Reviewerly/exHarmony huggingface dataset:
Difficulties in Translating Kurdish Sport Collocations into English by Kurdish Translators

Abstract Collocations are found in all fields of life and all languages. When it comes to translating collocations between two languages, certain difficulties arise. These difficulties could be due to various factors such as cultural, structural and/or linguistic differences. This paper aims at identifying the difficulties encountered by Kurdish translators when they attempt to translate Kurdish sports collocations into English. It also tries to explain the underlying factors of such difficulties and how to overcome them. A sample of 100 Kurdish sport collocations was collected from five Kurdish websites and the researchers have added Kurdish sentences containing each of the collocations. Then, the sentences were selected based on the most relevant sports collocations and were sent out to Kurdish translators across the Kurdistan region to be translated into English. The lists were sent to the translators through Google form links and personal emails. The translated sentences were analysed; the difficulties were identified and discussed to arrive at the underlying factors. The results show that the Kurdish collocations in question are the most challenging for translators because they are culture-bound. The other types are also difficult to translate to varying degrees. The most practical strategy to deal with translating collocations is semantic translation adoption. Keywords: Kurdish Sport Collocation, Translation, English Collocation
Credit Card Fraud Detection Using Machine Learning Techniques

There are many types of fraud in our daily life. One of the frauds occurring these days is credit card fraud. When people around the globe make credit card transactions, there will also be fraudulent transactions. To avoid credit card fraud, we must know the patterns and how the fraud values differ. This paper proposed credit card fraud detection using machine learning based on the labeled data and differentiating the fraudulent and legitimate transactions. The experiment was conducted using supervised machine-learning techniques.
Machine Learning for Complex Data Analysis: Overview and a Discussion of a New Reinforcement-Learning Strategy

Today, Big data analysis are becoming a fundamental process for many organizations to improve their operations. Due to the specific nature of Big Data, traditional data management, storage and analysis tools cannot guarantee a perfect data analysis process. Therefore, it is necessary to use advanced techniques to ensure effective analysis of big data. In recent years, several research works have addressed this type of problem, based on the use of learning. In this article, we provide an overview of related works describing the application of machine learning in big data analysis in three specific domains (healthcare, transportation and agriculture). Next, we present our new complex data analysis strategy.
Research on the problem of aggregation of multiple rules in fuzzy inference systems

In this paper, we establish the matching relation between implication operator and aggregation operator, which provides a new solution for the design and construction of multi-rule fuzzy inference system. Firstly, according to the definition and monotonicity of implication operator, a new classification method of implication operator is proposed, and then the fuzzy inference process using different implication operators is classified. Then, dynamic maximum aggregation operator and dynamic minimum aggregation operator are proposed. Based on the compositional rule of inference (CRI) method, a matching method and basis of implication operator and aggregation operator for fuzzy inference systems is given and illustrated with examples. Finally, the applicability of the proposed method in this paper is further illustrated by comparing the method with existing methods in the literature and using the nearness degree as an evaluation index.
Development of Contextual Physics Teaching Materials Assisted by Virtual Lab Based-Android as Alternative Learning in Covid-19 Pandemic

This study aims to develop effective and efficient contextual physics teaching materials for students and teachers in the Covid-19 pandemic. The teaching materials are based on virtual lab-based Android so that they can be used in any conditions, including when applying physical distancing protocols at school or study from home (SFH). The teaching materials in the form of this application makes it easy for students to access the content on their mobile phones even when they cannot connect to the internet. Also, this application is very easy for teachers to be used. The research method used is based on the 4D model, that is define, design, develop and disseminate. The teaching materials that have been developed passed the first stage test with a Very Good result. The validity test is assessed by expert validators. The subject material used in this study is limited to Reflection and Refraction of Light. The response of students using the teaching materials is Good. According to the students, the teaching materials are easy to use, motivate learning, and deem effective in its implementation
Quantum dynamics of a driven parametric oscillator in a Kerr medium

In this paper, we first analyze a parametric oscillator with both mass and frequency time-dependent. We show that the evolution operator can be obtained from the evolution operator of another parametric oscillator with a constant mass and time-dependent frequency followed by a time transformation $t\rightarrow\int_0^t dt'\,1/m(t')$. Then we proceed by investigating the quantum dynamics of a parametric oscillator with unit mass and time-dependent frequency in a Kerr medium under the influence of a time-dependent force along the motion of the oscillator. The quantum dynamics of the time-dependent oscillator is analyzed from both analytical and numerical points of view in two main regimes: (i) small Kerr parameter $\chi$, and (ii) small confinement parameter $k$. In the following, to investigate the characteristics and statistical properties of the generated states, we calculate the autocorrelation function, the Mandel $Q$ parameter, and the Husimi $Q$-function.
PENGARUH PEMBELAJARAN DARI RUMAH TERHADAP MOTIVASI DAN HASIL BELAJAR PESERTA DIDIK

The purpose of this study was to determine and analyze the effect of learning from home on learning motivation and to know and analyze the effect of learning from home on learning outcomes at SMK Negeri 2 Kayuagung. In this study using primary data and secondary data, primary data were obtained by distributing questionnaires to 46 students as sample respondents. The quatitative reseach is used. Simple resression was used to analyse the data by using SPSS 22 version in model development and hypothesis testing. From the results of data analysis of the variables studied, namely the teacher competency variable and the learning strategy variable showed that learning from home at SMK Negeri 2 kayuagung was affective, and there was a significant relationship to the learning motivation but there was no significant to the learning outcomes.
Remarks on barriers to Open Science for ECRs

Yesterday was the first of four listening sessions by the White House's Office of Science and Technology Policy.
Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning

Nowadays, the advancement of drones is also factored in the development of a world surrounded by technologies. One of the aspects emphasized here is the difficulty of controlling the drone, and the system developed is still under full control by the users as well. Reinforcement Learning is used to enable the system to operate automatically, thus drone will learn the next movement based on the interaction between the agent and the environment. Through this study, Q-Learning and State-Action-Reward-State-Action (SARSA) are used in this study and the comparison of results involving both the performance and effectiveness of the system based on the simulation of both methods can be seen through the analysis. A comparison of both Q-learning and State-Action-Reward-State-Action (SARSA) based systems in autonomous drone application was performed for evaluation in this study. According to this simulation process is shows that Q-Learning is a better performance and effective to train the system to achieve desire compared with SARSA algorithm for drone controller.
Arbitrary application of arbitrary rules

When there are rules, who decides what they are intended for and how they are applied?
A Low-Rank Learning-Based Multi-Label Security Solution for Industry 5.0 Consumers Using Machine Learning Classifiers

The need for networking in smart industries known as Industry 5.0 has grown critical, and it is especially important for the security and privacy of the applications. To counter threats to important consumers devices’ sensitive data, various applications of smart industries require intelligent schemes and architectures. The data which is recorded and stored is vulnerable to security breaches. These attacks, though, can be recognized using machine-learning approaches, which necessitate the construction of a new dataset. The following paper uses a hybrid intrusion dataset which is used to solve multi-label classification problems using a multi-criteria decision-making process for consumer devices. The use of two datasets having the same attack from two different classes is difficult to recognize the class of attack. Our proposed model is going to recognize the type of attack from the two classes by combining the machine learning classifiers with the multiple criteria decision-making process and validated over existing state of art techniques.
E-Culture as a Source for Learning History and Culture for Students of the History Education Department Mulawarman University, Samarinda

In today's modern era all learning refers to digital-based things, both in terms of sources and media as well. It is also inseparable from learning in schools and tertiary institutions, in practice using digital-based media in accordance with current technological developments. The study, entitled E-Culture as a Source for Learning History and Culture for the Cultural History Course for Students of the Unmul History Education Department, uses a qualitative research method with data obtained from observations, questionnaires and interviews. E-culture is one of the applications available on digital platforms and can be used as a instructional media for students of history and culture. It is also inseparable from students of the Teacher Training and Education Faculty of history education Department at Mulawarman University. They use this application as a learning medium in history and culture courses. In its use, e-culture tends to contain the history and local culture of the people of Kalimantan, the focus is on the Dayak community as the largest tribe in Kalimantan. Students can take advantage of this application to increase their knowledge and be relevant to existing courses. Access to the application is also very easy and free, so students can make the most of it. Like applications that are developing today, e-culture also has drawbacks and is always developing in updates related to data and information. The advantage is that apart from being easy to use as a learning medium, there are also interactive tools that receive input and suggestions from users in terms of updating the latest information and data.&#x0D; Keywords: e-culture, learning history, history education, mulawarman university
Library interfaces, knowledge graphs, and Miller columns

Some quick notes on interface ideas for digital libraries and/or knowledge graphs. Recently there’s been something of an explosion in bibliographic tools to explore the literature.
Data Privacy Best Practices of A Local Higher Educational Institution: A Model for Governance

The passage of the Data Privacy Act of 2012, establishes a new data privacy framework for all industries and sectors, whether in public or private. Different agencies’ policies have aimed to foster innovation and the development of commercial and public sector technical skills. The hit COVID-19 pandemic pushed academic institutions for a paradigm shift in the delivery of instructional and administrative services in online platforms. This study assessed Laguna State Polytechnic University’s (LSPU) data privacy policies and governance transformation to determine its best practices and establish an informational privacy framework. LSPU commenced its full compliance in 2017, hence the researcher chose the respondents whose employment at the university is between 2017 and 2021 to ensure they have sufficient experience and skill in the implementation of the Data Privacy Act of 2012 and its rules and regulation in the university. Questionnaires were utilized to collect information, and interviews with unit heads and university staff validated the results. Based on the study’s hypothesis, it is determined that the governance of existing data privacy policies at the institution is completely established. Since the results of the analysis revealed a significant correlation, the respondents strongly support a very high degree of data privacy implementation oversight. Further, using the path-analysis technique, it was found that governance has a positive effect on the current practices of data privacy in the university. Finally, the study revealed that when there is a certain policy change responsive to the data privacy law that is to be implemented in the university, it is expected that practices in the implementation of data privacy law will show significant change. Keywords: Data Governance, Data Practices, Informational Governance, Data Privacy Model
Translating FOL-theories into SROIQ-Tboxes

Logical languages provide rigid formalisms for theories with varying expressive and scalable powers. In ontology engineering, it is popular to to provide a two-folded formalization of a theory; an expressive FOL formalization, and a decidable SROIQ fragment. Such a task requires a systematic and principled translation of the set of FOL formulas to achieve a maximally expressive decidable fragment. While no principled work exists for providing guidelines for the translation of FOL theories into SROIQ knowledge bases, this paper contributes with such a translation procedure.
Creating a Community

Here, we consider the interactive and online affordances of the forum by looking at the ways that posters respond to each other’s posts. What kind of language use characterises those which receive numerous responses vs those which receive none at all? We also ask, how do posters reach consensus and, considering the range of posts of people from different backgrounds, how is disagreement around understandings of anxiety negotiated? We code a sample of the forum posts using Egbert et al.’s (2021) coding scheme for functional discourse units. This is based on nine codes which indicate the particular purpose of a stretch of interactive text; for example, joking around, engaging in conflict or giving advice. Additionally, we look at uses of computer-mediated communication, including acronyms such as lol and emojis which are often used for affiliative purposes and disambiguation. This chapter uses corpus-based frequency approaches to identify longer stretches of interaction which are then examined qualitatively.
Dual autoencoder-based framework for image compression and decompression

Recent development in deep learning has shown great possibilities in many computer vision tasks. Image compression is a crown field of computer vision, and deep learning is gradually being used for image compression & decompression tasks. The compression rate of the lossy compression algorithm is higher than lossless compression but the main disadvantage of lossy compression is the loss of data while compressing it. It is observed that a higher compression rate causes higher data loss. Recent advancements in deep learning technique for computer vision like image noise reduction and image super-resolution has shown great possibilities in the area of image enhancement. If these techniques can be utilized to mitigate the impact of a higher compression rate on the output of lossy compression, then the nearly same image quality can be achieved. In this paper, an image compression & decompression framework are proposed. This framework is based on two convolutional neural networks (CNN) based autoencoders. Images and videos are the main sources of unstructured data. Storing and transmitting these unstructured data in the cloud server can be costly and resource-consuming. If a lossy compression can compress the image to store in the cloud server then it can reduce the storage cost and space. We have achieved a 4x compression ratio which means an image will occupy only 25% space compared to the original image. The original image will be retrieved using a joint operation of deconvolution and image enhancement algorithms. The proposed framework receives state-of-the-art performance in terms of Peak Signal-to-Noise ratio (PSNR) and Structure Similarity Index Measure (SSIM) with 4x compression.
Basketball action recognition algorithm based on global context awareness

ognition in simple scenes, the difference between classes of basketball actions is slight, and the backgrounds in the video are very similar. Therefore, it is not easy to recognize the basketball actions directly based on short-term temporal information or the scene information in the video. A Global Context-Aware Network (GCA-Net) for basketball action recognition is proposed to address this problem in this paper. It contains a Multi-Time Scale Aggregation (MTSA) module and a Spatial-Channel Interaction (SCI) module to process multiple types of information on feature layers. The MTSA module uses a temporal pyramid to get contextual links in the temporal dimension through one-dimensional convolution with different dilation rates. The SCI module enhances the feature representation to obtain more prosperous category attributes and spatial information by interacting with information across dimensions. We conducted experiments on the basketball action recognition dataset SpaceJam, and the results show that GCA-Net can effectively classify basketball actions. The average recognition accuracy of ten types of basketball actions in the dataset is 91.54%, which is an improvement compared with the current mainstream methods.
Problem-Solving, Process, and Idea Creation Tools

Problem-solving consists of using generic or ad hoc methods in an orderly manner to find solutions to problems. Some of the problem-solving techniques developed and used in philosophy, artificial intelligence, computer science, engineering, mathematics, medicine, and societies in general are related to mental problem-solving techniques studied in psychology and cognitive sciences (Helmold, 2021). The term problem-solving has a slightly different meaning depending on the discipline. For instance, it is a mental process in psychology and a computerized process in computer science. There are two different types of problems—ill-defined and well-defined—and different approaches are used for each. Well-defined problems have specific end goals and clearly expected solutions, while ill-defined problems do not. Well-defined problems allow for more initial planning than ill-defined problems. Solving problems sometimes involves dealing with pragmatics, the way that context contributes to meaning, and semantics, the interpretation of the problem. The ability to understand what the end goal of the problem is and what rules could be applied represents the key to solving the problem. Sometimes the problem requires abstract thinking or coming up with a creative solution.
The behavior of jump discontinuous function in timbre

The structure of every musical instrument is related to the applied topics of mathematics like logarithms, golden ratio, etc., and this paper practically explores into this topic to know how music is related to the mathematical concepts. It can be realized that our sensitivity to sound is linked to the logic of our brains. Every musical instrument has distinct sounds while playing the same frequency. Many people know that music is continuous in one frequency. But in the quality of the sound, the discontinuity occurs in some musical instruments. We can see how discontinuity occurs in such behavior of the musical waveform in one frequency.
Penerapan Algoritma K-Means dan Decision Tree Dalam Analisis Prestasi Siswa Sekolah Menengah Kejuruan

This study aims to determine how much influence parents' income and the distance from a student's home to school have on student achievement at SMK Tarbiyatul Ulum. Because if you know the factors that influence student achievement, steps or actions can be taken to improve student achievement. The method used in this study includes the use of clustering using K-Means and then classifying it using the Decision Tree method with a total of 157 datasets used as research material. After conducting the classification modeling using the decision tree algorithm, it was found that parents' income did not affect student achievement, but the distance from home to school did affect student achievement. Then at the evaluation stage, the decision tree algorithm is not suitable for use in predicting student achievement, because the accuracy and AUC values of this algorithm are 68% and 0.561, where these values fall into the failure category.
Filter, Map, and Reduce

The last chapter covered some pieces of functional programming that can be hard to wrap your head around, but this chapter will be much simpler. We will look at three general methods used in functional programming instead of loops and instead of explicitly writing recursive functions. They are really three different patterns for computing on sequences, and they come in different flavors with different functions, but just these three let you do almost anything you would otherwise do with loops.
Typomorphic features of placer gold of the Sololi River basin (northeast of the Siberian platform)

Mineralogical features of placer gold and gold-sulfide ore occurrences of the Sololi river basin from the Sololi uplift of the Olenek arch have been studied. According to the typomorphic indicators, two types of gold are distinguished. The first type is gold of a tabular and lumpy shape, often with mineral indentation imprints. The inner structure is represented by granulation, and recrystallization, high-grade intergranular veins, and high-grade shells typical of the gold of intermediate reservoirs. This type of gold is characterized by a high fineness. The sources of this type of gold were gold-bearing intermediate reservoirs of the Permian and Riphean widely developed in the studied area. The second type of gold includes subrounded and poorly rounded gold, ore gold of hooked, tabular, crystalline and angular-lumpy shapes with a pitted-bumpy or flat surface, as well as large growths of gold with quartz. The fineness of this type of gold varies from low to very high. Its primary sources were probably quartz veins genetically related to the metamorphic rocks of the Eekit series and Precambrian rhyolites. Ore occurrence formed by the Late Permian clastic rocks during the processes of the Mesozoic tectonomagmatic activation is considered as an additional source of feeding. Establishment of indicators of low–temperature hydrothermal processes as mineral parageneses, such as mercury gold, cinnabar and barite, suggests shallow and low-temperature formation conditions of this ore occurrence.