Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Twitter's Opinion on The Russia and Ukraine War Using Bert

News about the war that took place between Russia and Ukraine can not be denied affecting various aspects of life in the world. This affects the writings of every citizen of the world on various social media platforms, one of which is Twitter. Sentiment analysis is a process of identifying and making sentiment categories which is done computationally. Sentiment analysis process is also intended to make computers understand the meaning of sentences written by humans by processing using algorithms. This study uses a deep learning method using a language model, namely BERT (Bidirectional Encoder Representation Form Transformers) as a process of analyzing the sentiments that exist in tweets written about the war in Russia and Ukraine by twetter social media users. These sentiments will be divided into three parts, namely positive, neutral and negative. In this study, the hyperparameters used were 10 epochs, learning rate 2e-5, and batch size 16. The sentiment analysis test used the BERTbase Multilingual-cased-model model and the accuracy value obtained was 97%.
NG-GAN: A Robust Noise-Generation Generative Adversarial Network for Generating Old-Image Noise

Numerous old images and videos were captured and stored under unfavorable conditions. Hence, old images and videos have uncertain and different noise patterns compared with those of modern ones. Denoising old images is an effective technique for reconstructing a clean image containing crucial information. However, obtaining noisy-clean image pairs for denoising old images is difficult and challenging for supervised learning. Preparing such a pair is expensive and burdensome, as existing denoising approaches require a considerable number of noisy-clean image pairs. To address this issue, we propose a robust noise-generation generative adversarial network (NG-GAN) that utilizes unpaired datasets to replicate the noise distribution of degraded old images inspired by the CycleGAN model. In our proposed method, the perception-based image quality evaluator metric is used to control noise generation effectively. An unpaired dataset is generated by selecting clean images with features that match the old images to train the proposed model. Experimental results demonstrate that the dataset generated by our proposed NG-GAN can better train state-of-the-art denoising models by effectively denoising old videos. The denoising models exhibit significantly improved peak signal-to-noise ratios and structural similarity index measures of 0.37 dB and 0.06 on average, respectively, on the dataset generated by our proposed NG-GAN.
Improving Graph Neural Network Models in Link Prediction Task via A Policy-Based Training Method

Graph neural network (GNN), as a widely used deep learning model in processing graph-structured data, has attracted numerous studies to apply it in the link prediction task. In these studies, observed edges in a network are utilized as positive samples, and unobserved edges are randomly sampled as negative ones. However, there are problems in randomly sampling unobserved edges as negative samples. First, some unobserved edges are missing edges that are existing edges in the network. Second, some unobserved edges can be easily distinguished from the observed edges, which cannot contribute sufficiently to the prediction task. Therefore, using the randomly sampled unobserved edges directly as negative samples is difficult to make GNN models achieve satisfactory prediction performance in the link prediction task. To address this issue, we propose a policy-based training method (PbTRM) to improve the quality of negative samples. In the proposed PbTRM, a negative sample selector generates the state vectors of the randomly sampled unobserved edges and determines whether to select them as negative samples. We perform experiments with three GNN models on two standard datasets. The results show that the proposed PbTRM can enhance the prediction performance of GNN models in the link prediction task.
THE EFFECTIVENESS OF EDMODO TOWARDS INDONESIAN EFL LEARNERS’ ENGLISH PROFICIENCY

As a result of the utilization of technological advances to achieve competency integration, the 4.0 technology era is gaining traction in the field of education. This study aims to explore the efficacy of using the Edmodo application as an online-based learning medium in order to improve the way in which EFL students learn English using technology. By a mixed-methods research design, this study recruited twenty-two tenth graders in the Mathematics and Natural Science stream from an integrated Islamic senior high school in Aceh province, Indonesia. The data indicate that the majority of students possess the necessary devices to assist them in completing their tasks, and a significant proportion of them use these devices to play games and conduct research. By categorizing aspects and components, Edmodo improves students' vocabulary when used to assess their learning progress through assignments and quizzes. In addition, the students are more willing to request assistance from teachers and peers when using the application. Despite the fact that Edmodo requires a smartphone and a reliable internet connection to utilize it, the participants do not report having any significant issues with the two requirements, however, half of them report having trouble attaching assignments from different types of files such as Doc, Pdf, Video, and Audio. Overall, Edmodo is an outstanding and highly effective online resource for reading, writing, listening, and speaking English classes (e-learning).
LOGICAL FUNCTION ANALYSIS IN JAKARTA POST NEWSPAPER OF EDITORIAL AND NEWS TEXT

Logical Function is a function related to the logical relationship between one clause and another, including two aspects, namely interdependency relations and logico-semantic relations. This study demonstrated the clause complexity of the transcript of two different texts with a similar subject matter, news and editorial text from the Jakarta Post Newspaper, such as, logical function, coordination (parataxis) and subordination (hypotaxis). The method of this study highlighted the analysis of qualitative research on the issue of clause complexity based on the perspective of Systemic Functional Linguistics (SFL) in the two different texts. SFL allows the analyst to shed light on interaction with the social context to imbue texts with the meanings expressed. The results of the study show that taxis in logical function analysis of News and Editorial Text apply two taxis, they are parataxis and hypotaxis. Hypotaxis is dominantly used in both text, 85.71% in Editorial and 89.65% in News. The proportion of the dominant hypotaxis in News text was 37.93% and in Editorial 33.33%.
SNOW WHITE AND THE SEVEN DWARFS DRAMA: CONVERSATIONAL IMPLICATURE

Conversational implicature is one of the most important ideas in pragmatics. The importance of conversational implicatures as a means of conveying messages is directly well established. In conducting this research, the researchers used a qualitative descriptive method. This research is qualitative because the data are in the form of words and analyzed based on the setting or natural context. Qualitative research is a type of research in which data are studied naturally without any treatment or intervention by researchers. Related to teaching English, this research helps students to know about figure of speech – how they are expressed in the context of certain situations. Referring to implicature theory, students know that conversation is more than just a series of exchanges; it consists of exchanges ideas according to norms of conversational cooperation, which can be manipulated to create various meanings. The application of conversational implicatures in Snow White and the Seven Dwarfs is in the learning process in the classroom between teachers and students. Conversational implicatures in Snow White and the Seven Dwarfs can provide several benefits and lessons for all students in learning to speak by increasing students' motivation, interest, and understanding of the learning material.
A survey on tamper detection techniques for digital images

Recently, fake and fabricated images that have been manipulated for several purposes, including cosmetic and some for illegal purposes, have spread on social media.And because this is not an easy matter, it has become necessary for researchers in this field to search and investigate the types of images, to verify their authenticity and how to manipulate them.Therefore, the aim of this research is to serve as an assistant to the researcher who wants to enter this field.In this research, a survey of the types of forgery was presented with examples.The most important common methods for detecting forgery were also presented, and the previous studies that contributed to the process of detecting fraud, both traditional detection and deep learning-based detection, were highlighted, while giving the most important strengths and weaknesses of both types.We note from this that detecting the forgery process is a difficult process and takes time to detect the changes that have been made to the image that cannot be detected by the naked eye.In this research, researchers have been urged to go towards deep learning for the purpose of detecting the forgery of the features that it enjoys.
A Comparative Study for Speech Summarization Based on Machine Learning: A Survey

The most important aspect of human communication is speech.Lengthy media such as speech takes a long time to read and understand.This difficulty is solved by providing a reduced summary with semantics.Speech summarization can either convert speech to text using automated speech recognition (ASR) and then build the summary, or it can process the speech signal directly and generate the summary.This survey will look at a various of recent studies that have used machine and deep learning algorithms to summarize speech.it discusses the speech summarizing literatures in terms of time restrictions, research methodology, and lack of interest in particular databases for literature searches.As newer deep learning approaches were not included in earlier surveys, this is a new survey in this discipline where different approaches with various datasets were explored for speech summarization and evaluated using subjective or objective methods.
Analysis of development state of the thermal flow sensors of general, biomedical and ecological designation

The paper analyzed the characteristics of microelectronic flow sensors, which made it possible to draw a number of important conclusions, namely: modern microelectronic thermal flow sensors, and in particular biomedical sensors, are characterized by a significant variety of principles of signal formation - from elementary linear converters based on one sensitive element to non-linear ones ( generation, time-dependent) converters based on matrices of functionally integrated elements. The problem of energy consumption of thermal flow sensors remains relevant. This is especially characteristic when powering destination sensors from autonomous, i.e., small-sized, low-power, low-voltage electrochemical cells. A decrease in energy consumption (power and heating temperature) leads to the parasitic effect of signal line resistances and, as a result, to the deterioration of functional characteristics, in particular, to a decrease in the accuracy of flow rate measurement.
TRANSITION SIGNALS IN HEADLINE NEWS

This study aimed to analyze the types of transition signals in headline news and the reasons for using transition signals in writing. This study applied a descriptive qualitative design. The sources of the data were three headline news taken from Antaranews.com entitled “Indonesia urges ASEAN, China to help maintain regional stability,” the jakartapost.com entitled “Myanmar to dominate ASEAN agenda, but little progress expected”, and 9News.com entitled “Russian President Vladimir Putin won't attend upcoming G20 summit in Bali” published on November 11th, 2022. The technique of data collection was documentation and the procedures in the the data analysis were 1) reading the headline news, 2) finding the transition signal in headline news, 3) underlining the transition signals in headlines news, 4) classifying the transition signals, 5) counting the total frequencies of transition signals, and 6) finding the reasons of using transition signals in writing. The results showed that the types of transition signals found in the headline news were to indicate addition, time, sample, distinction, and cause and effect, and the most widely used was to indicate addition in each headline news. Moreover, the reasons for using transition signals in headline news were 1) to make the movement of the paragraphs in the text logical and smooth, 2) to strengthen the internal cohesion and coherence in a text, 3) to show the relationship between the parts of sentences in a paragraph or between paragraphs, and 4) to provide valuable signals to the reader about the relationship between parts of the text.
LINGUISTIC VARIATION BETWEEN MALE AND FEMALE STUDENTS IN ACADEMIC WRITING

The variation of linguistic characteristics in academic writing between male and female is inevitable to happen. Therefore, this study aimed at finding out the differences of male and female students in linguistic characteristics of academic writing from perspective of linguistics, especially at the level of text analysis. The quantitative research method with convergent parallel design was used to analyze sixty students’ essay writing chosen by using a purposive sampling technique. The result revealed that there were some differences of linguistic characteristics in academic writing between male and female. The female students outperformed male students in all linguistic characteristics in text-level analysis. The female students were found more capable in paragraphing competences, linking devices, text output counts, topical organization, and ending. The findings generally support the notion that differences of linguistic characteristics between male and female should be considered in teaching academic writing to create a friendly teaching-learning process for both genders.
Deep Learning Algorithm for Automatic Breast Tumour Detection and Classification from Electromagnetic Scattering Data

Breast cancer is, by far, the most diagnosed disease for the death of women worldwide.Researchers are working with an alternative technology to detect tumors before they reach a terrible stage because of the numerous limitations in the current imaging approach.This article suggests a promising technique by utilising non-ionizing microwave signal and artificial intelligence especially deep learning algorithms for early detection of breast cancer.This contribution will present a method to detect and classify the tumor category using backscatter signals obtained from antenna simulation in CST microwave studio software.The post-processed scattering parameters are utilized to create image through MATLAB programming environment.The high intensity in the image represents the precise position of tumor.The automatic classification of tumor is achieved by YOLOv 5 deep learning model from the created microwave images.A training dataset with fifty image samples are formed by preprocessing, and then augmentation is applied to create final dataset with 1000 samples.This approach can identify the location and type of early-stage tumor with size of 5 mm.
A Review on Twitter Sentiment Analysis Using ML

Abstract: Social Media sites like twitter have billions of people share their opinions day by day as tweets. As tweet is characteristic short and basic way of human emotions. So, in this paper we focused on sentiment analysis of Twitter data. Most of Twitter's existing sentiment analysis solutions basically consider only the textual information of Twitter messages and strives to work well in the face of short and ambiguous Twitter messages. Recent studies show that patterns of spreading feelings on Twitter have close relationships with the polarities of Twitter messages. In this paper focus on how to combine the textual information of Twitter messages and sentiment dissemination models to get a better performance of sentiment analysis in Twitter data. To this end, proposed system first analyses the diffusion of feelings by studying a phenomenon called inversion of feelings and find some interesting properties of the reversal of feelings. Therefore, we consider the interrelations between the textual information of Twitter messages and the patterns of diffusion of feelings, and propose random forest machine learning to predict the polarities of the feelings expressed in Twitter messages. As far as we know, this work is the first to use sentiment dissemination models to improve Twitter's sentiment analysis. Numerous experiments in the real-world dataset show that, compared to state-of-the-art text-based analysis algorithms.
Pengaruh Motivasi Belajar Terhadap Hasil Belajar Matematika Siswa SMP

This study aims to determine whether there is an influence of learning motivation and learning style on the mathematics learning outcomes of eighth grade students of SMP Negeri 5 Palibelo in the Academic Year 2021/2022. The population in this study were all seventh grade students in the odd semester of the 2021/2022 academic year at Nageri 5 Palibelo Junior High School, Bima Regency, which consisted of two classes of 35 students. Determination of the sample is done by using the remote sampling technique. The data analysis technique used in this study is SPSS using descriptive statistical data analysis and inferential statistics. This researcher uses instruments in the form of questionnaires and test questions. The results showed that there was a significant effect of learning motivation on students' mathematics learning outcomes with a correlation coefficient of 0.932. In the regression analysis, a constant value of 0.749 was obtained. from other factors, as well as the contribution given by 86.9%.
STUDENTS’ PERCEPTIONS TOWARD COLLABORATIVE LEARNING STRATEGY FOR OVERCOMING SPEAKING ANXIETY AMONG STUDENTS OF AN ISLAMIC SENIOR SCHOOL

Anxiety is one of the most frequent problems in speaking a foreign language including English. High levels of anxiety prevent students from speaking English fluently. Therefore, teachers need to apply a strategy that can reduce these anxieties. One of the strategies is the collaborative learning strategy. This research aimed to find out students’ perceptions toward collaborative learning strategies for overcoming speaking anxiety. The research was conducted on eleventh-grade students at an Islamic Senior School in East Lombok regency. The total number of samples in this research was 33 students from XI social 4 class. This study was qualitative in nature since it used observation, a questionnaire, and an interview to collect the data. Students’ perception of using collaborative learning in learning speaking reached 85.5% based on the students' responses to the questionnaire, while data from the observation and interview reveal that the perception of using collaborative learning strategy in overcoming students’ speaking anxiety was positive because it can increase students’ confidence, help the students to understand the material easier, and make the students more active in the class. It can be concluded that collaborative learning strategy can help students to overcome their speaking anxiety.
THE EFFECT OF STORYTELLING ON STUDENTS’ SPEAKING ABILITY

This study aims to determine whether short storytelling can improve students' speaking ability at MAN 1 BIMA. This research used a pre-experimental research design with a quantitative approach. In this research, the samples were taken from students' grade X Bahasa in MAN 1 BIMA. The total number of samples was ten students. Purposive sampling was used. Speaking test was used to collect the data. The collected data were analyzed descriptively and by using t-test. The results show that 1) the use of short storytelling is effective in improving the students' speaking ability, 2) the students' speaking ability in terms of pronunciation in speaking English can be improved after applying short storytelling, and 3) there was a significant difference between the students’ speaking ability before and after using short storytelling.
EXPLORING DEVELOPMENT STAGES OF INTERCULTURAL FRIENDSHIP AMONG EFL LEARNERS

Intercultural communication or Intercultural Communicative Competence (ICC) is the different condition of society that used symbol systems and rules for communication and interaction with other cultures and languages. This research explored the stages of the intercultural friendship of English Foreign Language (EFL) students in Indonesia who joined the International Community Service. Nine students in the same events participated in collecting the data of the questionnaire and interview based on the three familiar theories: trial, enmeshment, and renegotiation. Then the results showed that the students had prepared information about the place of events and learned the new cultures and habits of the different societies. The other one, the students avoided sensitive issues such as ethnicity, religion, and race to accept the contradiction among the students and society to understand one another to evaluate the contradiction to be a positive value based on the three theories of intercultural friendship. Lastly, their challenges were between them and society, and the students and their friends in a small group. Diversification of purpose and goal of their project made them difficult to implement such as internet connection and public fasilities. Besides, the village situation was poorer than other communities were, and required some facilities to assist them in their everyday operations. In accordance with the results, keeping good communication and relationship would be easier for students or institutions for the next event. The program was continued for the other participants to make the successful goals.
Estimation of Potential Evapotranspiration for Optimizing the Usage of Surface Irrigation in Wajo District

Indonesian government is trying to increase the production of agricultural commodities in order to support the food self-sufficiency program. To support this program, the availability of irrigation facilities is important in supporting the productivity of agricultural field. Climate change and changes in agricultural systems have an impact on changes in the need of irrigation water in crop cultivation. The decreasing availability of water for agriculture encourages us to be able to use water more efficiently, especially for irrigation water. The purpose of this research is to predict the ETp value as a basis for determining plant water requirements, to determine the most suitable ETp model, to validate the prediction results of ETp values ​​using direct measurements in the field, and to obtain information related to climate factors that have the most influence on the ETp rate. The measurement of the ETp value is based on the use of Merra-2 global climate data and climate data from field measurements. ETp models used are Blaney-Criddle, Hargraves, Remanenko, Penman and Penman-Monteith. Research results showed that the ETp rate in Wajo District tended to increase during the July-October period indicating an increase in water demand in crop cultivation. The most influential climate parameter in determining the Penman Monteith ETp rate is solar radiation. The results of the ETp analysis using global data show that the Penman model is the closest model to the Penman Monteith ETp model from field measurements.