Reviewerly/exHarmony huggingface dataset:
Collaborative Filtering Recommendation model Based on Graph Neural Network and Attention Mechanism

Abstract With the popularity and development of the internet and mobile terminals, people can access a lot of information through them every day. Recommender systems have become one of the important technologies for various online platforms that aim to predict whether a user will interact with an item or not. Among them, collaborative filtering-based models have made effective progress in learning user and item representations by modeling historical user-item interactions. Recently, models based on GCN have been effective in recommendation, and the main function of GCN models is to improve the embedding representation of users and items by iteratively aggregating feature information from neighbors using graph connectivity to extract additional information. However, in previous works, dividing users into subgraphs without intersection only leads to a partial loss of information, ignoring the potential connections that may exist between different groups of users; and because only users are divided, the influence of commodity factors on the purchase outcome at the time of purchase is ignored in the learning process. Based on the above considerations, in this paper we propose a message-passing recommendation model. The model uses intersects users and items in separate subgraphs and uses an optimized attention mechanism to obtain the final node embedding to optimize the embedding representation by introducing multiple embedding propagation layers that encode higher-order connectivity relationships. We conduct extensive experiments to evaluate the proposed model. The results show that our model can effectively improve the performance of the recommendation.
ANALYSIS OF GRAMMATICAL ERROR IN DESCRIPTIVE WRITING AMONG STUDENTS OF ENGLISH EDUCATION DEPARTMENT OF STKIP AGAMA HINDU SINGARAJA IN THE ACADEMIC YEAR OF 2020/2021

The present study aimed at: (1) investigating the types of grammatical errors committed by the second semester students of English Education Department of STKIP Agama Hindu Singaraja in writing descriptive text and (2) explaining the causes of grammatical errors committed by the second semester students of English Education Department of STKIP Agama Hindu Singaraja in writing descriptive text. The data were collected by using documentation, questionnaire and interview. The study was descriptive study. The instruments were writing instruction, self assessment, questionnaire, interview guide and tape recorder. The study revealed that there were four kinds of errors found in their writing text, such as: verb errors, preposition errors, article errors and pronoun errors. Total of errors that the students committed was 538 errors. The most frequent errors were in the use of verb as many as 285 errors or 52.97%. There were 189 errors in the use of article or 35.13%. In the use of pronoun was found as many as 39 errors or 7.24%. Meanwhile, there were 25 errors in the use of preposition or 4.64%. There were four causes of errors committed by the students namely: the interference of the learners’ mother tongue, context of learning, overgeneralization, ignorance of rule restrictions. The result from interview showed that the students’ grammatical errors were mostly caused by intralingual factor which is grammar and structure.
Deep learning for unmanned aerial vehicle transportation cost and path planning

With the rapid development of the unmanned aerial vehicle (UAV) industry and artificial intelligence industry, UAV has been used in the civil field. For civil UAVs, the distribution of logistics UAV is a hot research topic. Based on the UAV data set, the value function model of logistics UAV task planning is established by deep learning. It is found that there are obvious differences in value function for different deep learning methods. Moreover, it is also found that using the Random Forest Model has a good effect on the modeling of the value function. On this basis, the task planning model of logistics UAVs based on K-Means is established. In order to find an optimization algorithm suitable for the logistics system, this research uses an improved Genetic Algorithm, improved TS Algorithm, and Particle Swarm Optimization Algorithm to solve the model. By comparing three intelligent optimization algorithms, it is found that the improved TS Algorithm not only converges faster for this model but also has fast convergence for a large number of logistics task points by comparing the flight route and convergence curve of the objective function, which can meet the needs of largescale tasks in the actual logistics system.
Chain of Transactions: How Blockchain Is Changing the Landscape of FinTech

The fast pace of development of technology has disrupted almost all sphere of life, and businesses are no exception. New software, databases and systems have been launched for optimizing processes and help businesses to have a more agile management. The technology has changed the industrial age into silicon age, and the disruption is so apparent that a new term “FinTech” is extensively coined to distinguish the extensive use of technology in the world of finance. Blockchain is one such powerful technology, which has started a new era of internet – Internet-2.0 or Internet of Value. Blockchain is a decentralized database that consists of blocks that retain information, allowing the nodes composing the system to be aware of any data movement. Blockchain has been commonly associated with Bitcoin; however, nowadays it is not limited to just cryptocurrency. In fact, it is banking industry which uses 30% of blockchain application. Absence of intermediary, use of smart contract, etc. have ensured fast services and trust among the participants. It also democratizes money management and transforming the way stock market operations are conducted. Insurance is one such segment, where the blockchain technology is extensively used. But what is being sold as the “new future” comes with its own set of problems. Blockchain has a very high energy dependence. The process of integration and implementation of blockchain is difficult. This technology is very slow and costly, and scalability is always an issue. In this context, it is important to understand – what blockchain technology is, how it works, its features and how this technology is changing the landscape of FinTech while taken care of its drawbacks.
Construction and prediction of regional financial development index based on RBF and SVM integrated learning

With the effective control of the new crown epidemic, how to develop China's financial industry in the post-epidemic era still lacks a suitable regional index system. Taking Beijing, where the financial economy occupies an important position, as an example, this paper selects 12 typical relevant indicators from the four dimensions of structure, scale, vitality and efficiency, and constructs a model based on Radial Basis Function (RBF) and Support Vector Machine (SVM). The threelevel Bagging integrated learning area financial development index system. First, the coefficient of variation method (CVM) is used to determine the weight of each index to measure the level of financial development, and then based on RBF and SVM, the Bagging integrated learning prediction is performed, and the financial development index is obtained. The results show that the RBF-SVM integrated model is more fitting than the single RBF or SVM. The financial development index of Beijing has generally shown positive growth in the past 20 years, and the financial industry has a good development trend. In this paper, we establish a financial development index system to judge regional financial development differences and predict the development trend of the financial industry, thus providing suggestions for the healthy development of my country's financial industry.
A Order based Content-Based 3D Model Retrieval proposal

Abstract In computer graphics, Content-Based Retrieval (CBR) is a database system that consists of receiving an object as input and returning a list of similar objects. Although originally conceived for images, CBR systems can work with objects of another nature such as sounds, and three-dimensional models , represented as geometrical meshes, transporting CBR systems to contexts other than images To assess the proximity between the meshes, some techniques consist of performing mathematical transformations of the meshes into feature vectors and calculating the distance relationship between these vectors. In this work, we built a prototype of a 3D CBR system that uses an order relation, instead of a distance function, as a similarity function between the objects. Furthermore , we propose the use of an order in R n that we call Extended Lexicographical Order (ELO), which seeks to take into account all the information present in the vectors to be compared. We compared our prototype not only with the traditional distance functions but also with more classical R n order relations such as lexicographical and revlex. We also use two types of descrip-tors, a spectral descriptor based on compressed sensing theory and a harmonic spherical-based descriptor. In all cases, our prototype performed better when compared to traditional techniques.
Breast-I AS A screening tool in detecting early breast lesions in women-An experimental study in tertiary care centre Chennai

Background: India is facing challenging situation because the incidence of breast cancer has increased by 14% and the mortality rate has increased by 11.1%. Breast- i is a new tool for effective breast cancer screening, especially for detecting both non-palpable and very small lesions often missed by Clinical Breast Examination (CBE). This device is expected to help in detecting tiny breast lesions in the outpatient clinic itself, helping in reducing morbidity and mortality associated with late diagnosis of carcinoma breast. Objectives: To analyze diagnostic accuracy of Breast-i and to study the prevalence of Breast Cancer in the study population. Materials and Methods: The experimental study was conducted at Sree Balaji Medical College and Hospital, Chennai over eighteen months with sixty female patients using Breast-i for early diagnosis of Breast lesions. Detailed history was taken and CBE was done. Later, patients were screened with Breast-i and further evaluation was done if abnormalities were detected with Breast-i. The findings obtained from Breast-i were compared with FNAC and Trucut Biopsy finding. The obtained data were processed with MedCalc Software Version 19.0.
EFEKTIVITAS PEMBELAJARAN REMEDIAL DALAM MENINGKATKAN HASIL BELAJAR PESERTA DIDIK PADA MATA PELAJARAN IPS DI MTs NEGERI 1 PALU

This article discusses the effectiveness of remedial learning in improving student learning outcomes in social studies subjects at MTs Negeri 1 Palu. The focus of the discussion in this study is how the effectiveness of remedial learning in improving student learning outcomes in social studies subjects at MTs Negeri 1 Palu and what factors hinder Remedial Learning in improving student learning outcomes in social studies subjects at MTs Negeri 1 Palu. The method that the author uses in this study is a qualitative research method. With data collection techniques including observation, interviews, and documentation. The results of this study indicate that in the effectiveness of remedial learning in improving student learning outcomes in social studies subjects at MTs Negeri 1 Palu, namely efforts to improve students are carried out if these students have not reached the minimum standard value, then students will be given remedial. because in this remedial learning students only repeat material that is not understood. Then the inhibiting factors in the effectiveness of remedial learning in improving student learning outcomes in social studies subjects at MTs Negeri 1 Palu, namely, the lack of attention from students, limited learning time carried out in schools as a result of the pandemic that is still endemic and the lack of communication between teachers and students. students to the problem of improving the value and efficiency of remedial learning in schools.
On the Number of Affine Equivalence Classes of Vectorial Boolean Functions and &lt;i&gt;q&lt;/i&gt;-Ary Functions

Vectorial Boolean functions play an important role in cryptography, sequences and coding theory. Both affine equivalence and EA-equivalence are well known equivalence relations between vectorial Boolean functions. In this paper, we give an exact formula for the number of affine equivalence classes, and an asymptotic formula for the number of EA-equivalence classes of vectorial Boolean functions.
IS THE TEST SENSIBLE? DEVELOPING A CRITICAL READING TEST FOR INDONESIAN EFL LEARNERS

This study aims at developing a critical reading test for Indonesian EFL students. This (R &amp; D) project is intended to assess students' progress and is supposed to offer accurate and trustworthy results. The subjects were twenty students from the English Language and Literature Department at Brawijaya University who took part in the try out stage. The test items' difficulty indices ranged from 0.26 to 0.89. The reliability coefficient for the KR-20 was determined to be 0.95, indicating that this multiple-choice critical reading comprehension test is valid and reliable. Despite of the fact that the Critical Reading test is declared valid and reliable, the students’ score taking the Critical Reading test during the try out is less satisfactory. This might be caused by limited number of students taking the try out or problems dealing with teaching and learning process of critical reading in the classroom causing students’ inability to do the test well. Future researchers are suggested to address this issue by either involving more participants during the try out to get the reasons underlying students lack of ability in doing critical reading test.
ONLINE LEARNING AMIDST COVID 19 PANDEMIC DISRUPTION: EFL LECTURERS AND STUDENTS’ CLASSROOM INTERACTION

Due to Covid 19 pandemic, for the last two years face-to-face classroom instruction in all education level has been migrated to online mode which may be conducted elsewhere outside of the face-to-face classroom context. This shifting brings a new trend in the classroom interaction between teachers and students. This study aims at finding out types of classroom interaction the lecturers and students do in running online learning in a public university in Palembang. Qualitative method with a case study design was employed in this study. The participants of this study were the lecturers and students who taught and learned speaking subject in English education study program at one public university. The data were collected through observation and interview to the EFL lecturers and students. The data were analyzed by using thematic analysis through coding process. The findings revealed that there were four kinds of classroom interactions commonly happened during the online learning activities, viz. Student-instructor, student-student, student-content, and student-interface interactions.
AN INVESTIGATION INTO NETIZEN COMMENTS ON INDONESIAN PROMINENT LEADERS’ INSTAGRAM POSTS AND LANGUAGE LEARNING IMPLICATIONS

Instagram serves as an example of the most famous social media platforms. Instagram, particularly for the executives, can favorably relate connections among politicians, voters, and other supportive or contending parties. The current research aimed to analyze the form of social criticism delivered by netizens on the comments towards three Indonesian politicians' Instagram posts, namely Anies Baswedan, Ridwan Kamil, and Ganjar Pranowo, who have millions of followers putting concerns towards what they did on Instagram. Besides, the research also aimed to identify some pedagogical implications of Instagram on language learning. A qualitative approach was employed to uncover the three dimensions of the discourse: positivism-empiricists, constructivism, and critical into one analysis unit or social cognition. The research found that netizens' comments were in line with the status postings of each of the governor published on Instagram, focus on the three governors' performance during their tenure, and affected by the background of the supporting parties. This research supports the idea that Instagram is a viable means of language learning because it can provide a meaningful context to learn a language.
Connectivity with respect to <i>α</i>-discrete closure operators

Abstract We discuss certain closure operators that generalize the Alexandroff topologies. Such a closure operator is defined for every ordinal <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>α</m:mi> <m:mo>&gt;</m:mo> <m:mn>0</m:mn> </m:math> \alpha \gt 0 in such a way that the closure of a set <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>A</m:mi> </m:math> A is given by closures of certain <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>α</m:mi> </m:math> \alpha -indexed sequences formed by points of <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>A</m:mi> </m:math> A . It is shown that connectivity with respect to such a closure operator can be viewed as a special type of path connectivity. This makes it possible to apply the operators in solving problems based on employing a convenient connectivity such as problems of digital image processing. One such application is presented providing a digital analogue of the Jordan curve theorem.
ANALISIS KESALAHAN SINTAKSIS PADA TEKS PERSUASI KARYA SISWA KELAS VIII SMP NEGERI 40 BANDUNG

The research aims to describe the causes of sentence writing errors in the persuasive text of class VIII SMP Negeri 40 Bandung, examine various forms of sentence writing errors, and attempt to make solutions to reduce sentence writing errors in persuasive texts by class VIII students of SMP Negeri 40 Bandung. The research data analysis technique uses the theory of Miles and Huberman in Sugiyono. The research method uses a qualitative approach. Based on the results of the research, it was found 76 sentence writing errors which included 2 unsubjected sentence errors, 5 unsubjected sentence errors, 16 unsubjected and unpredicated sentence errors, 12 subject doubling errors, 1 error between the predicate and the inserted object, 11 sentence errors that illogical, 9 errors in ambiguous sentences, 1 error in omission of conjunctions, 12 errors in the use of excessive conjunctions, 4 errors in the use of foreign terms, 3 errors in the use of unnecessary question words. Based on the results of interviewing one of the Indonesian language teachers at SMP Negeri 40 Bandung, it was concluded that the cause of errors in writing sentences in the persuasive text of class VIII students at SMP Negeri 40 Bandung, namely students did not understand the material and did not carry out an assessment or examination of the writing of sentences in student work. The solution to reduce sentence writing errors in persuasive texts by class VIII students of SMP Negeri 40 Bandung is to make interesting learning videos and carry out in depth assessments or examinations of sentence writing in students works.
Implementation Of Fuzzy Logic On Turbine Ventilators As Renewable Energy

Energy needs in Indonesia in particular and in the world in general continue to increase. One of the sources of electricity supply, PLTA together with steam power plants (PLTU) and gas power plants (PLTG) indeed play an important role in the availability of electricity. Indonesia, which is an archipelagic country and one of the countries located on the equator, is a factor that Indonesia has abundant wind energy potential. The electrical energy needs of remote communities can be met. Turbine ventilator is a device that functions to circulate air which is placed on the roof of the building that functions as ventilation in residential and industrial buildings. Based on previous research, there are those who examine the use of turbine ventilators as power plants, but there are still many shortcomings that need to be fixed. turbine ventilator that is used to catch the wind and drive the generator, by connecting the wind turbine using a v-belt so that the rotation produced by the generator is maximized. Then the generator produces electrical energy.
STUDENTS GRADUATION PREDICTION BASED ON ACADEMIC DATA RECORD USING THE DECISION TREE ALGORITHM C4.5 METHOD

Abstract: An application can assist organizations in achieving the goals to be achieved by facilitating ongoing work processes. This happened in the Information Systems Study Program at one of the best private universities, namely Telkom University, where the SI Study Program has a website called PIPE and has one feature to be able to predict student graduation. However, this feature is currently being developed with an easy flow, so it requires development in the implementation of graduation achievements. Researchers solve these problems by building an assessment model based on academic data on the effect of choosing a specialization. Data mining is needed in this study to form predictive patterns, then one of the data mining groups is based on classification and using machine learning to perform automated assessments so that they can be sustainably performed. In determining the time and delay, using the decision tree method based on the C4.5 algorithm. The accuracy results obtained using the C4.5 algorithm are 94.11%, then the factor that becomes the root node is Jumlah SKS Lulus and the results have an influence on the selection of specialization. So that the results of this graduation model can be applied to the PIPE application. Keyword: C4.5 algorithm; classification; decision tree; graduation prediction Abstrak: Sebuah aplikasi dapat membantu organisasi dalam mencapai tujuan yang ingin dicapai dengan memudahkan proses kerja yang sedang berlangsung. Seperti yang terjadi pada Prodi Sistem Informasi yang ada pada salah satu Perguruan Tinggi Swasta terbaik yaitu Universitas Telkom, dimana pada Prodi SI memiliki website bernama PIPE dan memiliki salah satu fitur untuk dapat melakukan prediksi kelulusan mahasiswa. Namun fitur tersebut saat ini dikembangkan dengan alur penentuan sederhana, sehingga memerlukan pengembangan dalam hal implementasi algoritma prediksi kelulusan. Peneliti melakukan penyelesaian masalah tersebut dengan membangun model prediksi kelulusan berdasarkan rekam data akademik terhadap pengaruh pemilihan peminatan. Data mining dibutuhkan dalam penelitian ini untuk membentuk pola penyelesaian prediksi, kemudian salah satu pengelompokan data mining berdasarkan tugasnya adalah klasifikasi dan menggunakan machine learning untuk melakukan prediksi kelulusan secara otomatis terhadap data baru agar dapat dilakukan secara berkelanjutan. Dalam melakukan klasifikasi prediksi kelulusan tepat waktu dan terlambat, menggunakan metode decision tree berdasarkan algoritma C4.5. Hasil akurasi yang didapat dengan menggunakan algoritma C4.5 adalah sebesar 94,11%, kemudian faktor yang menjadi root node adalah Jumlah SKS Lulus dan hasil memiliki pengaruh terhadap pemilihan peminatan. Sehingga hasil model prediksi kelulusan ini dapat diterapkan pada aplikasi PIPE. Kata kunci: algoritma C4.5; decision tree; klasifikasi; prediksi kelulusan.