Reviewerly/exHarmony huggingface dataset:
Idealisme Ekalawya Dalam Wiracarita Mahabharata Dan Refleksi Pendidikan Di Era Tatanan Baru

This paper aims to interpret the learning idealism of the Ekalawya figure in the Mahabharata and translate it as a reflection of the learning process in this era of the new order. The problematization between regulations and the real conditions of students has given implications for the implementation of online learning. There are many obstacles that arise, ranging from limited infrastructure, costs required and mastery of ICT, both from the teacher and student side. Refers to literary reception theory and hermeneutic methods, Ekalawya is a true disciple, his ideals such as unyielding, independence, discipline, teacher devotion, and making his yad as an obligation have led him to become a great archer. His learning experiences, such as; independent learning, obedience to the social rules of Catur Varna, respect for Drona as a teacher, and also the sincere offerings (daksina) in the form of his thumb. The limited interaction between teachers and students, hard work in doing assignments, independence and honesty of students in answering the evaluation questions, and respect that is maintained are a form of reflection on the learning experiences of Ekalawya in the era of the new order.
New Hybrid Approach for Exemplar-Based Image Inpainting

Pavuluri, Rohith Abhinav, Tharun Kumar, Praveen Reza, MotaharImage inpainting is an approach of retaining the disfigured parts of the image in such a way that it looks acceptable to the naked eye. Image inpainting can be implemented by several techniques, wavelet-based, concept of variation and experience model. Here, in this article, we used the exemplar-based method, i.e. to copy and paste the texture from the known source region. The running time complexity is pretty higher for these techniques. So, a lot of techniques were introduced to improve confidence value and decrease time complexity. In this paper, a new hybrid algorithm is developed which mainly aims to increase the performance of restoration of the image by taking the information from nearby pixels and by selecting them with high probability pixels then replace them. The results achieved during the experiment show the efficiency of the hybrid algorithm. abstract environment.
Educations Board Game for Sustainable Agriculture - Pheromone Pest Control

The mode of agricultural operation affects the ecological environment, and the pollution of land by chemical pesticides also affects the environmental balance. The death of many bees affects pollination, and the animals in the upper layers of the food chain die. The pheromones are formulated with synthetic pheromones as bait and then trapped with appropriate insect traps to reduce the chance of pest mating. Table game matches the props of the garden and pests by moving the insects to the traps. The model constructed by the relationship between the shape of the bottom of the insect and the track. Augmented reality provides an interactive experience of an event-driven where the objects reside in the real world with computer-generated information. Users can use the QR codes to trigger online media. When students complete a specific task, they scan the QR Code with their mobile phone and get more exciting instructions and task guidance. Students satisfied to more in-depth knowledge of pheromones with AR too.
The English Morphemic Analysis: Challenges of Intelligibility in Non-Native Environments

Essential to the study and knowledge of any language is the question of intelligibility. To be knowledgeable in a language implies knowing how the language works, the way it is internally organised and the rules that guide this internal arrangement. A learner of English language in non-native environment, like Nigeria, is expected to be knowledgeable in its structure as to observe its irregularities and patterns or rules. Also, the English morph and morpheme are examined to ascertain the relationship that exists between them. The thrust of this study therefore is on the challenges of intelligibility in the nature, method and problems of morphemic analysis encountered by second language learners, especially among most students of English in some Nigerian universities. With the observation of this unsatisfactory state of affair, this study stresses the need for second language learners to discover and be conscious of the diverse problems posed by morphemic analysis in order to better understand how the English language works. This, however, can be enhanced if English language teachers could guide learners adequately on how to recognize and understand the nature, methods and the problems of morphemic analysis by comparing a wide variety of utterances, especially those which are partially the same. The study also recommends that relevant and adequate text books on the English morphology should be provided by the government and made available in the various libraries of Nigerian universities.
Fast WordPiece Tokenization

Tokenization is a fundamental preprocessing step for almost all NLP tasks. In this paper, we propose efficient algorithms for the WordPiece tokenization used in BERT, from single-word tokenization to general text (e.g., sentence) tokenization. When tokenizing a single word, WordPiece uses a longest-match-first strategy, known as maximum matching. The best known algorithms so far are O(nˆ2) (where n is the input length) or O(nm) (where m is the maximum vocabulary token length). We propose a novel algorithm whose tokenization complexity is strictly O(n). Our method is inspired by the Aho-Corasick algorithm. We introduce additional linkages on top of the trie built from the vocabulary, allowing smart transitions when the trie matching cannot continue. For general text, we further propose an algorithm that combines pre-tokenization (splitting the text into words) and our linear-time WordPiece method into a single pass. Experimental results show that our method is 8.2x faster than HuggingFace Tokenizers and 5.1x faster than TensorFlow Text on average for general text tokenization.
The Analysis of Errors Made by The Third Year Students of English Department in Translating Narrative Text

This study aims at finding out the errors made by the third year English Department students of Universitas Negeri Padang in translating a narrative text from English into bahasa Indonesia. This study used a descriptive method. The data were the translation of the narrative text entitled Snow White done by the third year English Department students of Universitas Negeri Padang. The data were collected using a test and were analyzed using the error analysis. The results of the study indicate semantic error is the error frequently committed by the students in their translation with the frequency of 72 times (59%). Pragmatic error (29%) is the second most frequent error, while morphological error and syntactical error are seldom found in the students’ translation. In conclusion, the errors made by the students are mainly motivated by their tendency to follow the meaning of the dictionary (word for word translation).
Smart Attendance Monitoring System for Facial Recognition-Based in Educational Institution

From early age, taking attendance in schools, colleges, offices and organizations is one of the most important tasks. But, until the early 21st century taking attendance was usually manual which is a time-consuming process and involves huge man power. Due to advancement in the field of computers and technology the process of taking attendance is computerized and most of the institutions, and organizations started using automated systems for marking the attendance. The main purpose of this paper is to build a face recognition-based attendance monitoring system for educational institution to enhance and upgrade the current attendance system into more efficient and effectiveness working as compared to before.
Deformation Medical Image Registration Algorithm Based On Deep Prior Optical Flow Network

Deep convolutional networks have become a common tool for image generation and reconstruction. Unlike people who attribute their excellent performance to the prior information that they can learn from a large number of image samples, the author of Deep Image Prior (DIP) believes that the generator The network can capture a large number of low-level image statistical information before any learning, which means that this information may not be learned through a large number of data sets, and it verifies that the CNN network has a better capture of natural image distribution information. Ability to imitate. Inspired by the idea of DIP, can the deep convolutional network's ability to capture a large number of low-level image statistical information be used in image registration to capture the deformation field between the floating image and the target image to compensate for the traditional manual features and the common convolutional network features Limitations of information capture capabilities. Because the traditional optical flow registration method only relies on gray information and gradient-driven deformation registration, the registration result is not accurate enough. Therefore, we propose an optical flow medical image registration method based on the depth image prior. The generator network extracts feature information to generate the deformation field, and the discriminator of the GAN network is used to further accurately improve the deformation field to make it closer to the real deformation field. Experimental results show that our proposed algorithm can effectively improve the registration accuracy, and its registration accuracy is better than Demons algorithm, medical image professional registration software Elastix and Scale Invariant Feature Transform (SIFT) Flow algorithm.
PENERAPAN MODEL COOPERATIVE LEARNING TIPE JIGSAW UNTUK MENINGKATKAN TANGGUNG JAWAB DAN PRESTASI BELAJAR BAHASA INDONESIA (Sudi Siswa Kelas X SMA Negeri 1 Gumay Talang Kabupaten Lahat)

This research was conducted with the aim to find out 1) The application of the Jigsaw cooperative learning model can increase student responsibility, 2) The application of the Jigsaw cooperative learning model can improve student learning achievement in Indonesian Language subjects in Students and 3) The effectiveness of the application of type cooperative learning models Jigsaw in improving student learning achievement in learning Indonesian Language Class X in SMA Negeri 1 Gumay Talang, Lahat Regency. The problem in this study is that the learning methods used are less varied, the low attitude of student responsibility in participating in learning. This study used 25 subjects in PTK research, 20 in experimental class research, and 20 people in control class research. Data collection techniques in this study used observation and tests. The results of this study indicate that the application of a Jigsaw cooperative learning model can increase student responsibility in Indonesian subjects, the application of a Jigsaw cooperative learning model can improve student learning achievement in Indonesian subjects, and the application of a Jigsaw cooperative learning model can effectively improve student learning achievement in Indonesian subjects class X in SMA Negeri 1 Gumay Talang, Lahat Regency.
Students’ perception toward English subject and their learning outcome

This research aims to find out students' perception of the English subject and provides an overview of the students learning outcome at SMA Negeri 1 Jangka Buya, Pidie Jaya. The instruments used in this research are questionnaires, interview guides, and documentation. Students' perceptions are classified into three categories: education, economy, and social interaction, and one group of negative question models support the other three. The student's perception of English is dominant in education. Generally, the students of SMAN 1 Jangka Buya come from dayah (Islamic school). They believe that mastering English can help them promote Islamic teachings more effectively. The negative question is the second dominant result; most students admit that language is a medium to build up connection and communication verbally or through written text. Meanwhile, in the economy category, students believe that we can increase income with English language skills. It is also shown that students who have good grades in English also have a high perception of certain categories. In comparison, students who have low perception get low learning outcomes on the English subject. So that, the students' perception and background must be hardly noticed by the teacher in planning a good teaching and learning process.
Well log data super-resolution based on locally linear embedding

Unconventional remaining oil and gas resources such as tight oil, shale oil, and coalbed gas are currently the focus of the exploration and development of major oil fields all over the world. Therefore, to make best understand of target reservoirs, enhancing the vertical resolution of well log data is crucial important. However, in the face of the continuous low-level fluctuations of international oil price, large scale use of expensive high resolution well logging hardware tools has always been unaffordable and unacceptable. In another aspect, traditional well log interpolation methods can always not realize high reliable information enhancement for crucial high frequency components. In this paper, in order to improve the well log data super-resolution performance, we propose for the first time to employ Locally Linear Embedding (LLE) technique to reveal the nonlinear mapping relationship between 2-times-scale-difference well log data. Several super resolution experiments with well log data from a given area of Daqing Oil field, China, were conducted. Experimental results illustrated that the proposed LLE-based method can efficiently achieve more reliable super-resolution results than other state-of-the-art methods.
Literature Review on Phenomenon of Concept Drift and its Handling Approaches

Data extracted from dynamic distributions for realworld applications lead to a phenomenon called concept drift. Term concept in “concept drift” primarily refers to an unseen dependency or correlation between input attributes and target variables. There are various factors responsible for the drift in the concept. Such as random noise, change in the distributions of incoming streaming data, variations in the dependency of target variable on input features. This hitch could encounter when the dependency and relationship between input data attributes and target variables would vary gradually. So, the model must be capable of processing this data efficiently and swiftly adapt to these variations. This paper presents an outline of the concept drift research field and provides a review of various kinds of approaches adopted by researchers for mitigating issues related to concept drift.
deepQuest-py: Large and Distilled Models for Quality Estimation

We introduce deepQuest-py, a framework for training and evaluation of large and lightweight models for Quality Estimation (QE).deepQuest-py provides access to (1) state-ofthe-art models based on pre-trained Transformers for sentence-level and word-level QE; (2) light-weight and efficient sentence-level models implemented via knowledge distillation; and (3) a web interface for testing models and visualising their predictions.deepQuest-
Pelatihan Web Programming di SMK Dr Cipto Mangunkusumo Perdagangan

The development of information and technology is currently so fast. Along with these developments, users must be up to date in accordance with the times. Currently there are many web applications that can be used as needed. To balance the development of information technology with the needs of users, it is very necessary for human resources who are able to provide creative ideas and high innovation. SMK Dr. Cipto Trade has national talents who are competent in the field of information technology. Student / I SMK Dr. Cipto Trade, especially the RPL Department, has sufficient capabilities in the field of information technology, including the web. However, the students' abilities still need to be added and sharpened. One way is to provide them with web programming training using HTML and the PHP programming language. The results of this training show a significant increase in ability.
Writing Sprints untuk Publikasi Ilmiah Mahasiswa pada Pembelajaran Berbasis Capaian: Sebuah Penelitian Tindakan Kelas

The strategy for the success of scientific publications for students is very much needed in learning achievement-based subjects. This study aims to analyze the application of Writing Sprints in article writing among students for the effectiveness of publication in scientific journals. This study applies a qualitative approach using descriptive-analytical method. The results of the research and discussion show that Writing Sprints has produced two student scientific publications in two weeks supported by modules, reviews, and feedback factors. This is a finding that the student's lecture period can be taken in a short time in a course with achievement-based learning, depending on the strategy applied. The conclusion of this study is that the module, review, and feedback factors are very decisive in learning based on the achievement of scientific publications through the Writing Sprints approach.
Using association rules mining, multi-level data representation and cross-layer operations for data management in mobile ad hoc network

This paper presents a data management framework for mobile ad hoc network called association rules based network layer data and information management (ALADIN). Despite the extensive research on da...
Automatic Detection of Implicit Aggression in Russian Social Media Comments

This article studies the characteristics of implicit and explicit types of aggression in the comments of a Russian social network with the means of machine learning.As it is hypothesized that expression of aggression depends on local norms, the dataset contains the comments collected from a single social media community.These comments were divided into three classes: polite communication, implicit aggression, and explicit aggression.Trying different combinations of data preprocessing, we discovered that lemmatization and replacement emojis with placeholders contribute to better results.We tested several models (Naive Bayes, Logistic Regression, Linear Classifiers with SGD Training, Random Forest, XGBoost, RuBERT) and compared their results.The study describes the misclassifications and compares the keywords of each class of comments.The results can be helpful while enhancing the algorithm of detection of implicit aggression
Preservation of the Muyu Indigenous Language with an Android-based Dictionary

Indonesia is a country rich in cultural diversity, with each culture having its own regional language characterizing the area. The Muyu language is one of the regional languages, used by the Muyu tribal community in Boven Digoel Regency, Papua Province. The language is now under the threat of abandonment by the Muyu community, especially the younger generation. One of the causes is the lack of written learning media for the language to be inherited by older speakers in the form of books and electronic dictionaries. This study will try to develop an Android-based Muyu-Indonesian-English dictionary application using the waterfall method (classic life cycle). The aim is to test students' preferences and attitudes towards the development of Muyu language knowledge. The research sample consisted of 40 respondents consisting of Muyu students at Musamus University. A questionnaire was used to measure the extent of student responses to the use of the Muyu-Indonesian-English dictionary application. The test results show that the system has no errors in carrying out the functions that have been created previously at the manufacturing stage. The three-language Muyu-Indonesian-English dictionary application is able to search for vocabulary properly and correctly in Muyu, Indonesian, and English. The attitude of students towards the usefulness of this dictionary application is measured at the medium category, meaning that the application is useful as a tool to facilitate students of the Muyu language, especially Android users, in finding vocabulary in three languages, and can be a tool for learning the Muyu language and preserving local wisdom so that it does not become extinct. Doi: 10.28991/esj-2021-SP1-06 Full Text: PDF
Precision Batching: Bitserial Decomposition for Efficient Neural Network Inference on GPUs

We present PrecisionBatching, a quantized inference algorithm for speeding up neural network inference on traditional hardware platforms at low bitwidths. PrecisionBatching is based on the following insights: 1) neural network inference with low batch sizes on traditional hardware architectures (e.g: GPUs) is memory bound, 2) activation precision is critical to improving quantized model quality and 3) matrix-vector multiplication can be decomposed into binary matrix-matrix multiplications, enabling quantized inference with higher precision activations at the cost of more arithmetic operations. Combining these three insights, PrecisionBatching enables inference at extreme quantization levels (< 8 bits) by shifting a memory bound problem to a compute bound problem and achieves higher compute efficiency and runtime speedup at fixed accuracy thresholds against standard quantized inference methods. Across a variety of applications (MNIST, language modeling, natural language inference, reinforcement learning) and neural network architectures (fully connected, RNN, LSTM), PrecisionBatching yields end-to-end speedups of over 8× on a GPU within a < 1 - 5% error margin of the full precision baseline, outperforming traditional 8-bit quantized inference by over 1.5 × - 2× at the same error tolerance.