Reviewerly/exHarmony huggingface dataset:
Gold Reselling in Online Transaction: A Study from Muamalat Perspective

Gold is promoted largely as purchasing and selling physical valuable item in Malaysia.It is importantly become a saving tools as well as for investment purpose to the society.Henceforth, many of gold's company such as Public Gold, Dinar Emas Kelantan, and others grab this kind of opportunity to offers the valuable gold item in order to attract the customer's intentness.One of the companies' marketing strategy is by issuing gold special edition such as Black Panther Wakanda Forever, Covid-19 Special Edition, and Dinar Emas Legasi TG Nik Aziz, Appreciation to Malaysia 8 th Prime Minister and others.This special offer initially caught an attention among gold reseller or collector since it may able to raise a mark-up profit by reselling this gold bar to the buyer.Besides, the lock price system also initiated in gold transaction among reseller and buyer involved the divergent among Islamic scholar poit of view.Henceforth, this article aims to explain legal consequences in reselling gold from Muamalat Perspective.This article employed inductive and deductive method by understanding from Quran, hadith, as well as Islamic school of thought as references.It is expected to contribute in exploring the concept of reselling gold transaction from Islamic law transaction which is clearly aims to avoid the element of gharar, and riba.This article suggest the policy maker such as National Fatwa Council or Shariah Advisory Council (SAC) Bank Negara Malaysia to develop a guideline or parameter regarding gold reselling standard.
Deteksi COVID-19 Berdasarkan Hasil Rontgen Dada (Chest Xray) Menggunakan Python

Chest X-ray have an important function in the three areas of healthcare, namely diagnosis, treatment and re-examination. Studies from China suggest Chest Radiographs (X-Ray) and Chest Computed Comography (CT) scans can help diagnose COVID-19. Therefore, chest x-rays (x-rays) and chest computed tomography (CT) are appropriate methods for lung infections due to COVID-19. Based on this, the authors tried to make a model for the classification of digital images of Chest X-Ray results with the labels of Normal, Pneumonia, Tuberculosis (TBC), and COVID-19. Through the resulting model, the best model to use will be compared.&#x0D; The method used to create this model is through training and testing the dataset using the Convolutional Neural Network (CNN) architectural model, namely VGG19, ResNet50, and InceptionV3. The number of images used is 1,000 Chest X-Ray images. The dataset is divided into training and validation datasets in several ratios of 20% : 80%, 50%: 50%, and 80%: 20%. While testing uses 10% from train datset chest x-ray images as a confusion matrix dataset and 4 chest x-ray images as a prediction dataset.&#x0D; From the results of the research that has been done, the best model is VGG19 at 41 of 61 epoch and a ratio of 20% : 80%. Where the VGG19 model produces 94.44% for accuracy and 0,1084 loss value for training. Whereas at the testing stage with a configuration matrix, 95% accuracy value was obtained. Then for testing the new data prediction produces the best accuracy with 98.97% accuracy for the Normal label, 99.16% for COVID-19, 99.56% for Pneumonia, and 99.79% for Tuberculosis (TBC).
Desenvolvimento de competências emocionais dos profissionais de saúde em contexto de uma Unidade de Cuidados Coronários

Complex work environments, such as the context in which health professionals operate, include an articulation of knowledge and skills, and it is true that a strong demand for the construction of emotional skills also implies a greater use of knowledge. It means that, in addition to the need for each professional to have technical knowledge in specialised domains, they also need to bring together a set of essential personal and interpersonal skills. In this sense, emotional competencies are positively associated with a set of personal characteristics that promote the use of good professional practices and serve as a protective factor for the well-being of individuals. This research aims to study the impact of a psychoeducational and training intervention on the development of emotional competencies of health care professionals working in a coronary care unit. The choice of this type of care unit is justified by the priority and permanent dimension of intervention in the health area required by chronic heart disease.&nbsp; To answer this objective, a mixed-methods research methodology was used. Data were collected from six health professionals, using the focus group method and questionnaires validated for the sample population. The combined analysis of qualitative and quantitative data allowed for a better understanding of reality and a more rigorous analysis of the intervention with health professionals. The participants' speeches revealed that the psychoeducational and formative intervention allowed for the introspection and reflection of feelings regarding the procedures implemented by these professionals for cardiac patients in a period of pandemic caused by SARS-COV2. The continuity of training actions for the mastery of emotional skills is highlighted by all professionals, allowing a more effective confrontation of a complex reality.
Sparsity-based evolutionary multi-objective feature selection for multi-label classification

Feature selection (FS) is typically an essential pre-processing step for many machine learning tasks. However, most existing FS approaches focus on single-label classification, whereas multi-label classification (MLC) is an emerging topic where each instance can be assigned to more than one class label. Sparsity-based FS is an efficient and effective method that can be applied to MLC. However, existing sparsity-based approaches based on gradient descent can get trapped at local optima, and cannot optimise the selected number of features and classification performance simultaneously that are often in conflict, thus making FS a multi-objective problem. Evolutionary multi-objective optimisation (EMO) can be applied to FS due to its potential global search ability. EMO-based algorithms have not been utilised in sparsity-based approaches for FS in MLC. This paper proposes a novel sparsity-based MLC FS method based on Multi-objective Evolutionary Algorithm with Decomposition (MOEA/D). The experimental results indicate improvement in the MLC performance in comparison to a existing multi-objective FS algorithms.
Genetic programming with a new representation and a new mutation operator for image classification

Due to the high dimensionality and variations of the image data, it is challenging to develop an image classification method that is able to capture useful information from images and then conduct classification effectively. This paper proposes a new GP approach to image classification, which can perform feature extraction, feature construction, and classification simultaneously. The new approach can extract and construct multiple informative features to effectively handle image variations. Furthermore, a new mutation operator is developed to dynamically adjust the size of the evolved GP programs. The experimental results show that the proposed approach achieves significantly better or similar performance than/to the baseline methods on two datasets.
Environmental Friendly Supply Chain Management - A Perception Analysis

Accounting is a media which communicates the business and financial information of each and every organisation so as to cater to the needs of various interested parties. Business involves undertaking a series of activities, which may be financial or nonfinancial, to achieve its prime goal. Irrespective of the nature of the business activity, it is important to note that all the information in relation to the activities undertaken by them must be communicated so as to ensure the transparency and accountability towards its stakeholders. To achieve the quantitative and qualitative communication of business and financial information, it is necessary to follow standardized, comprehensive, and unified accounting standards and guidelines. One such standard is International Financial Reporting Standards. The present study is intended to analyse the perception of users of financial statement from financial analysts and investors. Based on the primary data, it is found that users of financial statements are having positive perception towards harmonisation of accounting standards and they opined that it enhances the quality of financial reporting.
Evolving local interpretable model-agnostic explanations for deep neural networks in image classification

For deep convolutional neural networks (deep CNNs), a severe drawback is the poor interpretability. To address this drawback, this paper proposes a novel genetic algorithm-based method for the first time to automatically evolve local interpretable explanations that can assist users to decide whether to trust the predictions of deep CNNs. In the experiments, the results show that the evolved explanations can explain the predictions of deep CNNs on images by successfully capturing meaningful interpretable features.
A transfer learning based evolutionary deep learning framework to evolve convolutional neural networks

The manual design of CNNs has become exceptionally complex due to the more sophisticated CNN architectures. Thankfully, more and more researchers endeavour to mitigate the difficulty of manual design by designing automated process, but the computational cost of the automatic methods is extremely high due to the huge search space. In this paper, an evolutionary deep learning framework based on transfer learning is proposed to reduce the computational cost, while maintaining the classification at a competitive level. The main idea is to evolve a CNN block from smaller datasets, and then increasing the capacities of the evolved block to handle larger datasets. The proposed method obtains good CNNs with less than 40 GPU-hours. It also achieves a promising error rate of 3.46% on the CIFAR-10 dataset.
The Teaching of English by Using Telegram Application: A Case Study at SMKN (Vocational High School) 4 Malang

This study aims to describe the implementation of Telegram application as a teaching media in teaching English and to know students’ perceptions toward this implementation. Using case study as research design, the teacher who used Telegram as teaching media in English teaching and 2 students who are taught by this teaching media were being participants in this qualitative study. The observation checklist form adapted from Lubis (2018) and the interview guidelines adapted from Chorida (2017) was used to gain the data. To reach the objective of the study, the researcher conducted online observation and interviews with those participants. The finding shows that the implementation of Telegram at SMKN 4 Malang can give positive impact in English language teaching. The use of Telegram facilitates students to autonomously learn. It is due to all of the materials needed by students are already provided with clear instructions by the English teacher in one of its features called Bot API. Related to the student responses in using Telegram application, students are pretty attractive to use this Telegram application as the teaching media. Therefore, it is suggested to English teachers to implement Telegram application as one of teaching media in delivering materials.
PROGRAM PENGABDIAN MASYARAKAT MELALUI WORKSHOP ANALISIS DATA PENELITIAN BIDANG PENDIDIKAN MENGGUNAKAN SOFTWARE SPSS

Education research data analysis workshop using SPSS software is one form of higher education Tridharma that must be implemented by lecturers, namely community service. The purpose of this workshop is to provide insight and knowledge about data processing using statistics assisted by SPSS software, especially among teachers of TK Muslimat NU 29 Mahkota Gresik. This workshop was attended by 21 participants from TK Muslimat NU 29 Mahkota Gresik. The workshop was held for 1 day starting at 08.00 WIB - 16.00 WIB, on 27 January 2019 to be precise. The material provided at this workshop included descriptive statistics, data distribution measures, data centering measures, hypothesis testing and correlation analysis. The feedback from this workshop was to provide training in analyzing data using SPSS software. The final result of this workshop participants were able to analyze data using SPSS software.
A Comparative Study of Linguistic and Computational Features Based on a Machine Learning for Arabic Anaphora Resolution

Anaphora resolution is one of the problems in natural language processing. It is the process of disambiguating the antecedent of a referring expression from the set of entities in a discourse. The correct interpretation of pronouns plays an important role in the construction of meaning. Thus, the resolution of pronominal anaphors remains a very important task for many natural language processing applications. Additionally, it plays an increasingly significant role in computational linguistics. However, a significant amount of work on anaphora resolution is focused on English; anaphora resolution for other languages, including Arabic, is still limited. In this paper, we present a new set of computational and linguistic features to resolve Arabic anaphors using a machine learning approach. In this paper, an in-depth study was conducted on a set of computational and linguistic features to exploit their effectiveness and investigate their effect on anaphora resolution. The aim was to efficiently integrate different feature sets and classification algorithms to synthesize a more accurate classification procedure. Four well-known machine learning algorithms k-nearest neighbor, maximum entropy, decision tree and meta-classifier, were employed as base-classifiers for each of the feature sets. A wide range of comparative experiments on Quran datasets was conducted, the discussion presented, and conclusions were drawn. The experimental results show that our approach gives satisfactory results.
Classification of colposcopic images using a multi-breakpoints discretization approach on temporal patterns

Cervical cancer represents the fourth cause of death in women worldwide. One of the efforts to decrease this mortality has focused on implementing automatic tools for supporting the experts in diagnosing this illness. In this work, eMODiTS was implemented to explore its performance in this particular domain. A comparison among the most used symbolic discretization (EP, SAX, αSAX, ESAX, ESAXKMeans, 1D-SAX, rSAX, and TD4C) methods and the well-known machine learning algorithms (kNN with k = 1,5,7, SVM, J48, and MLP) was done to analyze the eMODiTS performance. Results suggest that eMODiST outperforms most of the symbolic discretization methods concerning the statistical test results; only the results of the 1D-SAX algorithm do not present a significant statistical difference compared with eMODiTS. Although our proposal results are comparable with 1D-SAX, eMODiTS achieves these results with a more compressed data version. Concerning the comparison of machine learning-based methods, MLP outperforms eMODiTS. However, the classification performance and the ability to detect truly healthy and non-diseased cases are not sacrificed by eMODiTS. Moreover, the colposcopist validated the classification results confirming that the algorithm matched the real valorization. Finally, eMODiTS provides a visual tool for making decisions by experts about cervical cancer detection in the early stages.
Sales Information System Using Web for Small Business (Case Study: CV. Tanaka Service)

Along with the development of business based on web which is currently have used information Technology. The efficiency of the business in need is one reason why many small businesses that implement information technology. It plays a central role that is essential for the development of small business in theof era the times today, so the small business should have the ability quickly to adapt to changes that occur. The purpose of this study was to create a media sale clear and easily accessible by the consumer as well as easy to be managed by the small business (CV. Tanaka Service). To that end, it was the design of information systems do web-based sales. This study will describe the activities and the products produced at each stage of the web design for CV. Tenaka Service. The design of this information system is limited only to the extent of information systems sales and the sales transaction database. At the final stage of the design, be the implementation of the web-based information system that is designed.
Challenges in teaching English writing skills: Lessons learnt from Indonesian high school English language teachers

Writing skill, among the four target language skills, is one of the skills that language learners need to master. In teaching this skill, therefore, teachers should take any effort to help learners write using the language they learn correctly. However, in the context of teaching English in Indonesia, particularly the writing skill, teachers often face challenges. The study reported in this article seeks to identify the challenges experienced by several Indonesian English language teachers in teaching English writing skills at VHS, a vocational high school, in Gorontalo, Indonesia. The participants involved were all English language teachers working at the school. Data were collected using interview and observation, and analyzed by using a thematic analysis method. The findings showed that there were four themes describing the teachers’ challenges in teaching English writing skills. They were teaching technique-related challenges, teaching time-related challenges, linguistics-related challenges, and learning motivation-related challenges. Even though the teachers are able to cope with the challenges, they still need to explore more strategies in teaching, thus the teaching and learning process can run effectively.
INFORMAL EDUCATION AS AN EFFECTIVE FORM OF DEVELOPMENT OF PROFESSIONAL COMPETENCE OF MODERN COMPUTER SCIENCE TEACHER

In modern conditions formal education is unable to meet the various educational needs of teachers, especially when it comes to ICTs and computer science (CS), so informal adult education is becoming an increasingly urgent personal need of modern educators. Taking into consideration the challenges of today and the demands to the teachers, the state has provided for the possibility of continuous teacher training in the new Law on Education. Despite the fact that the issue of professional development of educators was given considerable attention in the researches of community, the problem of professional competence of modern CS teachers in the field of informal education remained insufficiently covered and was not the subject of a dedicated special research. The aim of the article is to outline the optimal ways to develop the professional competence of computer science teachers in the system of informal education. Theoretical research methods were used during research, namely: study and analysis of psychological-pedagogical, normative and special literature on the above-mentioned issue(s); analysis of state educational standards, programs, textbooks and teaching materials. Some its aspects are defined and the ways of organizing the process of development of professional competence of teachers in the conditions of informal education are described. It is determined that further research is aimed at processing and testing the research-based materials in real conditions. The analysis of the legal framework in the field of education showed that the state is currently taking a number of measures to ensure the continuous development of professional competence of modern CS teachers as well as the development of professional competence of modern educators in general, which should include such areas that will allow teachers to exploit new approaches and educational technology that can improve and amplify the learning process of students.
Blockchain adoption in tourism:Grounded theory-based conceptual model

This study aims to move forward a blockchain tourism research and add understanding regarding blockchain adoption in the tourism industry. Grounded theory method based literature review was applied as a research methodology. Based on the literature review, the authors developed a conceptual model of blockchain adoption in tourism by defining casual, context, intervening conditions that affect blockchain adoption and outlining the consequences of blockchain adoption on the tourism sector. This research will contribute to the blockchain adoption literature as well as will give answers to the industry practitioners on how this innovative technology can be adopted and whether it worth investments or not.
Experiments with adversarial attacks on text genres

Neural models based on pre-trained transformers, such as BERT or XLM-RoBERTa, demonstrate SOTA results in many NLP tasks, including non-topical classification, such as genre identification. However, often these approaches exhibit low reliability to minor alterations of the test texts. One of the problems concerns topical biases in the training corpus, for example, the prevalence of words on a specific topic in a specific genre can trick the genre classifier to recognise any text on this topic in this genre. In order to mitigate this problem, we investigate techniques for attacking genre classifiers to understand the limitations of the transformer models and to improve their performance. While simple text attacks, such as those based on word replacement using keywords extracted by tfidf, are not capable of deceiving powerful models like XLM-RoBERTa, we show that embedding-based algorithms which can replace some of the most “significant” words with words similar to them, for example, TextFooler, have the ability to influence model predictions in a significant proportion of cases.
Study on the Random Factor of Firefly Algorithm

The firefly algorithm (FA) is a swarm intelligence algorithm that mimics the swarm behaviour of the firefly in nature. The idea is simple, and FA is easy to realize. To improve its performance, a new method to control the random factor in FA is proposed in this paper, based on the design idea and mathematical model of FA and a simple experiment. Under the new method, the value of the random factor decreases according to a geometric progression sequence. Twenty common ratios of geometric progression sequences are used to optimize nine standard benchmark functions. The experimental results are analysed by the ANOVA and step-up methods. The analysis shows that the performance of FA improves under the new method to control the random factor.