Reviewerly/exHarmony huggingface dataset:
Blockchain applications in business and financial activities in Vietnam

The emergence and rapid development of blockchain technology is an important milestone in the development of every country across the world. In Vietnam, blockchain is applied in many aspects of life, such as finance, banking, business, commerce, e-commerce, tourism, education, and healthcare. Previously, blockchain was known as the origin of cryptocurrencies. However, in recent years, blockchain technology and its applications have gone far beyond cryptocurrencies. This study aimed to assess the current situation, development trends, opportunities, and challenges in applying blockchain in business and financial activities. The research analyzed the Vietnamese government’s policies related to blockchain technology. In addition, the study updates the development trends of blockchain technology in business and financial activities through international scientific conferences, books, newspapers, and magazines. The study’s findings indicate that Vietnam is a high potential market for developing blockchain technology. Also, Vietnam faces many opportunities and challenges in catching up with this trend, especially when Vietnam is implementing many policies to develop the digital economy. Therefore, thanks to assessing the current situation, development trends, opportunities, and challenges in Vietnam, the study helps to suggest some solutions for the difficulties in applying blockchain technology, especially the thirst for human resources in the blockchain industry.
Traditionally Heuristic-Analytical Type of Teaching: Theory and Practice

The article substantiates the scientific and theoretical provisions of the traditionally heuristic-analytical type of education, which is understood as a type of education that combines cognitive, creative, and analytical activities enter higher education, that is, reproductive and productive education. The main purpose of the study is to experimentally test the effectiveness of using heuristic-analytical training in the learning process. In the course of the research, the method of experimental research was used. It was stated that most of the respondents in the experimental group should have a high degree of development of professionally important skills, while the majority of respondents in the control group have an average level of development.
Implementation of the Fisher-Yates Shuffle Game Algorithm in Learning Hijaiyah Letters

In 2020 the Indonesian government and the world health agency (WHO) declared the Covid-19 virus a pandemic. As a result, some activities cannot be carried out offline, especially in learning activities. The government requires that learning activities be carried out online or online. The number of children who really need help to understand the learning material provided, for example, is a game which is one of the media in learning for children to make it more interesting in its delivery. With the aim of providing basic Koran education for early childhood with interactive and fun games and the application of the Fisher-Yates Shuffle algorithm in the quiz feature that is used to randomize hijaiyah letter questions. Stages of research 1). Collecting data using library studies, namely Iqra books and related research papers. 2). Game design using story board. 3). Application of the Fisher-Yates Shuffle algorithm. 4). Testing the Fisher-Yates algorithm, black box and question. The results obtained from 10 question and 96 respondents resulted in 89.5% concluding that the hijaiyah letter game uses the Android-based Construct 2 game engine. implementation of the Fisher-Yates Shuffle algorithm on the quiz feature is feasible to use.
Recommendations of Thesis Supervisor using the Cosine Similarity Method

During the thesis writing process, the role of the supervisor is needed so that the completion of the thesis is timely so that the quality of the thesis is maintained. One of the problems in determining the thesis supervisor for undergraduate computer science at Bumigora University is the subjectivity of the head of the study program in determining the thesis supervisor. Not only that, the selection process for supervisors is done manually so that it can take a long time and can slow down student thesis work. Students' thesis work will be late and not on time if the thesis topic is not in accordance with the competence of the lecturer. This study aims to apply the cosine similarity method to the recommendation of a thesis supervisor for undergraduate computer science at Bumigora University. The stages of this research consist of collecting thesis documents, pre-processing text (Case Folding, Tokenization, Filtering, Stemming), word weighting with TF-IDF, implementation of the cosine similarity method, and accuracy testing. The data used are 113 thesis documents which are divided as training data as many as 90 documents and testing data 23 documents. Based on the testing data on the test, the cosine similarity method can correctly recommend 21 of 23 thesis documents with an accuracy of 91.3%. Thus, the cosine similarity method can be applied to the case of selecting a thesis supervisor for undergraduate computer science at Bumigora University because it has very good accuracy.
X-Ray Classification of Pneumonia by Neural Networks Convolution using Vgg Architecture

Pneumonia is one of the deadliest diseases, killing 2-3 million people in developed countries like the United States. Based on WHO's view, pneumonia is one of the leading causes of death in children worldwide, WHO says many children under the age of 5 have died from the disease. And in 2017, the World Health Organization (commonly known as WHO) reported that pneumonia had claimed the lives of 800,000 children under the age of 5. This is why researchers developed this program, to help the public diagnose pneumonia. In this study, we generated a Deep Learning model using the CNN (Convolutional neural network) approach using the VGG16 architecture for thoracic pneumonia classification and normalization. The results of this study show that the Convolution neural network method can classify chest X-ray results with pneumonia with the highest accuracy of 0.9772
The Combination of R&amp;D and MDLC Models in WEB-Based Interactive Learning Media

The Technopreneur is a core course offered in semester IV (four) in the Information Systems department, Faculty of Information Technology, Unand. Based on observations, the learning process using the lecture method via video conference makes students more inclined to be subordinated. At the same time, the lecturer position tends to appear as a one-man show. The problems make students more-passive and weak in the creative power. To attract students' interest in learning, it is necessary to develop interactive learning media. This research aims to produce a WEB-based interactive learning media with a combination of R&amp;D and MDLC model. Learning media is designed based result course learning process on the previous semester and user needs. Based on the results of system functionality testing, it shows 100% of the features are functioning properly. While testing user satisfaction on multimedia elements from 20 respondents with the Mean Opinion Score (MOS) scheme was obtained at 4.11, which means the learning media is quite good. For interactive elements, it gets a score of 4.31, which means it is included in the category ready to be applied in the Technopreneur class.
Gold Price Prediction Based on Gold.org Data Using the Long Short Term Memory Method

Gold is one form of precious metal with priceless value in this day and age. Therefore, many people are starting to invest in gold. Someone who wants to invest in gold must pay attention to changes in gold's buying and selling price. One site that can be used as a reference to see changes in the price of buying and selling gold is gold.org. Several factors influence gold price changes, namely changes in the value of the US Dollar exchange rate, the amount of world gold production, and the increase in demand for gold itself. This means that the price of gold tends to be unstable because of frequent changes. The LSTM or Long Short Term Memory method can be implemented to predict gold prices based on previous gold prices. The prediction model built in this study predicts gold price in the future based on 60 last gold price data. Based on the results of accuracy measure, an accuracy of 87.84% was obtained with the value of the difference between the original price and the prediction of 5 and the number of epochs being 100.
Nave Bayes Optimization with PSO for Predicting ICU Needs for Covid-19 Patients

Covid-19 is a global pandemic that requires a coordinated worldwide response across all national health and healthcare systems. Identifying patients who are at high risk of contracting the Covid-19 virus is important to increase awareness before patients are further infected by the Covid-19 virus which can cause severe respiratory illness that requires special treatment in intensive care units (ICU). This study aims to predict ICU needs in patients infected with the Covid-19 virus. The value results from the prediction of ICU needs are used as a reference for hospitals to meet ICU needs for patients infected with Covid-19 so that they can increase ICU supplies. The prediction will be carried out using the Naïve Bayes algorithm method with optimization using the PSO algorithm. Based on the results of the study, the population size 20 with an accuracy value of the NBC algorithm was 87.03%, population size 40 with an accuracy value of 87.28, population size 60 obtained an accuracy of 87.13%, population size 80 with an accuracy value of 87.16 % and population size 100, the results obtained are 87.26% so that each population has an increase in the accuracy value.
Mlo and CC view of feature fusion and mammogram classification using deep convolution neural network

Breast cancer is the most frequent type of cancer in women all over the world. The improvement of computer aided system help the radiologist for the effective analysis and diagnosis of breast cancer. It presents a computational methodology for classifying breast cancer as normal, benign and malignant from CC and MLO views of mammogram image. The proposed strategy consists of feature extraction, multiple view feature fusion and classification. The input images are fed into feature extraction where convolution neural network is applied. The CNN is nicely suitable for both feature extraction, feature fusion and mammogram classification. In this framework, convolution layer, pooling and activation function are used as a feature extraction techniques. After the process of feature extraction, feature fusion is employed by average pooling of CNN. The feature fusion will increase or maximize the relevant information of the breast image. Finally obtained features from the fusion are fed into CNN classifier in which softmax and fully connected layer are employed as a classifier techniques. The proposed work achieves 98.4% of accuracy to classify the breast cancer from MLO and CC views using hybrid feature with CNN classifier.
Classification of acute lymphoblastic leukaemia using machine learning algorithms

Acute Lymphoblastic Leukemia is a serious disease which may cause death if it is not detected at an early stage. It is common in children as well as adults. The detection of ALL is being done manually by examining the blood smear samples under a microscope. Manual blood testing has got several shortcomings such as it is slow and accuracy is also less. Generally, the inspection is done by an experienced pathologists and if there is any malformations the presence of lymphoblasts will be there. The accuracy of the diagnosis will be dependent on the experience of the operator. The Proposed research work aims in improving the diagnosis of ALL using Machine Learning Classifiers. Few classifiers haven been applied and compared on the segmented dataset images. The automated system can provide several advantages like it will minimize human intervention and it would provide more accurate results. In this research work EDES-SVM and EDSC-SVM have been used for classification. Experimental results obtained are then compared with the results of other machine learning classifiers such as SVM, ESVM, DSC-SVM, DES-SVM. From the experimental results it is analysed that the proposed method outperforms the conventional methods.
The Effectiveness of Flashcard Media and Letter Learning Applications to Help Dyslexic Children's Reading Ability in Elementary School

Reading is one of the most important skills and is often taught in elementary schools. Usually learning difficulties that are often found are reading difficulties (dyslexia). This article tries to explore the characteristics of the difficulties experienced by dyslexic students in the reading process at school, as well as efforts to overcome reading difficulties for dyslexic students in elementary schools through flashcard media and the 'Learn Letters' application on smartphones. The researcher uses a qualitative type of research with a case study method. The subject of the study was a 7-year-old dyslexic child with the initials APS who attended one of the public elementary schools in the city of Bogor. Data collection techniques used are observation, interviews, and documentation. Data analysis was carried out by data reduction, data presentation, and drawing conclusions. Research findings show that APS can memorize all the letters of the alphabet, but cannot distinguish the letters [b] and [d], [m] and [n], and has difficulty writing some letters and numbers correctly. The results showed that the flashcard learning media and the 'Learning Letters' application could improve the reading skills experienced by the research subjects. Recommendations from research results on the effectiveness of the flashcard creative learning media method and the 'Learning Letters' application can be followed up by using other applications or more interesting media by utilizing digital technology
"Let's Eat Grandma": Does Punctuation Matter in Sentence Representation?

Neural network-based embeddings have been the mainstream approach for creating a vector representation of the text to capture lexical and semantic similarities and dissimilarities. In general, existing encoding methods dismiss the punctuation as insignificant information; consequently, they are routinely treated as a predefined token/word or eliminated in the pre-processing phase. However, punctuation could play a significant role in the semantics of the sentences, as in "Let's eat\hl{,} grandma" and "Let's eat grandma". We hypothesize that a punctuation-aware representation model would affect the performance of the downstream tasks. Thereby, we propose a model-agnostic method that incorporates both syntactic and contextual information to improve the performance of the sentiment classification task. We corroborate our findings by conducting experiments on publicly available datasets and provide case studies that our model generates representations with respect to the punctuation in the sentence.
THE ILLUSION OF CONTROL AS A CATEGORY OF THE ILLUSION OF THINKING

People often make mistakes evaluating their personal control over certain situations. It is the result of a normal functioning of cognitive system. The illusion of control is a cognitive bias. It is our tendency to believe that, or act as if, we can skillfully influence and control outcomes of chance events. The illusion of control occurs when individuals overestimate their personal influence over an outcome. The article provides a theoretical analysis of the concept of «the illusion of control» in cognitive psychology. Langer (1975) originally proposed that the illusion of control occurs because people confuse skills and chance situations. Langer found that people’s confidence in their chances of winning was influenced by many different factors—none of which actually had any effect on their chances of winning. Thompson et al. (1998) have presented a more comprehensive explanation of the illusion of control based on a control heuristic that can account for more of the findings. According to the control heuristic explanation, people use both connection and intention to judge their control. Because both can be present even when control is not, personal control is often overestimated. The illusion of control is affected by skill-related factors, success or failure emphasis, need or desire for the outcome, mood, intrusion of reality, power, and regulatory focus. The illusion of control consists of overestimating the influence that our behavior exerts over uncontrollable outcomes. The challenge for scientists is to research how individuals manage their use of illusory control across a variety of circumstances to find optimal effects.
The Economic Learning Achievement Influenced by the School Environment and Learning Motivation

Learning achievement is part of an outcome of the learning process carried out by a student at school.This study analyzes the influence of the educational environment and learning motivation on economic learning achievement.This form of research is retrospective and quantitative.This study utilized as many as 83 respondents from Bengkulu Utara's class X Social Science State High School 8 using a technique of saturation sampling.This study's method of analysis is regression analysis.1) There is a strong correlation between economic learning achievement and student motivation in school, and these findings are further supported by research showing that both student motivation and school environment have a significant impact on economic learning achievement (63.7% and 37.3%, respectively).
Style Transfer of Black and White Silhouette Images using CycleGAN and a Randomly Generated Dataset

CycleGAN can be used to transfer an artistic style to an image. It does not require pairs of source and stylized images to train a model. Taking this advantage, we propose using randomly generated data to train a machine learning model that can transfer traditional art style to a black and white silhouette image. The result is noticeably better than the previous neural style transfer methods. However, there are some areas for improvement, such as removing artifacts and spikes from the transformed image.
Incremental Prototype Tuning for Class Incremental Learning

Class incremental learning(CIL) has attracted much attention, but most existing related works focus on fine-tuning the entire representation model, which inevitably results in much catastrophic forgetting. In the contrast, with a semantic-rich pre-trained representation model, parameter-additional-tuning (PAT) only changes very few parameters to learn new visual concepts. Recent studies have proved that PAT-based CIL can naturally avoid fighting against forgetting by replaying or distilling like most of the existing methods. However, we find that PAT-based CIL still faces serious semantic drift, the high-level forgetting problem caused by classifier learning bias at different learning phases, which significantly reduces the performance of PAT-based CIL. To address this problem, we propose Incremental Prototype Tuning (IPT), a simple but effective method that tunes category prototypes for classification and learning example prototypes to compensate for semantic drift. Extensive experiments demonstrate that our method can effectively compensate for semantic drift. Combined with well-pre-trained Vit backbones and other PAT methods, IPT surpasses the state-of-the-art baselines on mainstream incremental learning benchmarks.
AN ANALYSIS OF THE USE OF HEDGES BY YOUNG CHILDREN

&#x0D; &#x0D; &#x0D; Children tend to have simple pronunciation and language compared to adults. However, children will also experience language development according to their age such as Noah Ritter, who was five years old when interviewed by television stations until invited to the Ellen Show two years letter when Noah was seven years old. Noah Ritter is also nicknamed "The Apparently Kid" because he often says "apparently" in his sentences. In this case, there is a child who can speak using hedges whereas the hedges themselves are usually used by adults. This study aimed to describe the use of hedges in young children and explain some of the factors and causes why young children can use hedges like adults. The method used in this research was a descriptive qualitative method. For the results and discussion, the researchers found that a child can use hedges from many factors such as the environment from both family and friends and how often Noah Ritter always hears and imitates the language used by his parents. As a result, in language development, he can apply hedges. It can be concluded that the hedges used by children are the result of children imitating their environment because children often listen to adults talk and indirectly understand the context of their use even though they do not fully understand.&#x0D; &#x0D; &#x0D;
An Online Sparse Streaming Feature Selection Algorithm

Online streaming feature selection (OSFS), which conducts feature selection in an online manner, plays an important role in dealing with high-dimensional data. In many real applications such as intelligent healthcare platform, streaming feature always has some missing data, which raises a crucial challenge in conducting OSFS, i.e., how to establish the uncertain relationship between sparse streaming features and labels. Unfortunately, existing OSFS algorithms never consider such uncertain relationship. To fill this gap, we in this paper propose an online sparse streaming feature selection with uncertainty (OS2FSU) algorithm. OS2FSU consists of two main parts: 1) latent factor analysis is utilized to pre-estimate the missing data in sparse streaming features before con-ducting feature selection, and 2) fuzzy logic and neighborhood rough set are employed to alleviate the uncertainty between estimated streaming features and labels during conducting feature selection. In the experiments, OS2FSU is compared with five state-of-the-art OSFS algorithms on six real datasets. The results demonstrate that OS2FSU outperforms its competitors when missing data are encountered in OSFS.
Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round

Meta-learning from learning curves is an important yet often neglected research area in the Machine Learning community. We introduce a series of Reinforcement Learning-based meta-learning challenges, in which an agent searches for the best suited algorithm for a given dataset, based on feedback of learning curves from the environment. The first round attracted participants both from academia and industry. This paper analyzes the results of the first round (accepted to the competition program of WCCI 2022), to draw insights into what makes a meta-learner successful at learning from learning curves. With the lessons learned from the first round and the feedback from the participants, we have designed the second round of our challenge with a new protocol and a new meta-dataset. The second round of our challenge is accepted at the AutoML-Conf 2022 and currently ongoing .