Reviewerly/exHarmony huggingface dataset:
Reprint: A Randomized Extrapolation Based on Principal Components for Data Augmentation

Data scarcity and data imbalance have attracted a lot of attention in many fields. Data augmentation, explored as an effective approach to tackle them, can improve the robustness and efficiency of classification models by generating new samples. This paper presents REPRINT, a simple and effective hidden-space data augmentation method for imbalanced data classification. Given hidden-space representations of samples in each class, REPRINT extrapolates, in a randomized fashion, augmented examples for target class by using subspaces spanned by principal components to summarize distribution structure of both source and target class. Consequently, the examples generated would diversify the target while maintaining the original geometry of target distribution. Besides, this method involves a label refinement component which allows to synthesize new soft labels for augmented examples. Compared with different NLP data augmentation approaches under a range of data imbalanced scenarios on four text classification benchmark, REPRINT shows prominent improvements. Moreover, through comprehensive ablation studies, we show that label refinement is better than label-preserving for augmented examples, and that our method suggests stable and consistent improvements in terms of suitable choices of principal components. Moreover, REPRINT is appealing for its easy-to-use since it contains only one hyperparameter determining the dimension of subspace and requires low computational resource.
PENINGKATAN HASIL BELAJAR PESERTA DIDIK KELA IV TEMA 4 (BERBAGAI PEKERJAAN) MELALUI MODEL PEMBELAJARAN PROBLEM BASED LEARNING

The problem of this research is the low learning outcomes of students, especially in Theme 4. Students still do not meet the completeness of the KKM value. The purpose of this study is to improve student learning outcomes through the Problem Based Learning Model in Learning Theme 4 Class IV Semester 1 SDN 1 Kebokura. The subjects of this study were fourth grade students of SDN 1 Kebokura. This research is a Classroom Action Research which lasts for two cycles consisting of planning, action, observation and reflection stages. The data collection technique in this research is a student evaluation test which consists of knowing the learning outcomes of students. Based on the research results obtained in this study, it can be concluded that the learning outcomes of Theme 4 students of class IV SDN 1 Kebokura can be increased by using the Problem Based Learning model. The increase in learning outcomes can be seen from the initial condition of the average score of 64.5 with a percentage of the number of students who completed 41.6%. In the first cycle there was an increase in the average value of 77.6 with the percentage of the number of students who completed 75%, with a target of 85%. Then in the second cycle there was an increase in the average score of 89.3 with the percentage of students who completed 100%, with a target. Based on the results of the study, it can be concluded that there is an increase in student learning outcomes in Theme 4 using the Problem Based Learning model.
Evaluation of Sub-Network search programs in epilepsy-related GWAS dataset

ÖzThe active sub-network detection aims to find a group of interconnected genes of disease-related genes in a protein-protein interaction network.In recent years, several algorithms have been developed for this problem.In this study, the analysis of disease-specific sub-network identification programs is evaluated using epilepsy data set.Under the same conditions and with the same data set, 9 different programs are run and results of their Greedy algorithm, Genetic algorithm, Simulated Annealing Algorithm, MCC (Maximal Clique Centrality) algorithm, MCODE (Molecular Complex Detection) algorithm, and PEWCC (Protein Complex Detection using Weighted Clustering Coefficient) algorithm are shown.The top-scoring 5 modules of each program, are compared using fold enrichment analysis and normalized mutual information.Also, the identified subnetworks are functionally enriched using a hypergeometric test, and hence, disease-associated biological pathways are identified.In addition, running times and features of the programs are comparatively evaluated. Aktif alt ağ tespiti, bir protein-protein etkileşim ağında hastalıkla ilgili genlerin birbirine bağlı bir grup genini bulmayı amaçlamaktadır. Son yıllarda bu problem için çeşitli algoritmalar geliştirilmiştir. Bu çalışmada, hastalığa özgü alt ağ tanımlama programlarının analizleri epilepsi veri seti kullanılarak değerlendirilmiştir. Aynı koşullar altında ve aynı veri seti ile 9 farklı program çalıştırılmış ve bu programların Greedy algoritması, Genetik algoritma, Simüle Tavlama Algoritması, MCC (Maximal Clique Centrality) algoritması, MCODE (Molecular Complex Detection) algoritması ve PEWCC (Protein Complex) AğırlıklıKümeleme Katsayısı) algoritması sonuçları gösterilmiştir.Her programın en yüksek puan alan 5 modülü,
ELECTRONIC DOCUMENT AS A SUBJECT OF CRIMINAL LAW PROTECTION

The article is devoted to the problem of criminal legal protection of electronic documents. The information environment provides ample space for documentation, but also creates risks of safe transfer, storage of documents, their protection from unlawful modification, special attention should be paid to the criminal legal protection of official electronic documents. It should be noted that the current criminal legislation regulating the issues of liability for illegal trafficking of official documents does not quite correspond to modern realities. The Criminal Code operates with concepts and terms that were focused on paper document flow. Firstly, it is necessary to revise the traditional approaches to establishing criminal liability for illegal trafficking in official documents, unifying the basic concepts and features for different documents. In addition, the acquisition, transportation and storage of counterfeit ofs should be decriminalized.
Motivational Problems of Practising Speaking in Large Classes in EAP Courses at the University of Dhaka

Motivating the students to speak is generally considered to be more difficult than motivating them in practicing other skills. Another great challenge for the teacher is keeping up a good level of motivation with an exceptionally large class. In the English language courses at the University of Dhaka, the teachers are confronted with a number to obstacles that puts extra burden on them and hampers their motivation in the same way as it decreases the motivation of their students. This study tries to portray the classroom situation and analyse the underlying problems that are responsible for reducing the motivation of speaking in English for both the students and the teachers. It aims to identify these variables in order to find out a way of minimizing the hindrances and enhance successful practising of English speaking skill.
Fan-beam Projection-based Feature Extraction for Facial Expression Recognition

This paper presents a novel method of feature extraction using Fan beam projection-based data. The Fanbeam projection covers the image completely and hence gathers all the important information. Even though the image quality is distorted, this type of feature extraction method helps to gather all the important information as there is a huge volume of projection data. Also, the use of multiple detectors speeds up the entire process. All the projections of the image together form a sinogram image which is unique for each facial expression image. Hence, the sinogram image is divided into grids and the histogram formation results in a feature vector for each image. The classification of these feature vectors using Radial Basis Function-based Extreme learning Machine (RBF-ELM) results in high classification accuracy for all the datasets.
The Director’s Embodiment of Bella Bartok’s Opera "Duke Bluebeard’s Castle":Compositional and Production Aspect

It was considered the main director's approaches to ideological and thematic analysis of B. Bartok's opera "Duke Bluebeard's Castle", which formed the basis of the concept of the play produced by the applicant. The author outlined the compositional features of the realized performance in comparison with the composer's idea. The applicant highlights the features of the director's interpretation of modern opera in the postmodern aesthetics, based on various methodological approaches, including analytical, communicative and structural-systemic methods. Factors that cause the phenomenon of abuse in human relations have been analyzed in the world of modern trends. There are options for triggers that can provoke the violent existence of partners in modern society. The need to introduce into the traditional composition of B. Bartok's opera the second part-reflection with a specialist psychologist in order to draw attention to the problem of violence in relationships and the desire to encourage society to dialogue on this topic. The moments of interpretation of musical and theatrical works of the above aesthetic direction are marked. The author's point of view on the problem of violence is proposed, where the victim, in connection with the acquired negative experience, turns himself into an abuser. The main markers of abusers' behavior and unhealthy relationships are highlighted. The vectors that guide the consideration of the causal relationships of this kind of relationship are marked so that directors can understand the nuances of the behavior of abusers and victims of violence. The author explains the directorial embodiment of an operatic work of the Art Nouveau period with clearly delineated characteristic features of time and place of action in the conditions of theatrical postmodernist orientation. The specifics of the director's interpretation of B. Bartok's opera "Duke Bluebeard's Castle" in the author's art project are determined. The perspective for further scientific research of the problem of abuser and its reflections on theatrical stages is modeled
Improving the Performance of Closet-Set Classification in Human Activity Recognition by Applying a Residual Neural Network Architecture

The Time Series Classification Residual Network (TSCResNet) deep learning model is introduced in this study to improve the classification performance in the human activity recognition (HAR) problem. Specifically in the context of closed-set classification where all labels or classes are present during the model training phase. This contrasts with open-set classification where new, unseen activities are introduced to the HAR system after the training phase. The proposed TSCResNet model is evaluated with the benchmark PAMAP2 Physical Activity Monitoring Dataset. By using the same quantitative methods and data preprocessing protocols as previous research in the field of closed-set HAR, results show that the TSCResNet model architecture is able to achieve improved classification results across accuracy, the weighted F1-score, and mean F1-score.
Planning and Manufacturing of Four Axis Solar Panels With Reflector Angle Adjustments

Solar thermal energy is one type of renewable energy, so this type of energy can be converted into other energy. This study uses a four-axis solar tracker with angle settings on the reflector to get optimal sunlight, scanning to determine the optimal lighting angle, measurement results are stored in real-time in the data logger. This study uses an LDR (Light Dependent Resistor) as a sunlight detector, equipped with several sensors, namely: current, voltage and power sensor (INA219), light sensor (MAX4409), and temperature sensor (DS18B20), and reflector angle as a parameter of solar efficiency panels. . The results showed that a four-axis solar tracker equipped with a reflector was able to increase the output power. The maximum power production produced by solar panels is: At a reflector angle of 300, the maximum power generated by a static panel is 143.43 W while a solar tracker is 175.15 W. At a reflector angle of 450 the maximum power generated by a static panel is 170.01 W and solar tracker 236.36 W. At an angled reflector of 600 the full power generated by a static panel is 87.77 W, and a solar tracker is 123.36 W. This study concludes that a solar tracker panel with an angle setting of 300 is more capable of maximizing power output than a static solar panel.&#x0D; &#x0D; Keyword : Solar Tracker, Four Axist, Reflektor
study on the impact of Corona Virus and it’s mutants in India using machine learning algorithms

With the advent growth of technology, large volumes of data are available in the internet for researchers to explore. Social Media is one such platform that helps researchers in analyzing various data for various reasons such as improved Customer Service, development of quality Products. , discovering New Marketing Strategies, improve Media Perceptions and much more. The Social networking sites such as Facebook, Twitter, Instagram are being used by people on a large scale to share and express their views in the form of text, emoji and post. This data is used for sentiment analysis. This paper aims at analyzing the human sentiments and emotions in the second wave of Corona virus in India and about the awareness of vaccination using twitter data. Machine learning algorithms such as Naïve Bayes, Logistic Regression and Support Vector Machine were implemented on the twitter data set. As the pandemic situation has created an alarming situation with new symptoms, the disease has affected many in India. This study will assist the government agencies and health care volunteers to better assess the mental state of public and to bring more awareness on safe and secured living by adapting precautionary measures.
The Evaluation of University Course Quality under the Background of Wireless Communication and Big Data

In recent years, education issues have been the focus of attention from all walks of life. An important indicator for measuring the level of education in colleges and universities is the quality of teaching. It determines the level of the students' basic quality that is the target of training. Curriculum is the core of forming teaching quality and the core element of cultivating talents. Curriculum evaluation is an important means to detect and diagnose the teaching quality of schools and the source of talent training quality.The purpose of this article is to study the quality evaluation system of college courses under the background of wireless communication and big data, taking students as the initiators of evaluation activities, positioning the factors that they are interested in, and referring to the selection of evaluation indicators in other research results to determine the evaluation system of this study. Through students’ evaluation of Japanese MOOC courses, the evaluation system constructed by the laboratory is evaluated. The method of collecting evaluation data is to issue electronic questionnaires and then perform statistical analysis on the results of the questionnaires after collection, to understand the current status of MOOC course learning and the degree of recognition between learners and evaluation indicators. Through the analysis of the questionnaire data, the reliability and validity of the questionnaire are tested to ensure the validity and reliability of the questionnaire data. The evaluation indicators are screened by factor analysis, and the entropy method is used to determine the weight of the evaluation indicators, thus forming the final MOOC course evaluation. Reorganizing and constructing the Japanese curriculum quality evaluation index system conforms to the general law of the construction of the index system.
A Study on the Influence of Container Terminal Information Quality on the Reuse Intention

In the era of information technology, companies are introducing information system to improve their work processes in accordance with changes in information system.However, the reality has occupied the majority failure cases because the users do not provide the required level of information.In the process of building information system, it is able to bring the competitive advantage in the market by reflecting the needs of users sufficiently, it is necessary to reduce the loss of the company and increase the satisfaction of the user.The information system used in a container terminal is a system that integrates and manages all processes, such as planning, operation, and management of a container terminal, and is a key element in operating the terminal.The automation of container terminals that perform tasks such as unloading, loading, and transfer using the terminal operating system is continuously expanding.In order to secure high-satisfaction and loyal customers of container terminals, terminal operating systems depend on providing high-quality information to consumers.The purpose of this study is to analyze the relationship between information quality factors of container terminals and customer satisfaction and re-use intention.Based on the information system success model, the types of information quality of container terminals were classified into understandability, accuracy, timeliness, formality and usability as the cause variables affecting customer satisfaction.The results of this study are as follows.First, the information quality of container terminals had a significant effect on customer satisfaction.Second, customer satisfaction had a significant effect on re-use intention.
LEARNING MODEL BASED ON CREATIVITY DEVELOPMENT IN IMPROVING LITERATURE APPRECIATION ABILITY

Learning literature in universities has not been able to increase creativity even though many models have been used. Therefore, this study aims to describe the effectiveness of the learning model based on creativity development in improving students' literary appreciation abilities. This quasi-experimental research uses a Quasi-experimental Design model of the Non-Equivalent Before-after Design. The population includes students of the Indonesian Language Education Study Program at the University of PGRI Madiun and STKIP PGRI Pacitan. Sample selection was done randomly. Data were collected through a short story appreciation ability test. Data analysis was carried out in two stages, namely the analysis prerequisite test stage and the data analysis stage. Data analysis used independent t-test. Based on the results of data analysis and discussion, it can be concluded that the learning model based on creativity development can improve students' ability to appreciate short stories. This enhancement has proven to be very effective. However, the application of this learning model needs to be supported by the environment, facilities, and curriculum.
Electroencephalography and Blood Cells Analysis for Cerebral Malaria Detection Using Deep Learning and Neural Networks

Cerebral malaria is a clinical syndrome that is marked by the asexual parasitic form, 'plasmodium falciparum.' Early diagnosis can help in avoiding fatal and long-lasting neurological outcomes, such as severe psychosis, metabolic acidosis, and hypoglycemia. Therefore, the developed model aims to detect the same for narrowed down symptoms associated with the same. The design is divided into the following functions: the recognition of potential seizures in the patient and the identification of parasitic blood cells. In the first half, the deep-learning algorithm consists of a Neural Network Sequential model to grasp the exclusive electroencephalogram (EEG) features of epileptic seizures and also reveal the correlation between data samples that are successive to each other. The latter work focuses on inspecting the decompressed blood cells images that are fed into a deep convolutional neural network to distinguish parasitized cells from healthy cells. With this, the transfer of medical findings is also made effortless since compression of images is done without loss of valuable information.
Creating Sentiment Dictionaries: Process Model and Quantitative Study for Credit Risk

Since textual user generated content from social media platforms contains valuable information for decision support and especially corporate credit risk analysis, automated approaches for text classification such as the application of sentiment dictionaries and machine learning algorithms have received great attention in recent user generated content based research endeavors. While machine learning algorithms require individual training data sets for varying sources, sentiment dictionaries can be applied to texts immediately, whereby domain specific dictionaries attain better results than domain independent word lists. We evaluate by means of a literature review how sentiment dictionaries can be constructed for specific domains and languages. Then, we construct nine versions of German sentiment dictionaries relying on a process model which we developed based on the literature review. We apply the dictionaries to a manually classified German language data set from Twitter in which hints for financial (in)stability of companies have been proven. Based on their classification accuracy, we rank the dictionaries and verify their ranking by utilizing Mc Nemar’s test for significance. Our results indicate, that the significantly best dictionary is based on the German language dictionary SentiWortschatz and an extension approach by use of the lexical-semantic database GermaNet. It achieves a classification accuracy of 59,19 % in the underlying three-case-scenario, in which the Tweets are labelled as negative, neutral or positive. A random classification would attain an accuracy of 33,3 % in the same scenario and hence, automated coding by use of the sentiment dictionaries can lead to a reduction of manual efforts. Our process model can be adopted by other researchers when constructing sentiment dictionaries for various domains and languages. Furthermore, our established dictionaries can be used by practitioners especially in the domain of corporate credit risk analysis for automated text classification which has been conducted manually to a great extent up to today.
Penyusunan Laporan Keuangan Berdasarkan ISAK 35 dengan Menggunakan Microsoft Excel 2016 pada LKSA Bina Yatim

Orphanage or LKSA (Institute for Child Social Welfare) is a form of non-profit oriented that earns income from government grants or donations from the public who do not expect compensation for what has been given. LKSA's responsibility for these contributions needs to be transparent and reported regularly in the form of financial statements to avoid misuse of funds and to increase the credibility of LKSA to external parties. Currently, the standard for preparing financial statements that applies to non-profit oriented entities is ISAK 35. However, there are still LKSA that have not implemented this standard. One of them is LKSA Bina Yatim. This study aims to determine how the preparation of financial statements has been carried out, to design financial report formats with Microsoft Excel and compose it based on ISAK 35, and to find out the implications of applying these design and preparation to LKSA Bina Yatim. The research method used descriptive qualitative method. The research data was obtained through interviews, documentation, and literature study. The results of this study indicate that LKSA Bina Yatim is still manual in recording and preparing its financial statements and only prepare a simple income statement that does not follow the standard. Therefore, the authors designed the format for preparing financial statements using Microsoft Excel to assist LKSAs in preparing financial reports based on ISAK 35 in May 2021. The implications of this design and preparation can help LKSA in preparing financial statements effectively and efficiently.