Reviewerly/exHarmony huggingface dataset:
PEMANFAATAN SITUS PURBAKALA CANDI MUARO JAMBI SEBAGAI OBJEK PEMBELAJARAN SEJARAH LOKAL DI ERA DIGITAL

This study aims to analyze the use of Muaro Jambi temple site as an object of local history learning in the digital era. The problems discussed in the article are: (1) How does the Muaro Jambi Temple can be used as local history object of study? (2) How does the Muaro Jambi Temple could be used as the object of local history learning in digital era? To answer the two problems, authors conducted literature study method by collecting several articles from research results related to the research theme. Data collection is done by analyzing the source documents and literature. The results of this study indicate that the use of historical relics in the surrounding environment can lead to effective learning, coupled with the application of local history learning material using interactive media that follows the times, such as videos, digital books, and the internet. In addition to giving rise to effective, interesting, and interactive learning, of course, the purpose of this utilization is to increase historical knowledge of learners, especially on local historical material.
Research on smart navigation system based on AR technology

The system uses the Unity3D engine to develop the Android app, and develop AR technology modules with Vuforia toolkit, integrating geographic information service technology and panorama technology. We combined the two tracking and registration methods which are based on sensors and natural images features, to implement a tourism navigation and AR introduction system based on Kulangsu, a famous scenic spot in Xiamen. The system is mainly divided into two modules, the route navigation module and the scenic spots guide module.
Implementation Cooperative Learning Type STAD Assisted Edmodo in Improving Students' Critical Thinking Skills

The STAD cooperative learning model can support active, independent and responsible learning for students. The application of this model is expected to improve students' critical thinking skills. The purpose of this study was to determine the improvement of students' critical thinking skills using Edmodo-assisted learning models. Edmodo was modified to become a teacher tool to expand learning at home. This research is an experimental study using a pretest-posttest control group design involving 39 students of class VIII MTs At-Taqwa Beru, Sikka, East Nusa Tenggara. Based on the results of the study it was found that the Edmodo-assisted STAD type cooperative learning model can improve students' critical thinking skills.
Penerapan Model Pembelajaran Berbasis Masalah Untuk Meningkatkan Aktivitas Dan Hasil Belajar Siswa

This study aims to improve the activities and student learning outcomes of Class IX-G SMP Negeri 5 Amlapura Semester I Academic Year 2018/2019. The subjects in this study were 22 st grade students, amounting to 22 people; consisting of 8 male students; and 14 female students, in the first semester of the 2018/2019 school year. This study uses a class action research design through two stages. Student learning activity data is collected by observation sheets, and learning outcome data is collected by multiple choice tests. Both types of data were analyzed by quantitative descriptive methods. Before the two stages are carried out, the initial observation activities are conducted by the students' learning activities, and analyzing the questions of UTS Indonesian Language Class IX semester I of academic year 2018/2019. The activity was carried out to determine the level of initial activity and student learning outcomes before the implementation of the Problem Based Learning model. The results of this study indicate that the application of the Problem Based Learning design can increase learning activities and Indonesian language learning outcomes of Class IX-G SMP Negeri 5 Amlapura semester I of 2018/2019, namely in the initial observation of low student learning activities, increasing from an average of 59,77 and completeness 13,63% in the first stage, in the second stage the average student learning activity was 81.25 and the completeness level was 86,36%. While student learning outcomes increased from an average of 68,18, absorbency of 68,18%, and classical completeness level of 50,00% at initial reflection, increased to an average of 69,09, absorptive capacity of 69,09%, and the level classical completeness was 59,09% in stage I. In stage II it increased to an average of 77,27, absorbency 77,27%, and classical completeness rate 90,91%. Based on these results the researchers formulated suggestions; (1) Students are expected to be able to utilize this Learning design to improve their learning activities and outcomes; (2) Other teachers can apply this learning design, by examining the weaknesses of the results of this study; (3) Schools can make consideration material as one of the learning design that can be developed to solve learning problems in class; (4) Other researchers can make the results of this study a relevant research study.
A Survey of Cross-Lingual Plagiarism Detection using Natural Language Processing

Plagiarism detection is gaining importance due to requirements for integrity in Research works especially when it comes to Cross-lingual plagiarism. In this paper, we have researched a new approach for Cross-Lingual sentence level plagiarism detection. The proposed approach works as initially the suspicious document is translated to a particular language and candidate document is retrieved for further processing. Pre-processing techniques will be selected using NB Classifier for better efficiency which will be used on document which includes POS tagging, Chunking, Stop-word removal, Lemmatization, Syntactic-semantic rule. Then Machine Learning classification algorithms such as Support Vector Machine (SVM), Naïve Bayes Classifier (NBC), Decision Tree (DT) are used to check plagiarism. The Evaluation and Analysis are performed using F-score, Accuracy, Precision and Recall. Our aim in this paper is to find the plagiarism and improve the accuracy of plagiarism detection between two languages by using concepts of natural language processing.
MimicProp: Learning to Incorporate Lexicon Knowledge into Distributed Word Representation for Social Media Analysis

Lexicon-based methods and word embeddings are the two widely used approaches for analyzing texts in social media. The choice of an approach can have a significant impact on the reliability of the text analysis. For example, lexicons provide manually curated, domain-specific attributes about a limited set of words, while word embeddings learn to encode some loose semantic interpretations for a much broader set of words. Text analysis can benefit from a representation that offers both the broad coverage of word embeddings and the domain knowledge of lexicons. This paper presents MimicProp, a new graph-mode method that learns a lexicon-aligned word embedding. Our approach improves over prior graph-based methods in terms of its interpretability (i.e., lexicon attributes can be recovered) and generalizability (i.e., new words can be learned to incorporate lexicon knowledge). It also effectively improves the performance of downstream analysis applications, such as text classification.
Learning and Teaching Support for Modular-Rating Educational System

The main purpose of the modular system is to change organizational foundations of the pedagogical process in higher education to those that would ensure its substantial democratization and the conditions for a real change in the student’s role in learning (from the object to the subject of this process), provide the educational process with the necessary flexibility, introduce the principle of teaching individualization.&#x0D; The regulation on the organization of the educational process in higher educational institutions determines scientific and methodical support that includes the State Standards for Specialties (Course Description, Educational and Professional Program), the curriculum, the syllabus for all normative and elective disciplines, discipline-specific teaching materials, the programs of practices of various kinds, textbooks, manuals, instructional and methodical guidelines, tasks for final, current and state control, etc.&#x0D; The modular-rating and credit-modular educational systems require a detailed development of the system of learning and teaching support considering students’ individual characteristics to form a methodological culture of thinking of general phenomena and patterns of developing the physical world, as well as the ability of interdisciplinary synthesis and a deep understanding of scientific and professional tasks. The role a teacher plays in the system of such training is to motivate, determine the level of knowledge, consult and inform students, while students transform from passive listeners to active participants of the educational process. Such training enables students to study at their own pace, considering their own abilities and temperament.&#x0D; The teaching and methodical complex on the discipline is the system of normative, methodical and didactic documents that determine the objectives of learning, the content of an academic discipline, didactically and scientifically substantiated sequence, methods and means of the formation of knowledge, skills, abilities, professional and civic qualities in students.
DIGITAL COMPETENCE OF FUTURE TEACHERS AS AN INTEGRAL COMPONENT OF PROFESSIONAL TRAINING

The article analyzes the current research of the future teachers’ training in the context of the problems of using innovative technologies in the professional activities. The author concludes that the digital competence of future teachers is an important component of vocational training. In the modern world, the information, presented in the digital format, is the main direction of structural reforms in many countries. The aim of the article is to highlight and characterize the modern requirements of the formation of digital competence of future teachers in the process of professional training in institutions of higher education in Ukraine. The international requirements for digital competence of citizens in an educational context have been characterized. Digital competence indicators of teachers include knowledge, skills and abilities to use online tools creatively while respecting proper rules of ethical behavior on the Internet. The importance of the formation of digital competence, as a vital skill of the personality, in the professional training of future specialists has been proved. In the context of the study, digital skills of a teacher in the 21st century have been highlighted. They should be based on the free and appropriate using of various opportunities in the process of professional activity, inherent in computer services and online technologies. The use of digital technologies in the educational process contributes to the independent cognitive activity of students and the organization of the effective interaction between its participants. Innovative technologies allow students to solve the tasks, which are as close as possible to the reality. The author defines the concept of “digital competence of future teachers” as the ability of a personality to use, select expediently and interact with digital technologies in pedagogical activity, which gives the chance to involve students in training and to cooperate effectively with all participants of educational process. Digital competence of future teachers is an important component of their professional activity, which will contribute to the effective using of innovative technologies in teaching. Owing to the modern information technologies, computer services and online technologies are widely introduced and used in the educational process.
Building on Word Animacy to Determine Coreference Chain Animacy in Cultural Narratives

Animacy is the characteristic of being able to independently carry out actions in a story world (e.g., movement, communication). It is a necessary property of characters in stories, and so detecting animacy is an important step in automatic story understanding. Prior approaches to animacy detection have conceived of animacy as a word- or phrase-level property, without explicitly connecting it to characters. In this work we compute the animacy of referring expressions using a statistical approach incorporating features such as word embeddings on referring expression, noun, grammatical subject and semantic roles. We then compute the animacy of coreference chains via a majority vote of the animacy of the chain's constituent referring expressions. We also reimplement prior approaches to word-level animacy to compare performance. We demonstrate these results on a small set of folktales with gold-standard annotations for coreference structure and animacy (15 Russian folktales translated into English). Folktales present an interesting challenge because they often involve characters who are members of traditionally inanimate classes (e.g., stoves that walk, tree that talk). We achieve an F1 measure 0.90 for the referring expression animacy model, and 0.86 for the coreference chain model. We discuss several ways in which we anticipate these results may be improved in future work.
The HW-TSC Video Speech Translation System at IWSLT 2020

In this paper, we present details of our system in the IWSLT Video Speech Translation evaluation.The system works in a cascade form, which contains three modules: 1) A proprietary ASR system.2) A disfluency correction system aims to remove interregnums or other disfluent expressions with a fine-tuned BERT and a series of rule based algorithms.3) An NMT System based on the Transformer and trained with massive publicly available corpus is used for translation.
Solving the Longest Simple Path Problem with Heuristic Search

Prior approaches for finding the longest simple path (LSP) in a graph used constraints solvers and genetic algorithms. In this work, we solve the LSP problem with heuristic search. We first introduce several methods for pruning dominated path prefixes. Then, we propose several admissible heuristic functions for this problem. Experimental results demonstrate the large impact of the proposed heuristics and pruning rules.
Solar fraction used in water heating systems for south Brazilian climate changes scenarios

Solar thermal systems consist of water heating from the global solar radiation. Increasing atmospheric concentrations of greenhouse gases tend to increase the earth's surface temperature. The main objective of this work was to estimate the solar fraction obtained by means of solar heating systems for dwellings, for eight locations in the State of Paraná, in scenarios of possible climate changes projected until the end of the 21st century. F-Chart method was used to simulate the performance of solar heating systems based on the monthly average of solar radiation data, which determines the annual solar fraction or percentage of the energy demand that is covered by the solar installation. The results showed that with the impact of the climate changes, the decrease in the percentage of energy demand average that is covered by the solar installation was on average 14.3%, for both scenarios. The simulated values showed a slight decrease trend of radiation data and an increase of the solar fraction. All localities presented a characteristic seasonal behavior, with annual values of solar fraction between 82.4 and 129.8%, according to the studied localities. In relation to the monthly solar fraction, the values between November and March presented averages of solar fraction between 104 and 147.2%. But from May to August, the percentage of energy demand served by the solar installation does not reach the totality, with values between 53.6 and 99.9%. The results prove that the State of Parana has favorable climatic conditions for the installation of solar heating systems, even if it is installed for aggregation purposes, in order to reduce the electric power consumption.
An Existential Criterion for the Normal and Abnormal Personality in the Works of Viktor Frankl. Summary

Background.This is the last in a series of four articles scheduled for publication in this journal.In the first article (Kapustin, 2015a), I proposed a new "existential criterion" for the normal and abnormal personality that is implicitly present in the works of Erich Fromm.According to this criterion, normal and abnormal personalities are determined, first, by their position regarding existential dichotomies, and, second, by particular aspects of the formation of this position.Such dichotomies, entitatively existent in human life, are inherent, two-alternative contradictions.In the other articles (Kapustin, 2015b(Kapustin, , 2016a)), I showed that this criterion is also implicitly present in the four famous personality theories of Freud, Adler, Jung, and Rogers.Objectives.To provide evidence that this criterion is present in the personality theory of Viktor Frankl and to present a comparative analysis of all six theories of personality.Results.The existential criterion for the normal and abnormal personality based on the works of Fromm is also implicitly present in theoretical conceptualizations of personality, predisposed and non-predisposed to developing various psychological problems and to mental disorders, by Freud, Adler, Jung, Rogers, and Frankl, although in more particular forms, related to more specific existential dichotomies, characterizing the nature of human life.Conclusion.The fact that the existential criterion is present in these six theories of personality, developed within totally different approaches to psychology and psychotherapy, is evidence of a high degree of its theoretical justification and of the possibility of their integration.
Mining evolutions of complex spatial objects using a single-attributed Directed Acyclic Graph

Directed acyclic graphs (DAGs) are used in many domains ranging from computer science to bioinformatics, including industry and geoscience. They enable to model complex evolutions where spatial objects (e.g., soil erosion) may move, (dis)appear, merge or split. We study a new graph-based representation, called attributed DAG (a-DAG). It enables to capture interactions between objects as well as information on objects (e.g., characteristics or events). In this paper, we focus on pattern mining in such data. Our patterns, called weighted paths, offer a good trade-off between expressiveness and complexity. Frequency and compactness constraints are used to filter out uninteresting patterns. These constraints lead to an exact condensed representation (without loss of information) in the single-graph setting. A depth-first search strategy and an optimized data structure are proposed to achieve the efficiency of weighted path discovery. It does a progressive extension of patterns based on database projections. Relevance, scalability and genericity are illustrated by means of qualitative and quantitative results when mining various real and synthetic datasets. In particular, we show how such an approach can be used to monitor soil erosion using remote sensing and geographical information system (GIS) data.
Novel Opinion mining System for Movie Reviews

Opinion Mining (OM) works on transferring the online available opinions into useful knowledge.In this paper, a novel opinion mining system of reviews in Turkish has been presented.The proposed system utilizes Word2Vec, which is one of the states of the art text feature extraction method, along with an ensemble learning algorithm for classification.The challenging and benchmark "IMDB Movies Reviews" dataset has been used for conducting the experimental comparison and verification.In addition, the performance of the proposed method is compared to some of the well-known machine learning algorithms like Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Naive Bayes (NB).The tested ensemble methods are the Random Forest (RF), AdaBoost Classifier, and Gradient-Boosting Classifier (GBC).The results of the conducted experiments using the dataset have shown that the performance of SVM, KNN, and NB are comparable.However, the performance, robustness, and stability of the system have been significantly improved by adapting the RF ensemble learning, along with the Word2Vec feature vector, and suitable pre-processing operations on the data.In addition, the proposed method is compared to one of the states of art ensemble methods and have shown superior performance with respect to it.
Frequency-Aware Reconstruction of Fluid Simulations with Generative Networks

Convolutional neural networks were recently employed to fully reconstruct fluid simulation data from a set of reduced parameters. However, since (de-)convolutions traditionally trained with supervised L1-loss functions do not discriminate between low and high frequencies in the data, the error is not minimized efficiently for higher bands. This directly correlates with the quality of the perceived results, since missing high frequency details are easily noticeable. In this paper, we analyze the reconstruction quality of generative networks and present a frequency-aware loss function that is able to focus on specific bands of the dataset during training time. We show that our approach improves reconstruction quality of fluid simulation data in mid-frequency bands, yielding perceptually better results while requiring comparable training time.
Combining Fine-Tuning with a Feature-Based Approach for Aspect Extraction on Reviews (Student Abstract)

One key task of fine-grained sentiment analysis on reviews is to extract aspects or features that users have expressed opinions on. Generally, fine-tuning BERT with sophisticated task-specific layers can achieve better performance than only extend one extra task-specific layer (e.g., a fully-connected + softmax layer) since not all tasks can easily be represented by Transformer encoder architecture and special task-specific layer can capture task-specific features. However, BERT fine-tuning may be unstable on a small-scale dataset. Besides, in our experiments, directly fine-tuning BERT on extending sophisticated task-specific layers did not take advantage of the features of task-specific layers and even restrict the performance of BERT module. To address the above consideration, this paper combines Fine-tuning with a feature-based approach to extract aspect. To the best of our knowledge, this is the first paper to combine fine-tuning with a feature-based approach for aspect extraction.
An Interpretable Multimodal Visual Question Answering System using Attention-based Weighted Contextual Features

Visual question answering (VQA) is a challenging task that requires a deep understanding of language and images. Currently, most VQA algorithms focus on finding the correlations between basic question embeddings and image features by using an element-wise product or bilinear pooling between these two vectors. Some algorithms also use attention models to extract features. In this extended abstract, a novel interpretable multimodal system using attention-based weighted contextual features (MA-WCF) is proposed for VQA tasks. This multimodal system can assign adaptive weights to the features of questions and images themselves and to their contextual features based on their importance. Our new model yields state-of-the-art results on the MS COCO VQA datasets for open-ended question tasks.