Reviewerly/exHarmony huggingface dataset:
THE SUPPORTING EFFECT OF CINEMA IN PEDAGOGICAL EDUCATION (THE 1986-1990 SOVIET PERIOD)

The research paper analyzes the influence of cinema on the process of forming a linguistic personality, its supporting effect in the practice of pedagogical education. Literary and musical works, theatrical productions, visual and photographic art are integrated into a film, complement each other and enhance their impact on a person. In addition to an entertainment function, cinema performs two more important functions: on the one hand, it educates the viewer and promotes actual ideological principles and dominant values of the society, and it quite objectively reflects the state and problems of the society. The first function, if we are speaking about films whose subject is the school, and the focus group is teachers and students, can be called pedagogical, since it reflects the relationship and mutual influence of cinema and school, and the second function is actualizing. Cinematic works popularize a certain behavior and a specific ideology. Due to their influence on young people, works of cinematic art can contribute to the spread of moral behavior, or, on the contrary, promote deviant behavior as a norm. Representatives of the older generation face a difficult task, i.e. to instill moral and ethical norms and values to the younger generation. During periods of rapid changes in a society, the pedagogical and actualizing functions are in contradiction with each other, since the ideology and the moral and pedagogical norms have not changed yet, but the structure of dominant cultural values has already been violated. In this case, in order to achieve the desired pedagogical effect from watching the film, further efforts are required from the teacher to clarify and discuss the problems raised in the film. The authors’ conclusion is that films of the 1986-1990 period, on the one hand, educate the viewer, and on the other, reflect the state and problems of the society, and here we can talk about a hidden educational effect, since the viewer himself must answer the question, whether it is right to do so or not. During the research it was revealed that such values as responsibility / independence, personal formation, selfdevelopment, openness to dialogue, purposefulness, appeal to reason (and not to feelings) are clearly formed in the films produced in 1986-1990.
Dynamic Hybrid Fragmentation Method for Multimedia Databases

Multimedia databases store high-volume data, which causes problems in efficient information retrieval, and increases execution costs and response times of the queries. To solve this problem, data fragmentation techniques exist to improve query performance, increase information availability, and efficiently execute more operations accessing less irrelevant data. This article presents a comprehensive review of 34 methods related to hybrid fragmentation and subsequently proposes the design of a hybrid fragmentation method that adapts the scheme according to workload changes to maintain efficient retrieval of multimedia data. The proposed technologies are Java as a programming language, Java Server Faces (JSF) as a framework, MySQL and MongoDB database management systems, and NetBeans as an Integrated Development Environment (IDE), following the UWE methodology (Unified Modeling Language-based Web Engineering).
An Adaptive Fuzzy Ensemble Model for Facial Expression Recognition Using Poplar Optimization and CRNN

Facial expression recognition (FER) is one of the trending topics in the research field in recent years. Facial expressions are the direct way in which humans communicate their emotions and identifying these emotions requires complex cognitive skills. Because of continual advances in image processing and machine learning techniques, several novel strategies are presented each year. Their performance still needs to be enhanced, and it is a challenging task. So, we aim to propose a novel method called Ensemble Convolutional Recurrent Neural Network (CRNN), and this model is proposed to enhance the prediction accuracy of facial expressions. The methods such as GRU, LSTM, and CNN prediction outputs are incorporated. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is constructed as the ensemble model's topmost layer to adequately examine the few sub-models. The final output is predicted by the fuzzy inference system. The facial Recognition Dataset and EMOTIC dataset are utilized in the method for evaluating the results. The various performance metrics are utilized to test the efficiency of the proposed model such as precision, False Positive Rate, F1-score, True Positive Rate, and accuracy. Our proposed method achieves 99.52% for precision and 99.35% for F1-score. Compared with other existing such our proposed method achieves 99.75% and the value of the Area under the ROC curve (AUC) is 0.95.
Construction of intelligent network anti-risk index evaluation system based on machine learning

With the growth of the Internet, network attack means emerge in endlessly, and internet safety issues become more and more important. Anti-virus software and firewalls generate a large amount of alarm information. Faced with such a huge amount of data, network managers can hardly grasp the security status of the network system and cannot take corresponding preventive measures in time. In order to improve the data security transmission performance of the network, it is needy to evaluate the anti-risk index of intelligent networking. At present, most of the internet safety assessment methods are from the perspective of security vulnerabilities. By scanning whether there are some known vulnerabilities in the network, the corresponding assessment results and solutions are given. In this paper, an intelligent network anti-risk index assessment system based on improved machine learning algorithm is proposed. Through this system, the internet safety situation is predicted, and then the algorithm model of internet safety assessment decision system is constructed by using assessment rules. The simulation results show that this method has high accuracy in evaluating the anti-risk index of intelligent networking, strong ability in detecting intrusion information, and improve internet safety.
The Analysis of Source Culture in The Current EFL Textbooks in Indonesia

The aim of this study was to investigate the culture values represented in an English textbook provided by the Ministry of Education and Culture in terms of the type of culture values represented. This is a qualitative research, the data were collected in the form of the reading passages in the textbook. The illustrations that accompany the text analyzed if it assists the interpretation of the meaning to collect data and data analysis in this research was descriptive analysis in order to reveal what cultures values are represented in the textbook, the contents in the reading passages were categorized into the source culture, the target culture, the international culture.&#x0D; The result of this research was found that there was a significant on the findings and discussion the findings and discussion, it can be concluded that the result from analyzing the text book focus on 45 the reading passages, there are 9% that present source culture (SC), 18% present target culture (TC), 26% present international target culture (IC), and until 47% culture free(CF) dominate the cultural content on the textbook “Interlanguage for Second Grader Students of Junior High School by Joko Priyana”.
Kemampuan Mengidentifikasi Konjungsi dalam Cerita Rakyat “Terjadinya Lau Kawar” Pada Siswa Kelas VIII SMP PAB 5 T.P 2022-2023

Conjunctions or connecting words are categories that connect words with words, clauses with clauses, or sentences with sentences or between paragraphs and paragraphs. Conjunctions have many types and functions that are divided, this causes students to not understand the division. Folklore is a cultural heritage of the past that has begun to be forgotten. The purpose of this study is to describe students' ability to identify conjunctions in folklore The occurrence of Lau Kawar in class VIII SMP PAB 5. This research method uses descriptive research with a quantitative approach. The population in this study were 114 grade VIII students, and the sample used was 28 students. The conclusion obtained from this study is the ability of students to identify conjunctions in the Folklore of Lau Kawar by using a description test by class VIII 3 SMP PAB 5 Academic Year 2022-2023 with an average value of 70.71 rounded up to 71 with a standard deviation of 10, 51. The average value is in category B "good".
Utilization of Reading Literacy Carts (GELISBA) to Interest in Reading to Support the MBKM Program at Bunda Pusaka Preschool

Reading as a skill views the nature of reading as a process or activity that applies a set of skills in managing what is read that captures meaning. Developed countries are countries where people's interest in reading is high. Therefore, interest in reading occupies an important position for the progress of a nation. Therefore, in order to increase the reading interest of students at the Early Childhood and Kindergarten levels in Sungai Pauh Pusaka even better, we are holding a work program that is an urgency for PKM activities in the form of procuring Reading Carts which we call Reading Literacy Carts (GELISBA). The objectives in community service activities are as follows: (a) Helping to increase public knowledge by reading various useful information; (b) Helps increase a person's level of understanding in drawing conclusions from the information read; (c) Improve a person's ability to provide critical assessment of a written work; (d) Helping to grow and develop good character within a person; (e) Increasing the value of one's personality through reading and writing activities (f) Fostering and developing a culture of literacy in the midst of society at large; (g) Helps improve the quality of one's time use so that it is more useful. The method of service activities uses the observation method or direct observation. Results of service activities This positively impacts teachers and students who can utilize the Reading Literacy Cart (GELISBA) as a learning medium
Big Data Platform as a Service for Anomaly Detection

Multipurpose Computation is reached with the platform as a service with container orchestration (PaaSCO) which integrates containerized technologies in a package-based style. These platforms include Kubernetes, OpenShift, Distributed Cloud Operating System DC/OS, Cloud Foundry, and Docker Swarm. Using a PaaSCO facilitates and accelerates the construction of platforms for developing applications by unifying components and software frameworks to build applications for different use cases. Containers enable ubiquity, portability, and distributed computing capabilities, as well as the programming and development of different applications and services to engineering and sciences areas like the internet of things, software development, data analytics, microservices, and artificial intelligence. This means that the same solution can be deployed in different PaaSCO environments including public, private, and hybrid clouds to obtain the same service. The National Laboratory of Information Technologies of the Autonomous University of Ciudad Juárez (LaNTI) offers computing infrastructure services to researchers inside and outside the institution. Due to their lack of expertise in infrastructure and computing tools, most researchers have difficulty installing and configuring the software tools necessary for their research. We present a solution using the PaaSCO Distributed Cloud Operating System (DCOS) through the integration of a stack of components required by a Big Data architecture. By implementing the solution proposed in this work, we are contributing to the academic and research environment by accelerating the generation, and implementation of components required by the Big Data Analytics system. Using containerized tools makes it easy for researchers to focus on the substantive part of their research. In addition to allowing data analytics tasks with a focus on anomaly detection, two experiments demonstrating resource elasticity and isolation are presented to demonstrate the platform additional benefits.
Redescending M-Estimators Analysis on the Intuitionistic Fuzzy Clustering Algorithm for Skin Lesion Delimitation

This study has conducted an investigation of the Redescending M-Estimators as alternative to strengthen an Intuitionistic Fuzzy C-Means algorithm against atypical information. In this regard, the objective function was stated taking into account the loss function, while the update expressions for membership matrix and prototypes vector were formulated by a derivative procedure, depending on the influence functions. The evaluated estimators were Huber Skipped Mean, Simple Cut, Tukey Biweight, Hampel’s Three Part Redescending, Andrew’s Sine, German-MacClure, Lorentzian, Asad-Qadir, Insha and Alamgir. The empirical study was performed using the ISIC 2017 dataset in order to make the skin lesion delimitation task, these images had inherent artifacts that were considered as atypical information; the performance was quantified by popular state-of-the-art metrics. The quantitative results show an outstanding performance of the Hampel’s Three Part Redescending with Jaccard Similarity Coefficient $$\,=0.905\pm 0.054$$ , Dice Measure $$\,=0.910\pm 0.060$$ , Misclassification Ratio $$\,=\,7.174\pm 0.864$$ and Hausdorff Distance $$\,=6.281\pm 0.804$$ , in contrast all rest estimators.
FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices Using Divide and Collaborative Training

In Federated Learning (FL), the size of local models matters. On the one hand, it is logical to use large-capacity neural networks in pursuit of high performance. On the other hand, deep convolutional neural networks (CNNs) are exceedingly parameter-hungry, which makes memory a significant bottleneck when training large-scale CNNs on hardware-constrained devices such as smartphones or wearables sensors. Current state-of-the-art (SOTA) FL approaches either only test their convergence properties on tiny CNNs with inferior accuracy or assume clients have the adequate processing power to train large models, which remains a formidable obstacle in actual practice. To overcome these issues, we introduce FedDCT, a novel distributed learning paradigm that enables the usage of large, high-performance CNNs on resource-limited edge devices. As opposed to traditional FL approaches, which require each client to train the full-size neural network independently during each training round, the proposed FedDCT allows a cluster of several clients to collaboratively train a large deep learning model by dividing it into an ensemble of several small sub-models and train them on multiple devices in parallel while maintaining privacy. In this collaborative training process, clients from the same cluster can also learn from each other, further improving their ensemble performance. In the aggregation stage, the server takes a weighted average of all the ensemble models trained by all the clusters. FedDCT reduces the memory requirements and allows low-end devices to participate in FL. We empirically conduct extensive experiments on standardized datasets, including CIFAR-10, CIFAR-100, and two real-world medical datasets HAM10000 and VAIPE. Experimental results show that FedDCT outperforms a set of current SOTA FL methods with interesting convergence behaviors. Furthermore, compared to other existing approaches, FedDCT achieves higher accuracy and substantially reduces the number of communication rounds (with 4-8 times fewer memory requirements) to achieve the desired accuracy on the testing dataset without incurring any extra training cost on the server side.
Analyze of the supply chain management advantages of Tesla and the impact of the epidemic

In contemporary society, where the energy crisis is becoming more and more serious, the energy transition of automobiles is imminent. By studying the background of Tesla, its production strategy, battery technology, supply chain, marketing network, and operation mode under the epidemic, combined with the comparison of traditional car companies, this paper hopes to provide relevant suggestions and improvement methods to the energy transition of traditional car companies, which can make the electric vehicle industry develop more rapidly.
Hungry Hub

The "Hungry Hub" is an application which provides a feature which helps in creating an efficient and user friendly Catering application. This system will solve key problems faced catering services, and also the clients, through the use of technologies such as Web applications. Hungry Hub accomplishes this by providing two interfaces for the two types of people - a web application for customers and a web application for catering staff members. The web application allows customers to have a seamless experience with features such as viewing catering services and reviews, booking an item or full meal packages, leaving a review, and also viewing confirmation details. The platform will provide a range of features to help clients to order food items based on the packages and plans given in the application and it also enables communication with the catering company. In addition to those features, we’ll also have a feature to donate the remaining / leftover food to nearby orphanages.
Complexity Science

There contains much wonderful fractal art in beautiful music, especially in the classics, in which the pitch and rhythm have fractal features. The noise of different frequencies in the pitch is mainly divided into white noise, red noise and pink noise.
Cluster Analysis Using k-Means in School Dropout

Traditional measurements of university students are not providing the data needed for adequate guidance of the students. The evidence for this is the constantly growing problem of student dropout. This paper presents a comparison of selected prediction algorithms to detect students at risk of becoming inactive at the end of their first semester. Our proposal uses data from previous academic years to produce a dataset with the reports from the school performance of the students. Additionally we used Random forest, J48 decision tree, and Logistic regression. Our main approach is to implement k -means algorithm to split the database into subgroups of classes and compare it with traditional applications to evaluate each case. The dataset uses school performance of the student from 2017 to 2019 to make a prediction about the chance of a newcomer student to drop out of school during the start of their academic year. The results show that Random forest algorithm reaches a better performance than other algorithms. We show that applying our proposed clustering-based processing, our algorithm flagged as potential drop outs, 80% of those who actually did drop out. In conclusions section we discuss the effectiveness and propose a future work.
Determining Mathematics Subject Materials For Learning Media Using Fuzzy Mamdani Based on Initial Evaluation Criteria

The amount of government funding for education has not been able to increase the knowledge skills of Indonesian students in mathematics and science. Using game technology as a learning medium can improve students’ abilities. This study proposes learning media for geometric mathematics educational games that run on mobile phone applications equipped with initial evaluations for players as an alternative learning media that adapt students’ initial abilities. We use the fuzzy Mamdani method to select math material based on the player’s initial ability score. Testing performs function tests in games using the black box method. The black box test results show that the supporting and main functions have a 100% pass rate.
Response to UK IPO Open Consultation on Artificial Intelligence and Intellectual Property: Patents (Sept-Nov 2020)

A comment on the idea of granting patents to AI as an inventor, assuming AI can pass the threshold of inventiveness required under patent law. The submission raises doubt that disclosure theory could justify awarding patents to AI, and calls on the UK IPO to look for empirical evidence that potential patentees are not investing in R&D of AI inventions.
Blockchain Voting: A Comparative Analysis

Abstract: The electronic voting has emerged over time as a replacement to the paper-based voting to reduce the redundancies and inconsistencies. The historical perspective presented in the last two decades suggests that it has not been so successful due to the security and privacy flaws observed over time. This project is about decentralizing authority to record, count and verify votes and the voters, rather than having a central authority. We are using blockchain technology which is a distributed database. With its immutability property and decentralized architecture, it can run and support a voting scheme that is open, fair and independently verifiable. The project will be developed in Ethereum framework which primarily uses Solidity as a language. Ethereum’s backbone is its decentralized virtual machine called Ethereum Virtual Machine. The application will be deployed on the thirdweb(web3.0). Overall this project is a potential roadmap for blockchain technologies developing in the nation to support complex applications. Keywords: Blockchain, Cryptography, Cryptocurrency, Ethereum Framework, Solidity, Decentralization, Hashing, Dapp, Web3, Digitalizing.
ANALISIS PENDEKATAN STATISTIK DAN FUZZY MAMDANI DALAM PREDIKSI PRODUKTIVITAS PADI

Indonesia has fertile soil, so it is suitable for agricultural land. However, Indonesia still needs to import rice from abroad because rice productivity in Indonesia is often inconsistent. Therefore, rice productivity in Indonesia needs to be estimated for the future. This study aimed to determine the amount of rice productivity from production factors and harvested area that occurred in Indonesia from September 2021 to June 2022. The data used were rice production and rice harvested from January 2019 to August 2021 sourced from the Central Statistics Agency, Indonesia. Data processing in this study uses the Polynomial Regression method to determine predictions of future rice production and harvest area and the Mamdani Fuzzy Logic method. Data processing in this study uses a prediction method, namely the Polynomial Regression method, to determine future production and harvested area predictions and the Mamdani method of Fuzzy Logic for decision making. The results obtained from the Mamdani polynomial and fuzzy regression methods, predictions of rice productivity in September 2021 to June 2022 have increased in the range of 23.33 to 27.91.Keywords: Fuzzy Mamdani, polynomial regression, prediction, rice productivityMSC2020: 62A86