Reviewerly/exHarmony huggingface dataset:
Peranan Kreativitas Guru terhadap Motivasi Belajar Peserta Didik Sekolah Dasar

This study aims to determine the role of an elementary school teacher on students' learning motivation. One of the elements that influence the success of a student is motivation. If students have the drive to learn, then they will achieve the desired learning outcomes. In an effort to produce quality learning and education, teachers must be creative. To encourage motivation to acquire the principles of the creed, the teacher must be able not only to understand the teaching material but also various learning methods and designs. The findings show that teachers can increase students' motivation in learning in various ways, including by providing teaching materials, using various teaching techniques, managing classes, and utilizing learning media. High learning motivation will lead to the best results, the task of a teacher is to foster student motivation. The research methodology in research is a literature review (library research) available in online or offline libraries. The main component in learning, motivation can support and encourage learning. According to literature findings, students with high levels of motivation are more likely to try hard, dress impeccably, and not want to give up. They are also more likely to engage actively in reading to improve learning outcomes and solve problems. On the other hand, people who are unmotivated, appear disinterested, get discouraged easily, and don't pay attention to what they are learning will have difficulty in school.
Blockchain and Supply Chain Management: The Future of Trust and Transparency

Abstract: Supply chain management involves the coordination of various parties in the production, handling, and distribution of goods. Blockchain technology offers a new approach to managing the supply chain by enabling the creation of secure, decentralized, and transparent networks for tracking and verifying the movement of goods. This can help improve supply chain efficiency, reduce the risk of fraud and counterfeiting, and provide greater visibility and traceability throughout the entire supply chain. With its ability to facilitate real-time tracking, smart contracts, and secure data sharing, blockchain technology has the potential to revolutionize the way supply chains are managed.
Preface Lekantara Annual Conference on Engineering and Information Technology (LiTE)

Lentera Akademika Nusantara (Lekantara), an Indonesian scientific publisher, organized Lekantara Annual Conference on Engineering and Information Technology (LiTE) on November 30, 2021 held fully virtual. The conference aims to exchange knowledge and research finding among academicians, researchers, professionals, policy makers, and postgraduate students. The Awareness to increase the number of publications on the results of research that has been done deserves appreciation from all parties. Thus, Lekantara provides motivation and space for researchers to disseminate their research and accommodate their results of research. Lekantara Annual Conference on Engineering and Information Technology (LiTE) is one out of six conferences held by Lekantara in 2021. Lekantara Annual Conference on Engineering and Information Technology (LiTE) was chosen to be implemented virtually due to the Covid-19 pandemic that is still spreading. The event is virtually implemented with a model that all invited speakers are given time to present their material for 30 minutes every invited speaker and after that a question and answer is carried out with the participants with a direct questioning system, through chat forums and Q&amp;A forums provided by the zoom application. Overall, the conference took 5 hours the number of participants who joined the zoom room was recorded at participants. Participants came from few countries, namely Indonesia, Malaysia, Vietnam, Philippine, United States of America, UAE, and United Kingdom. Lekantara Annual Conference on Engineering and Information Technology (LiTE) is implemented with the support of a stable internet network system and a zoom application. In the implementation there were several technical obstacles encountered by the participants, namely the difficulty of joining the zoom application due to the unstable internet signal. The holding of a virtual conference felt less meaningful, due to the lack of interaction between speakers and participants Lekantara Annual Conference on Engineering and Information Technology (LiTE) committee received 132 manuscripts and a total of 132 papers were presented and discussed. The papers were authored by researchers from Indonesia, Malaysia, Vietnam, Philippine, United States of America, UAE, and United Kingdom All papers have been scrutinized by a panel of reviewers who provide critical comments and corrections, and thereafter contributed to the improvement of the quality of the papers and scope. 91 papers were selected and eligible to be published in the proceeding. We sincerely express our gratitude to the international/national advisory committee, presenters, organizing committee members, session chairs, all members of organization, participants, contributors and all the members of Lekantara Annual Conference on Engineering and Information Technology (LiTE) . Last but not the least, we are thankful to IOP JPCS Conference Series for producing the proceeding. List of Organizing Committee is available in this pdf.
PEMBUATAN PETUNJUK TEKNIS ASPEK MEKANIS KARYA ILMIAH UNTUK MENINGKATKAN TARAF LITERASI MAHASISWA

The purpose of this writing is to find out how to make technical guidelines for the mechanical aspects of scientific work to increase student literacy levels. There are two important aspects that must be considered, namely substantive aspects and mechanical aspects. The substantive aspect is related to the substance of the science being studied, while the mechanical aspect is the aspect that becomes the means of conveying the substantive aspect. This community service activity uses a focus group discussion (FGD) method or technique and is continued with socialization or training on how to write scientific papers based on technical guidelines for mechanical aspects which are elaborated into documents entitled Technical Guidelines for Mechanical Aspects of Writing Scientific Papers. The results of the dedication show that the writing mistakes that are often made by students in writing scientific papers are mechanical aspects which include spelling, punctuation, and grammar errors. In addition, there were also errors in the composition aspect caused by the lack of attention of students in reading the writing guide. With existing technical guidelines for scientific work, students are able to adjust their writing style according to thesis and journal writing standards so as to increase student literacy levels.
A Real-Time Video Surveillance for Rule Violation Detection on High Ways

Processing of images obtained with cameras is important for monitoring and/or securing highways. Early warning is important for highway safety in cases such as accident, traffic rule violation, automatic detec-tion of people or animals entering the road. For this purpose, besides the detection of objects such as vehicles and people, other moving targets or objects falling on the road, such as boards, etc., must be detected. For this, besides object recognition, motion detection and object tracking are also needed. The purpose of motion detection is to classify pixels in an image as background and foreground. Within the scope of this study, deep learning-based machine learning method was used for the detection of vehicles and people entering the highway, and classical image processing methods were preferred for the detection of any moving object other than these. In this context, a software infrastructure has been developed in which different processes can work together and share data with each other. The developed software was tested on the Nvidia Jetson development card, and algorithm performance and speed tests were carried out on the selected GPU supported Nvidia Jetson card.
End-to-End AutoML Implementation Framework

Automated machine learning (AutoML) has been an active research area in recent years. Researchers investigate the potential of AutoML as more stakeholders want to maximize the value of their data. The methods are designed to increase the effectiveness of machine learning (ML), accelerate model development processes, and make it accessible for domain experts that are not ML professionals. The systems without the aid of humans are feasible with AutoML, an area that has been increasingly studied recently. Even though efficiency and automation are two of AutoML's key points, a number of critical steps still require human involvement, such as understanding the characteristics of domain-specific data, defining prediction problems, creating a suitable training dataset, and choosing a promising ML technique. A comprehensive and updated analysis of the state-of-the-art in AutoML is presented in the study. AutoML techniques, including hyperparameter optimization (HPO), feature engineering, and data preparation are presented. As-is prediction structure and AutoML-based benchmark model are compared to show how to implement these methods. It is stated what a real end-to-end machine learning pipeline looks like and which parts of the pipeline have already been automated. Our AutoML implementation framework has been introduced and presented as a road map for the entire ML pipeline. Several unresolved issues with the current AutoML techniques are discussed. The obstacles have been outlined that must be overcome in order to achieve this objective.
Predicting the Number of People Exposed to Covid 19 with the Newton Gregory Maju Polynomial Interpolation Method

Since March 2020 until now the Corona Virus (Covid-19) is still running rampant and even new variants have appeared. The increase in people exposed to this virus can cripple the community's economy. The government must be able to anticipate the increasingly high spread of Covid-19. Therefore, a prediction model is needed for the number of people exposed to Covid-19 so that the government can anticipate it as a preventive measure. Research that has been carried out since early 2021 using the Monte Karlo, Arima, K-Nearest Neighbors methods, Time Series Analysis, Winter and Artificial Neural Networks. Furthermore, the researchers predicted the number of people exposed to Covid-19 using the Newton Gregory Maju interpolation method. The prediction results for the DKI Jakarta area in March 2022 are based on historical data for January - February 2022 with the smallest error of 2.17%, the highest error of 46.28% and the average error of 17.27%.
Entity Factor: A Balanced Method for Table Filling in Joint Entity and Relation Extraction

The knowledge graph is an effective tool for improving natural language processing, but manually annotating enormous amounts of knowledge is expensive. Academics have conducted research on entity and relation extraction techniques, among which, the end-to-end table-filling approach is a popular direction for achieving joint entity and relation extraction. However, once the table has been populated in a uniform label space, a large number of null labels are generated within the array, causing label-imbalance problems, which could result in a tendency of the model’s encoder to predict null labels; that is, model generalization performance decreases. In this paper, we propose a method to mitigate non-essential null labels in matrices. This method utilizes a score matrix to calculate the count of non-entities and the percentage of non-essential null labels in the matrix, which is then projected by the power of natural constant to generate an entity-factor matrix. This is then incorporated into the scoring matrix. In the back-propagation process, the gradient of non-essential null-labeled cells in the entity factor layer is affected and shrinks, the amplitude of which is related to the size of the entity factor, thereby reducing the feature learning of the model for a large number of non-essential null labels. Experiments with two publicly available benchmark datasets show that the incorporation of entity factors significantly improved model performance, especially in the relation extraction task, by 1.5% in both cases.
Compiling HTN Plan Verification Problems into HTN Planning Problems

Plan Verification is the task of deciding whether a sequence of actions is a solution for a given planning problem. In HTN planning, the task is computationally expensive and may be up to NP-hard. However, there are situations where it needs to be solved, e.g. when a solution is post-processed, in systems using approximation, or just to validate whether a planning system works correctly (e.g. for debugging or in a competition). There are verification systems based on translations to propositional logic and on techniques from parsing. Here we present a third approach and translate HTN plan verification problems into HTN planning problems. These can be solved using any HTN planning system. We collected a new benchmark set based on models and results of the 2020 International Planning Competition. Our evaluation shows that our compilation outperforms the approaches from the literature.
Penerapan Algoritma Ascent Hill Climbing Pada Game Edukasi Penyusunan Deretan Angka Puzzle-8

Number preparation using Puzzle 8 is a game that is very popular with children at this time, this educational game has very broad benefits for improving memory and thinking patterns. The 8 Puzzle Game Algorithm must have a clear step-by-step process in order to achieve a goal in the Search system. Logic Game 8 puzzles using the Ascent Hill Climbing Algorithm are often used in artificial intelligence. The problem that occurs in the research, the lack of understanding of children to find a suitable algorithm to arrange the 8 Puzzle game according to the principle of how an algorithm works. The game of compiling 8 puzzles must follow the rules that have been determined in order to obtain the correct algorithm so as to produce an arrangement that is in accordance with the initial data. The benefit of this research is that it provides a solution to complete the game of 8 puzzles according to the rules of the Ascent Hill Climbing Search Algorithm so as to produce a solution in the search method. Based on the search process, the final result of the puzzle game is in the correct position of 8 squares according to the initial rule, so the search is stopped and produces a goal.
Penerapan Model Pembelajaran Kooperatif Model STAD Untuk Meningkatkan Hasil Belajar Ekonomi Siswa Kelas XI MA

The problem in this study is the low student learning outcomes in the Economics subject of Class XI Students at MA Almunawwarah Bottoe, Barru Regency. The approach used in this research is descriptive qualitative with this type of research is Classroom Action Research (CAR) which has a cycle which includes planning, implementation, observation, and reflection. The research setting was carried out in Class XI with the research subjects consisting of teachers and 15 students in class XI. Data collection techniques and procedures used were observation, tests, and documentation. The data analysis used is qualitative. The results showed that there was an increase in activity in teacher learning and student learning activities as well as learning outcomes in Economics, the increase could be seen from each cycle, namely in cycles 1 and 2. The conclusion in this study was that through the application of the STAD model cooperative learning model could improve learning outcomes. students in Economics class XI MA Almunawwarah Bottoe Barru Regency.
Exploiting the Local Parabolic Landscapes of Adversarial Losses to Accelerate Black-Box Adversarial Attack

Existing black-box adversarial attacks on image classifiers update the perturbation at each iteration from only a small number of queries of the loss function. Since the queries contain very limited information about the loss, black-box methods usually require much more queries than white-box methods. We propose to improve the query efficiency of black-box methods by exploiting the smoothness of the local loss landscape. However, many adversarial losses are not locally smooth with respect to pixel perturbations. To resolve this issue, our first contribution is to theoretically and experimentally justify that the adversarial losses of many standard and robust image classifiers behave like parabolas with respect to perturbations in the Fourier domain. Our second contribution is to exploit the parabolic landscape to build a quadratic approximation of the loss around the current state, and use this approximation to interpolate the loss value as well as update the perturbation without additional queries. Since the local region is already informed by the quadratic fitting, we use large perturbation steps to explore far areas. We demonstrate the efficiency of our method on MNIST, CIFAR-10 and ImageNet datasets for various standard and robust models, as well as on Google Cloud Vision. The experimental results show that exploiting the loss landscape can help significantly reduce the number of queries and increase the success rate. Our codes are available at https://github.com/HoangATran/BABIES .
Efficient authentication method using binary search tree with multi-gateway in wireless IoT

IoT played a major part on the worldwide network. Maintaining security along with privacy of IoT nodes are the main challenges in IoT. Most of the authors had used different technologies for authentication like username, password, OTP, RFID, schemes implemented using a single factor, multifactor parameter which are discussed in the review section. But they continue to suffer from drawbacks like its adaptability to real-time applications that threaten users' data protection. Strong authentication mechanism is required, so user can get direct access of sensor information. To achieve it, our research concentrates on a unique authentication scheme. The goal of this research is to find whether an authenticated user is travelled through HGWN and FGWN, accessed registered sensor is used through HGWN and FGWN. The security attacks and data hacking are handled by the proposed technique with Binary Search Trees. Based on results, the proposed work outperforms than existent method.
A Comparative Study of Focus Constructions in Chinese and Korean: Focusing on Cleft Sentence

By comparing the representative cleft sentences in Chinese and Korean, this paper explores the cleft sentences of “shi......de”, the pseudo-cleft sentences of “...deshi...” and the cleft sentences of Korean “kes eun” corresponding relationship. Firstly, this paper studies the grammatical characteristics of “shi” and “de”, which are the constituent elements of Chinese cleft sentences, and their function of focus marking as focus constructions. Then, compare it with the Korean “kes eun” cleft sentence, and examine the similarities and differences of the two language cleft sentences. And then explore the relationship between the internal structure of focus constructions the two languages. Finally, the inner relationship between the two languages a t the grammatical level is explored to provide a theoretical basis for the realization of universal grammar.
TSVM-PI: transductive support vector machine based on privileged information learning

In semi-supervised classification tasks, there are often a large number of unlabeled data and unavoidable noise in the training dataset. Using the structural distribution information in the data or the privileged information of a feature to guide the classifier learning can make the model have better performance. Based on this consideration, this paper proposes a transductive support vector machine model using privileged information(TSVM-PI). The new model adds the regularization term of the privileged information guidance group on the basis of transductive support vector machine(TSVM), and uses the privileged information constraint term to constrain the slack variables. The introduction of privileged information can effectively improve the prediction accuracy of the new model, and make the model have a stronger anti-noise ability. In order to verify the classification performance of the algorithm, and data sets in artificial data set and UCI data set are used to conduct multiple sets of experiments. The noise was added to one of the UCI experimental data, and different proportions of noise group experiments were designed to explore the sensitivity of the model to noise. The experimental results show that the new model has better classification performance and is less sensitive to noise data than TSVM.
MENINGKATKAN HASIL BELAJAR MENULIS TEKS BERITA MENGGUNAKAN MODEL APTITUDE TREATMENT INTERACTION (ATI)

The purpose of this study was to determine the application of the ATI learning model in improving the ability of class VIII.A students to write news texts at SMP Negeri 4 Pagar Alam. This research method uses CAR. The action taken is the application of the Aptitude Treatment Interaction (ATI) learning model with the stages of preparation, implementation of actions, reviews and descriptions. This research was conducted in 2 cycles. In the initial test, the average score of students only reached 58.6 or only 9 people completed, while in cycle 1 the average score was 75.94. Furthermore, the value of students in cycle 2 was quite good, ie more than 85% of students got a score of 8.0 and the average acquisition reached 81.78, which was an increase of 97.2% classical completeness. Meanwhile, individual student scores have increased each cycle. Even though at the end of the evaluation there was still one student who scored &lt;7.0.
AN ANALYSIS OF THE ACTIVITIES IN ENGLISH TEXTBOOK (2017 REVISE EDITION) OF 11” GRADE SENIOR HIGH SCHOOL ENTITLED “STOP BULLYING NOW” PUBLISHED BY KEMENDIKBUD

This research was conducted to evaluate the english textbookcontent of 11th grade Senior high school (2017 Revise Edition) Entitled "STOP BULLYING NOW"published by KEMENDIKBUD.This study used descriptive qualitative research approach.The data source is 11th grade Senior high school english textbook and used the content analysisto evaluate the English textbook based on standard of KEMENDIKBUD.This study gathered the source data by using checklist instruments,which are consists of Textbook content,Presentation,Languange and Graphic Design.The data analysis resulted from the percentage of fullfilment scoreand gain 95% as the Good CategoryEnglish textbookand have been fulfilled the requirements of KEMENDIKBUD for eleventh grade senior high school.ThisEnglishtextbookEntitled"STOPBULLYINGNOW"itisaqualifiedbookbycontaingoodmaterialsbasedonMAGICConcepttheyareMotivating,AuthenticandAppropriate,GraphicandGraded,Interesting,InteractiveandIntegrated,ContextualizedandCreative.
Improving Disaster Management Capability and Disaster Resilience by Using Big Data: A Comparative Analysis between Korea and China

The purpose of this study is to verify the impact of using big data on the government’s disaster management capability and resilience and to grasp the relationship between them. Based on the questionnaires collected and analyzed in Korea and China, the results of the study showed that (1) Chinese citizens’ satisfaction with the government’s disaster management capability and disaster resilience were higher than Korean citizens’ satisfaction respectively; (2) using big data had a positive impact on the government’s disaster management capability, for China, specifically the prediction of COVID-19, the promotion of internal and external cooperation of the government. But for Korea, using big data had no significant impact on the promotion of internal and external government cooperation and on the government’s capability to respond to internet public opinion; (3) using of big data had a positive impact on disaster resilience, for China, especially in terms of public psychological recovery, post-epidemic government resumption of work and production policy. For Korea, while using big data had no significant impact on public psychological recovery.