosity of the Polymethyl Methacrylate (PMMA) emulsion and the solids content. The treatment of independent variables is H2O2 by 1%, 2%, 3%, and 4%. This research procedure was carried out in a two-stage process. The first stage is the process to separate the inhibitor contained in the methyl methacrylate monomer by adding a 1 N concentration of NaOH solution, stirring until evenly distributed, then put into a separating funnel to separate pure methyl methacrylate from the inhibitor dissolved in alkaline solution. The second stage is the polymerization reaction process using the emulsion method. Dissolve the poly vinyl alcohol in hot water at 70oC, put the poly vinyl alcohol solution into a three-neck flask, which is equipped with a stirrer and a hot plate heater, add the initiator and up to 60oC, then add the methyl methacrylate monomer with stirring and heating at room temperature 100oC for 1 hour. The resulting product is a thick poly methyl methacrylate emulsion, has a milky white color, and has good adhesion. Furthermore, the viscosity test was carried out using ford cup number 4 and the solids content test. The results showed that the more initiators added, the higher the viscosity and the higher the solids content.
The Health Protocol in the Probolinggo District's Tourism Development

The goal of this study is to describe and analyze the application of health protocols to the growth of the tourism sector as the implementation of Probolinggo Regent Regulation Number 62 of 2020 in the region of the Bromo Tengger Semeru National Park, Sukapura District, and Probolinggo Regency; to describe and analyze the motivating and impeding factors for the application of health protocols in the tourism sector as the implementation of Probolinggo Regent Regulation. The primary informants and other informants in this qualitative study. Key informants are knowledgeable about and have access to various fundamental data. The key informants are the managers and stakeholders in the tourism industry who are directly involved in the investigated social interactions. According to the study, following health standards and providing cleaning supplies are two ways regulations are implemented. Implementing health protocols, however, will not be able to return the Bromo Tengger Semeru National Park region's economic situation to what it was prior to the Covid-19 outbreak. Business players' responses to interviews revealed that since the Covid-19 Pandemic, income has fallen.
Explicit Risk Management in Agile Software Projects: Its Relevance and Benefits

Risk management is the systematic process of controlling risks and it is critical to the success of all software development projects. Agile software development methodologies by the inbuilt features utilized, control risks. However, this does not work for all cases of software development projects and supplementary means may need to be applied. This can be addressed by introducing an intentional and formal way of managing risks in the agile environment. Explicit risk management promises numerous benefits if properly implemented.&#x0D; This study intends to review and deduce based on the risks identified, the relevance and benefits of formally managing risks in agile software development projects. To achieve the aim of this study, the researcher reviewed risk management procedures in a typical agile setting as well as research that exposed the insufficiency of the inherent risk management process in agile projects and the identified risks. Related research papers from peer-reviewed journals and other reliable sources were reviewed to extract risks that occurred using agile without explicit risks management. The study inferred that some risks do exist that occurred with the introduction and use of the agile method itself. Also, there could be risks that surface when the project size exceeds a limit. Thus, managing risks explicitly will go a long way to address such risks. Consequently, the researcher was able to deduce the relevance and benefits of implementing explicit risk management in an agile software development project.&#x0D; This study showed that it is beneficial to incorporate formal risk management procedures in agile software development when mega software projects are being developed. However, to maintain the agility of the agile methods which happens to be a major benefit of the utilization of agile methods, more research is needed to further explore explicit risk management in the agile environment without violating the swiftness in the agile settings.
GUI R UNTUK ANALISIS KERANJANG BELANJA DENGAN ALGORITMA APRIORI PADA SUATU PERUSAHAAN E-COMMERCE

Technological developments help people live easier. One of the technological developments is being able to trade digitally or it can be called e-commerce. To increase revenue, e-commerce companies collect consumer sales history data that can be analyzed and obtain information about consumer habits. One of the analyzes that can be used is shopping basket analysis which aims to find a pattern in transaction data. In data processing and analysis is done using the R program computation and GUI R is made with a recommendation system simulation. The results of the shopping cart analysis produce as many as 22 rules using a minimum support of 0.06 and a confidence of 0.5. The greater the support value, the more often the product or rule is purchased by consumers from all data transactions and vice versa. Meanwhile, the greater the trust value, the more often the products purchased under the regulation are purchased together. Thus, the information can be used to help carry out promotions to increase sales by the company.
Selfish Mining in Ethereum

We study selfish mining in Ethereum. The problem is combinatorially more complex than in Bitcoin because of major differences in the reward system and a different difficulty adjustment formula. Equivalent strategies in Bitcoin do have different profitabilities in Ethereum. The attacker can either broadcast his fork one block by one, or keep them secret as long as possible and publish them all at once at the end of an attack cycle. The first strategy is damaging for substantial hashrates, and we show that the second strategy is even worse. This confirms what we already proved for Bitcoin: Selfish mining is most of all an attack on the difficulty adjustment formula. We show that the current reward for signaling uncle blocks is a weak incentive for the attacker to signal blocks. We compute the profitabilities of different strategies and find out that for a large parameter space values, strategies that do not signal blocks are the best ones. We compute closed-form formulas for the apparent hashrates for these strategies and compare them. We use a direct combinatorics analysis with Dyck words to find these closed-form formulas.
Algebraic Effects for Extensible Dynamic Semantics

Abstract Research in dynamic semantics has made strides by studying various aspects of discourse in terms of computational effect systems, for example, monads (Shan, 2002; Charlow, 2014), Barker and 2014), (Maršik, 2016). We provide a system, based on graded monads, that synthesizes insights from these programs by formalizing individual discourse phenomena in terms of separate effects, or grades. Included are effects for introducing and retrieving discourse referents, non-determinism for indefiniteness, and generalized quantifier meanings. We formalize the behavior of individual effects, as well as the interactions between effects, in terms of algebraic laws tailored to the relevant discourse phenomena. The system we propose is thus modular and suggests a novel approach to integrating formal accounts of distinct semantic phenomena. Finally, we give an interpretation of the system into pure $$ \lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>λ</mml:mi> </mml:math> -calculus that respects the laws. Future work will aim to integrate more discourse phenomena using the same methodology, for example, presupposition and conventional implicature.
Bitcoin Price Prediction Using Machine Learning Techniques

This paper discusses, trying to accurately assess the price of Bitcoin by looking at different parameters affects the value of Bitcoin. In our work, we focus on understanding and seeing the evolution of Bitcoin daily market, a1 and gaining intuition in the most relevant aspects surrounding the Bitcoin price. In the meantime, market capitalization of publicly traded cryptocurrencies exceeds $ 230 billion. The most important cryptocurrency, Bitcoin, is used primarily as a digital value store, and its pricing opportunities have been extensively considered. These features are described in more detail in the following paragraph: details of the main Bitcoin, as described in the paper. Bitcoin is the most expensive digital currency in the market. However, Bitcoin prices have been highly volatile, making it difficult to forecast. As a result, the goal of this research is to find the most efficient and accurate model for predicting Bitcoin prices using various machine learning algorithms. Several regression models with scikit-learn and Keras libraries were tested using 1-minute interval trading data from the Bitcoin exchange website bit stamp from January 1. 2012 to January 8, 2018. The best results showed a Mean Squared Error (MSE) as low as 0.00002 and an R- Square (R2) as high as 99.2 percent.
Decision Support System of Determining E-Commerce With The Topsis and Comparation With Ahp for UMKM In the Tuban Regency Area

E-Commerce has had a major impact on social and economic growth in society. However, E-Commerce system is not always profitable for UMKM actors themselves, there are several factors that cause UMKM quality assessments to decline from tight competitiveness. Many consumers consider buying products from the many existing E-Commerce systems. For this reason, this goal is to implement a decision support system in determining E-Commerce using the TOPSIS method with AHP method. Of course, in implementing a decision support system, it is necessary to have assessment criteria and a number of alternatives that will be used as a reference to determine E-Commerce for UMKM by calculating the TOPSIS method which will produce the final result of the value of each E-Commerce in the form of ranking. From research conducted on 38 UMKM in Tuban Regency, the results of E-Commerce Shopee are 60.5%, Tokopedia 15.8%, Blibli 10.5%, Lazada 7.9% and Bukalapak 5.3%. The data will be grouped from each alternative to find the average value based on predetermined criteria and calculated using the TOPSIS method AHP method, it can be concluded that the E-Commerce that is widely used by UMKM in the Tuban Regency area is Shopee &gt; Tokopedia &gt; Bukalapak &gt; Lazada &gt; Blibli. while with AHP resulted in the order of Tokopedia &gt; Shopee &gt; Bukalapak &gt; Blibli &gt; Lazada.
Lexical sophistication measures and writing proficiency: The case of Indonesian learners of Japanese

The present study tests two measures of lexical sophistication in writing proficiency (moving-average morphological richness and moving-average mean size of paradigm for testing the lexical diversity) and mean word length for testing writing form (plain, humble, honorification). The findings suggest that the three metrics work reliably. Regarding lexical diversity, moving-average of morphological richness (MAMR) and moving-average mean size of paradigm (MAMSP) of Indonesian Japanese learners-written texts are close to native Japanese-written data. Lexical complexity measured by word length by Indonesian Japanese learners is characterised by slightly less richness than native Japanese data but remains very close. Word length-frequency relationship in the Indonesian-written data presents outstanding fitting results to nine models, including the Poisson Model families and Binomial Model families, with 0.9918 as the lowest and 0.9987 as the highest determination coefficient R2. It is hoped that this study’s outcome may help develop an automatic evaluation of the writing proficiency of agglutinative languages with diverse writing forms.
The Role of Pedagogical Documentation in Early Childhood Education in Indonesia

Considering the significance of pedagogical documentation in the early childhood education/care system, the current study examines the influence of pedagogical documentation on children's semantic memory, observed activities and evaluated skills and qualities per their teachers and caretakers. This study also examines the interactive effect of the learning environment with pedagogical documentation to enhance children's semantic memory, observed activities, and evaluated skills and qualities. A mixed-method approach has been utilized to examine the proposed associations. Learning events were organized for children aged 36 to 72 months in the kindergartens and preschools in Mataram, Indonesia. After four weeks of observations, the teachers and caretakers of the children recorded their responses regarding children's semantic memory, observed activities, skills, and qualities. The data were analyzed using SmartPLS 3.0 software. The results showed a significant increase in children's semantic memory, evaluated skills and qualities, and a positive change in their routine activities. Simultaneously, results also presented the significant role of the learning environment in enhancing positive outcomes of the pedagogical documentation in the early childhood education/care system.
Analysis and Modeling of Cancer Data Numerically by Matlab

Matlab develop the math works stands for matrix laboratory.It is a software package used to perform scientific computation and visualization. It capability for analysis of various scientific problems, flexibility and powerful graphic makes it a very useful software package. The aims of this paper is to analyze and modeling the Cancer data numerically and by Matlab . We followed the applied mathematical method using Matlab. And we found the following some results: Sport modeling helps create a cancer simulator, the Tallabic is a model for a disease vector and has helped many in medicine and other fields. Cancer is one of the most dangerous diseases in the world, with the highest death rate due to this disease, and it has many types, including colon, breast and oral cancer. There are different ways to treat cancer, including chemotherapy, removing some parts of the body and x-rays. And the most vulnerable to cancer are women. Mathematical modeling has contributed to determining the growth of cancerous tumors and following-up treatment methods, to reduce the spread of the disease and contribute to the treatment of serious cancer