Reviewerly/exHarmony huggingface dataset:
Attitudes of Media Students in Palestine towards Video Journalism Content Disseminated by Russia Today on the Arabic Channel's Page on Facebook

This research aims to identify the attitudes of media students in Palestine towards video journalism through the Russia Today in Arabic channel page on the Facebook platform. It also aims to investigate media students' opinions about the visual content presented by the Russia Today page by verifying the motives behind their follow-up to the channel's page on its interactive platform, in addition to revealing the cognitive aspects that students are impacted by as a result of their follow-up to the video journalism through the channel's page. The study can be categorized under quantitative approaches. Statistical analysis is one of the most appropriate scientific methods for such analytical studies. It helps in obtaining information, data, and descriptions of the phenomenon under study. Therefore, the current study came out with several results and recommendations; the media faculties in Palestine should work on developing the skills of media instructors to use contemporary technologies, and train students to tell stories through video journalism. There should also be diversity in the media content adopted by the Russia Today channel, and it should be expanded to cover all social media platforms. Moreover, it is essential to take advantage of experts and visual content makers on social media platforms to raise students' practical skills to produce stories using video journalism.
On nonlinear greedy randomized Kaczmarz methods for solving large-scale systems of nonlinear equations

Abstract The greedy randomized Kaczmarz (GRK) method is an efficient method for solving large-scale systems of linear equations. Based on this basic idea, we propose a nonlinear greedy randomized Kacz-marz (NGRK) method for solving large-scale systems of nonlinear equations and prove its convergence. Then, to improve the computational efficiency of the NGRK method, we design the multi-step NGRK (MNGRK) method and the accelerated NGK (ANGK) method, respectively. Theoretical results show that the convergence speed of the MNGRK and ANGK methods is faster than that of the NGRK method. Finally, inspired by the max-distance coordinate descent (MDCD) method, we also establish the nonlinear max-distance Kaczmarz (NMDK) method and its accelerated version.
Sistem Informasi Rekam Medis Berbasis Aplikasi Desktop untuk Daerah Pedesaan

Rural areas have several challenges that must be resolved. The availability of internet connections in the rural area is also one of the facilities that until now has not been evenly distributed. The clinical administration system is still conventional which has many weaknesses such as data processing, time management, and requires document storage space. So that the conventional system reduces the quality of clinical health services in the area. Therefore, this research was made with the method used is the waterfall model which aims to solve the challenges in making information systems. The advantage of using this method is that it is easy to use, directed, structured, and good for optimization. In this research, the information system created is in the form of a desktop application and is made using the Python programming language and MySQL database as a data storage area. This system is able to store personal data and patient history data according to the required information. In addition, this clinical information system is able to process data quickly and accurately, minimize lost data, search for the required data quickly, make patient data reports quickly and neatly, the system can be accessed by users even though there is no internet signal, and can reduce use of paper and reduce the use of space for patient data document storage.
A review on image classification using deep learning

Classifying images helps computers identify more things that humans can see. to identify the picture in the same way a person would. Despite widespread use, issues with the current conventional system's categorization accuracy, adaptability, and impact remain unsatisfactory. Therefore, deep learning has been suggested for image categorization as a means to both address these issues and boost accuracy. In this article, we introduce the fundamental ideas behind picture classification using deep learning, review the several algorithms that may be used for this task, and then examine the benefits of utilizing deep learning for this task, as well as the numerous applications that make use of this technique.
Multi-organ Segmentation Based on 2.5D Semi-supervised Learning

Automatic segmentation of multiple organs is a challenging topic. Most existing approaches are based on 2D network or 3D network, which leads to insufficient contextual exploration in organ segmentation. In recent years, many methods for automatic segmentation based on fully supervised deep learning have been proposed. However, it is very expensive and time-consuming for experienced medical practitioners to annotate a large number of pixels. In this paper, we propose a new two-dimensional multi slices semi-supervised method to perform the task of abdominal organ segmentation. The network adopts the information along the z-axis direction in CT images, preserves and exploits the useful temporal information in adjacent slices. Besides, we combine Cross-Entropy Loss and Dice Loss as loss functions to improve the performance of our method. We apply a teacher-student model with Exponential Moving Average (EMA) strategy to leverage the unlabeled data. The student model is trained with labeled data, and the teacher model is obtained by smoothing the student model weights via EMA. The pseudo-labels of unlabeled images predicted by the teacher model are used to train the student model as the final model. The mean DSC for all cases we obtained on the validation set was 0.5684, the mean NSD was 0.5971, and the total run time was 783.14 s.
Semi-supervised Detection, Identification and Segmentation for Abdominal Organs

Abdominal organ segmentation is an important prerequisite in many medical image analysis applications. Methods based on U-Net have demonstrated their scalability and achieved great success in different organ segmentation tasks. However, the limited number of data and labels hinders the training process of these methods. Moreover, traditional U-Net models based on convolutional neural networks suffer from limited receptive fields. Lacking the ability to model long-term dependencies from a global perspective, these methods are prone to produce false positive predictions. In this paper, we propose a new semi-supervised learning algorithm based on the vision transformer to overcome these challenges. The overall architecture of our method consists of three stages. In the first stage, we tackle the abdomen region location problem via a lightweight segmentation network. In the second stage, we adopt a vision transformer model equipped with a semi-supervised learning strategy to detect different abdominal organs. In the final stage, we attach multiple organ-specific segmentation networks to automatically segment organs from their bounding boxes. We evaluate our method on MICCAI FLARE 2022 challenge dataset. Experimental results demonstrate the effectiveness of our method. Our segmentation results currently achieve 0.897 mean DSC on the leaderboard of FLARE 2022 validation set.
Improved Support Vector Machine (SVM) Performance on Go-Jek Service Review Classification Using Particle Swarm Optimization (PSO)

The choice of parameters in the classification process using the Support Vector Machine significantly affects the optimization of the results. Continuing research that has been done before, by optimizing SVM parameters using genetic algorithms that produce accuracy up to 86%. From the experimental results, it has been proven that genetic algorithms can be used to optimize parameters in SVM, but the number of iterations in GA is quite a lot and requires a longer computational time to find a solution. There is a more popular optimization method, PSO. Where PSO has a more efficient calculation than the Genetic Algorithm. For this reason, this research will apply PSO as an optimization method for SVM parameters as a classifier. PSO-SVM performance can improve the previous accuracy using GA-SVM. The results of the test are carried out with accuracy, and the F1 score test to determine the results of the application of the PSO Algorithm in SVM. Furthermore, it was compared to the accuracy of the kernel, which is the best. At this stage, it can be concluded that the accuracy of PSO-SVM increased to 87% in the Poly kernel with faster computing time.
The scientist’s dignity as a path to the truth

The article is devoted to the 90th anniversary of Academy member Vladislav Alexandrovich Lectorsky, his scientific views and personal qualities. On the basis of acquaintance with his works and many years of personal communication, an attempt is made to comprehend the reasons and conditions for the fruitful life in science, which allowed him to become a universally recognized classic of Russian philosophy during his lifetime and a recognized worldwide authority in the field of epistemology and philosophy of science. The mechanism of interaction of scientific inclinations and interests with personal inclinations and ethical principles of a scientist, which determined the choice of life path and direction of scientific research, is considered. There is a tendency to expand the scope of research from special problems of the theory of knowledge to generalizing concepts of philosophical epistemology, philosophy of consciousness, philosophical anthropology, encompassing cognition, man and culture in a holistic worldview system. There is a pronounced social orientation of epistemological research. The way of formation of a scientist’s personality and his civic position in the process of solving research tasks is shown – from the analysis of cognition as an end in itself to the application of the concepts and principles developed in this process to the philosophical understanding of a person, his place and destiny in a modern transforming society, the protection of rationality and humanism as values of civilization. The talent of V.A. Lektorsky as an organizer of science, who worked for a long time as the head of the theory of knowledge department and editor-in-chief of the journal “Voprosy filosofii”, the current professor and dean of the Faculty of Philosophy of GAUGN and editor-in-chief of the journal “Philosophy of Science and Technology” is noted. It is shown how the theoretical concepts of tolerance, critical thinking, and humanism developed by him are at the same time practical principles of his own life. This is the sign of a real philosopher, not just a researcher in the field of philosophy.
Lektorsky on dialectical materialism and enactivism

The article analyses V.A. Lektorsky’s views on the debate on constructivism and realism. On the one hand, it considers the history of development of constructivism and realism as philosophical positions embedded in the evolution of European philosophical tradition. On the other hand, the changes in V.A. Lectorsky’s views on constructivism and realism are traced from dialectical materialism to post-Marxist variant of realism. The latter is built on the basis of analysis of cognitive science and recognition of limitations of the so-called computational model of cognition, as well as on the explication of epistemological consequences of enactivism, a concept emphasizing significance that the physical body of a knowing subject actively inscribed in the cognizing environment, i.e. interacting with it, has for cognitive processes. The article discusses the two distinguished stages in Lectorsky’s work on epistemological realism, reconstructs its genesis, discusses and evaluate the arguments that Lectorsky presents in support of his conception so-called “constructive” or “activity” realism.
Manage theory in the Russian educational literature

One of the most important reasons for the crisis state of modern Russia is the low level of managerial training of government officials. The obvious way to overcome this situation is to improve the quality of education of graduates of relevant universities. The author, considering the theory of manage as a generalizing discipline of the whole complex of subjects focused on the training of qualified managers, focuses on the educational literature in this area. Based on a detailed analysis of the content of one of the typical-the article shows that it is a collection of haughtily organized material that has little in common with the theory of manage, but is quite suitable for the formation of the notorious “competencies” introduced into the educational process by the Ministry of Science and Higher Education of the Russian Federation. However, the main thing - the actual theory of manage - is not in the textbook. According to the author, the content of almost all educational publications on manage theory completely coincides with the nature and general orientation of the higher education system-the research institute of modern Russia, namely, the imitation of the educational process. Does this approach meet the needs of modern Russian society?
Evolutionary Processes in the Italian Language of the Internet: Vocabulary and Word Formation

The article is devoted to the evolutionary processes in the Italian language of the Internet, which are directly revealed at the lexical and word-formation levels. The article gives the general assessment of these processes from the point of view of a number of significant structural and sociolinguistic parameters in the context of the historical development of the Italian language. This research is based on the opposition of the language of the Internet to the structural norm of the Italian language; it is noted that the opposition is of slang nature. The key trend, as the author emphasizes, is the convergence of the oral and written forms of speech reflected in the used and normatively fixed forms of word-formation, in which traditional models as well as mixed polycodes (alphanumeric and/or alpha pictorial) are extensively used. Studying the media communication influence on the development of the Italian language, the author relies on the classification of the functional parameters of the opposition − convergence of written and oral forms of speech − by the Italian linguist G. Berruto. The main notions, which characterize such evolution, − diamesia, diastratia, diatopia, diafasia are studied. G. Berruto’s concept is developed by another Italian linguist G. Antonelli who singles out the language of the Internet communication as the culminating point of evolution. The article studies a large number of language examples revealing the picture of word-formation in the Italian language of the Internet. Word-formation is interpreted as the main factor in the normative evolution of the Italian language in the Internet communication. Particular attention is paid to contaminated word-formation methods, creolized forms of a language sign (contraction, pictogram), which are typical of the Internet language, as well as unregulated use of punctuation marks, and spelling violation. The article shows slang forms of lexical speech usage and word-formation in the Italian language of the Internet. The structural-linguistic and semiotic interpretation of the Italian Internet word-formation is given. The article carries out the rubrication of models and types of word-formation in the Italian language of the Internet. The article is suggested to specialists who are interested in semiotics of the Internet language, modern development trends of the Italian language, the peculiarities of Italian lexicology and word-formation.
PRODUCT CLUSTERING IN THE MSME BUSINESS OF GROCERY STORE

The business world is an exciting world to follow due to its dynamic and competitiveness. Sri Wahyuni Grocery is one of the MSMEs involved in buying and selling daily-basis needs. The business can manage an average of 115 transactions in a day, including various transactions for necessities. Most products purchased at this grocery shop are daily basic needs, such as sugar, tea, instant food, snacks, and fuel oil. It is known that from observations, some products are less desirable by buyers so they are not selling well and some products are selling well, so it is necessary to do a product grouping process to find out how to group these products. An analysis of existing data is needed to obtain information with the K-Means Clustering algorithm. This research aims to determine the pattern of transaction data owned by Traditional Grocery Store MSMEs and form a Clustering pattern of products offered by Traditional Grocery Store MSMEs. Through the data exploration process, this research will carry out a pattern search from transaction data and clustering patterns owned by Traditional Grocery Store MSMEs. Based on the findings from the research conducted, business actors will be able to develop various strategies to improve services to sales by relying on the use of various data mining algorithms. The research was conducted on Traditional Grocery Store MSMEs with transaction data for two months, and the research carried out data exploration to determine clustering patterns using the CRISP-DM method.
DETECTION OF ETHNO-LINGUAL IDENTITY USING ARTIFICIAL INTELLIGENCE, MACHINE LEARNING AND VOICE ANALYSIS TOOLS: INTRODUCING “AUTOMATED CRIMINAL ETHNICITY IDENTIFICATION SYSTEM” (ACEIS)

Voice evidence is also known as voiceprint like fingerprint, it has been proven to substantiate the findings. Voiceprint is a dissimilar character for different people. In forensic science sometimes, we come across cases where the suspect’s or victim’s ethnicity has to be identified using various number of identification factors like voice, physical and anthropological features etc. In such cases the examination of an individual’s ethnicity may be identified using the other available identification factors but when it comes to the Ethno-Lingual identification then examining the individual’s language for the same and that too without any digital tool, i.e., doing it manually, becomes a sturdy task for the examiner. The database of the voice samples of Hindi, English and Mother language has been successfully created by the authors which is named as the “Automated Criminal Ethnicity Identification System” (ACEIS). In this paper, the author has summarised the various studies conducted on the ethno-lingual identification and their acquisition. Based on the studies, it was concluded in the review that the use of Artificial Intelligence and Machine Learning was used in prior studied but in India it hasn’t been done yet. When known samples were analysed for their ethnicity, we noticed that an 80% matching was there among the samples belonging from same ethnicity. This matching-percentage was calculated on the basis of Pitch, Amplitude, Formant Frequencies, Frequencies and the average time taken to speak a word/letter etc.
Analysis of Twitter Sentiments About the Russian-Ukraine War Using Naive Bayes Based on Particle Swarm Optimization

The Russia-Ukraine crisis has not found a solution until now, at least until November 2022. Many Indonesians have expressed their opinions on this matter via social media Twitter. This study uses the Naïve Bayes (NB) Algorithm based on Particle Swarm Optimization (PSO) to analyze opinions regarding the RussiaUkraine war. Data was taken from Twitter using the keywords "Russia Ukraine", "Russia vs Ukraine", "Russia-Ukraine", and "Perang Russia". The number of data sets taken is 5000 data. The results showed that the accuracy of the Naïve Bayes (NB) algorithm without Particle Swarm Optimization (PSO) was 67.72%, the precision value was 58.33%, the recall value was 79.75%, and the error rate of 57.14% while Naïve Bayes (NB) with Particle Swarm Optimization (PSO) the accuracy obtained is 73.48%, the precision value is 65.62%, the recall value is 76.36% and error rate of 50.36%. Thus it can be said that the Particle Swarm Optimization (PSO) applied to the Naïve Bayes algorithm for objects in research can increase the accuracy of results.
RFID Based Automated Library Management System

Abstract: Historically, libraries have employed barcode-based book management systems for book identification, self-check-in, anti-theft, inventory control, and book classification. The process is manual. We suggest an RFID-based library management system that allows for quick transaction processing and streamlines the borrowing and returning of books from libraries with minimal manual labour. The suggested system has the ability to store data electronically that can be read by a reader. Using RFID tags, the system can issue and receive books. It can also use library databases to compute relevant factors like due dates, renewal dates, and fines.
Digital competence of science teachers in terms of gender, length of work, and school levels of teaching

Technological development in education should be balanced with digital competencies to create optimal education. This development has increased during the pandemic, which requires teachers to master digital competence to organize education for students. This study aims to determine the digital competence of science teachers and investigate differences in gender, length of work, and school levels of teaching. This study employed a cross-sectional survey and involved 105 science teachers in West Java Province, Indonesia. Online questionnaires were used to collect the data. This research has revealed that science teachers have different digital competence at the school level. In contrast, they do not have divergent digital competence regarding gender and length of work. Teachers with good digital competence can develop collaborative learning. Keywords: Education, Digital Competence, Science Teacher
The serious fallout from information compression

The serious fallout from information compressionConcern for the backlash of negative reviews encourages physicians to prescribe interventions their patients believed they need.
PERCEPTION OF ENGLISH TEACHERS RELATED TO TEXTBOOK PROVIDED BY GOVERNMENT AS A PRIMARY TEACHING MATERIAL

English textbook from government is issued for all the Indonesian students in junior high school. It should be used as the primary teaching material in English class. In fact, the English teachers in SMPN 4 Baubau meet some difficulties in applying that policy. The objective of this research was to find out the perception of English teachers related to textbook provided by government as a primary teaching material. This research used descriptive qualitative method with design case study. The sample of this research was three English teachers in SMPN4 Baubau. The findings showed the teachers did not use the government’s textbook as the primary but it was employed as the supporting teaching material and the teachers used other books from non-government publishers as the main textbook.