Reviewerly/exHarmony huggingface dataset:
Penerapan Data Mining Asosiasi pada Pola Transaksi dengan Metode Apriori

Indonesian business is a promising thing, the competition that occurs in the business of business people is always trying to be strategic and breakthroughs that can ensure the continuity of what they do. The youngest business cafe / restaurant, especially among young people to adults. The development of the Cafe / Restaurant business in Indonesia is growing rapidly, this can be seen from the many cafes / restaurants that have emerged which provide attractive interior design venues and offer a comfortable and pleasant atmosphere to gather with family and friends. Many competitors in business, especially in cafes / restaurants, require developers to find strategies that can increase sales, one of which is the use of transaction data.Data Mining is the process of extracting information from data sets through the use of algorithms and techniques involving the fields of statistics, machine learning, and database management system. A priori algorithms are a type of association rule in data mining. In this study using secondary data. The results of calculations and analysis of consumer research using Data Mining with the Apriori Algorithm method, overall from the sales sample data obtained 52 association rules that meet support above 20% and 50% confidence. Based on the test results using Rapidminer, it can be rejected by that the product that consumers are more interested in is the minimum confidence above 50%.
An Economic Model of Machine Translation

With the advent of free, online translation services such as Google Translate, many people are now able to obtain information relatively effortlessly from a wide variety of foreign language sources. The translations from these services are often worse than those provided by professional, human translators, however, and the tradeoff between these two alternatives is not always clear. When should a professional be used, and when is machine translation sufficient? In this study, we discuss factors involved in the decision and illustrate their use with a predictive model.
Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog Management

For task-oriented dialog systems, training a Reinforcement Learning (RL) based Dialog Management module suffers from low sample efficiency and slow convergence speed due to the sparse rewards in RL.To solve this problem, many strategies have been proposed to give proper rewards when training RL, but their rewards lack interpretability and cannot accurately estimate the distribution of stateaction pairs in real dialogs.In this paper, we propose a multi-level reward modeling approach that factorizes a reward into a threelevel hierarchy: domain, act, and slot.Based on inverse adversarial reinforcement learning, our designed reward model can provide more accurate and explainable reward signals for state-action pairs.Extensive evaluations show that our approach can be applied to a wide range of reinforcement learning-based dialog systems and significantly improves both the performance and the speed of convergence.
Postgraduate Mathematics Education Students' Perceptions of Technology-based Tools and Resources: Exploring the Influences of Connectivism and the Three Worlds of Mathematics

As we embrace the Fourth Industrial Revolution, transformations in education necessitate the use of technology-based pedagogy.This study explored postgraduate mathematics education students' perceptions of using technology-based tools and resources.This qualitative and interpretive study was conducted at a university in KwaZulu-Natal, South Africa.The study was framed utilizing the theory of connectivism and the three worlds of mathematics theory.Connectivism is a learning theory that is considered to be strengthened by existing technology-based tools and is founded on the notion that ubiquitous Information Communication Technology impacts teaching and learning.The three worlds of mathematics theory support the view that the development of mathematical thinking is founded on perception, operation and reasoning.Postgraduate mathematics education students engaged in interactive workshops using different online platforms.Subsequently, a sample of participants was interviewed using a semi-structured interview schedule.The interviews were conducted via various online platforms.Data that were generated were analyzed thematically.The results revealed two main themes.These themes focused on the strengths and challenges of using technology-based tools and resources to learn mathematics as perceived by the participants.The results of this study provide evidence that technology-based tools and resources may also be considered by lecturers when teaching mathematics topics that involve drawing complex mathematical diagrams and solving problems based on these complex mathematics diagrams.Also, the results support the view that to alleviate challenges associated with using technology-based tools and resources, lecturers need to ensure that the technology-based tools and resources being used are user-friendly, inexpensive, readily available and data-efficient.Moreover, this study support the view that lecturers need to consider attending professional development workshops and seminars that focus on integrating technology effectively in educational environments.This study has relevance globally and nationally when considering the implications of using technology-based tools and resources for learning mathematics.
The Effect of Utilizing the Six Thinking Hats Strategy on Developing the English Language Speaking Skill and Attitudes towards it among Online EFL General Diploma Students

The current study aimed at investigating the effect of utilizing the Six Thinking Hats strategy on developing the English Language speaking skill and attitudes towards the skill among online EFL General Diploma students. The participants were one experimental group, thirty-two students. Three main instruments were used for data collection: speaking sub-skills checklist, speaking test and attitudes scale. The experimental group were taught using the Six Thinking Hats strategy for twelve weeks during the first semester of the academic year 2020/2021. Data collected through the pre and post administration of the instruments was subjected to t-test. The results, revealed through the post-administration of the speaking test and the attitudes scale, displayed that the post-performance of the experimental group significantly exceeded its own pre-performance. Thus, the results reflected support for the study hypotheses. They proved the positive effect of the Six Thinking Hats strategy on developing the English Language speaking skill and attitudes towards the skill among online EFL General Diploma students.
SCRIPT: Self-Critic PreTraining of Transformers

We introduce Self-CRItic Pretraining Transformers (SCRIPT) for representation learning of text.The popular masked language modeling (MLM) pretraining methods like BERT replace some tokens with [MASK] and an encoder is trained to recover them, while ELEC-TRA trains a discriminator to detect replaced tokens proposed by a generator.In contrast, we train a language model as in MLM and further derive a discriminator or critic on top of the encoder without using any additional parameters.That is, the model itself is a critic.SCRIPT combines MLM training and discriminative training for learning rich representations and compute-and sample-efficiency.We demonstrate improved sample-efficiency in pretraining and enhanced representations evidenced by improved downstream task performance on GLUE and SQuAD over strong baselines.Also, the self-critic scores can be directly used as pseudo-log-likelihood for efficient scoring.
Analisis Sentimen dan Topic Modelling Pada Pembelajaran Online di Indonesia Melalui Twitter

This study aims to determine the comments and responses of various parties regarding the online learning carried out during the COVID-19 pandemic in Indonesia using sentiment analysis and topic modeling methods with Orange's machine learning.Data in the form of comments and reactions from various parties regarding online learning was taken on April 18, 2020 and January 21, 2021 from Twitter using the API (Application Programming Interface).The results of the processed data taken on April 18, 2020, revealed that of the comments and responses that appeared, most of them contained the emotion of surprise with a value of 44.5%.The excitement of surprise arose mostly because Indonesians weren't ready for online learning.And the data collection on January 21, 2021 contains the highest amount of joy emotions with a value of 48.4%.Joy's emotions arise due to the tale of the task rider.Information from this research is that the e-learning system that has been implemented to date is still experiencing problems.These can be seen from the complaints where the word assignment was used 481 times.In addition, complaints with the word abandonment reappeared 330 times in the second period.
Comparison of Different Processing Methods of Joint Coordinates Features for Gesture Recognition with a CNN in the MSRC-12 Database

This work presents an application of a convolutional neural network (CNN) to recognize gestures through images captured by a Kinect sensor. The objective is to compare the accuracy of different processing methods of joint coordinates feature in the artificial neural network, to specify which methods makes possible a more assertive network. The Microsoft Research Cambridge (MSRC-12) is the dataset used, it consists in a sequence of human body articulation movements represented by the Kinect Skeleton. In addition, the FastDTW algorithm is employed to normalize the data frames number. Three different methods are proposed in this paper: the 3D coordinates method, the subtraction method and the normalization method for training the CNN. We have also applied two types of training: combined and individual training. Using combined training is obtained with 3D coordinates method an accuracy rate of 87,50%, using subtraction method the accuracy rate is 87,16% and with normalization method the accuracy rate obtained is 76,93%. The average accuracy rate for 3D coordinate method using individual training is 91,70%, for subtraction method is 92,48% and for normalization method is 83,54%.
Developing ASR for Indonesian-English Bilingual Language Teaching

Usage-based analyses of teacher corpora and code-switching (Boztepe, 2003) are an important next stage in understanding language acquisition. Multilingual corpora are difficult to compile and a classroom setting adds pedagogy to the mix of factors which make this data so rich and problematic to classify. Using quantitative methods to understand language learning and teaching is difficult work as the ‘transcription bottleneck’ constrains the size of datasets. We found that using an automatic speech recognition (ASR) toolkit with a small set of training data is likely to speed data collection in this context (Maxwelll-Smith et al., 2020).
Understanding Capacity Saturation in Incremental Learning

In class-incremental learning, a model continuously learns from a sequential data stream in which new classes are introduced.There are two main challenges in classincremental learning: catastrophic forgetting and capacity saturation.In this work, we focus on capacity saturation where a learner is unable to achieve good generalization due to its limited capacity.To understand how to increase model capacity, we present the continual architecture design problem where at any given step, a continual learner needs to adapt its architecture to achieve a good balance between performance, computational cost and memory limitations.To address this problem, we propose Continual Neural Architecture Search (CNAS) which takes advantage of the sequential nature of classincremental learning to efficiently identify strong architectures.CNAS consists of a task network for image classification and a reinforcement learning agent as the meta-controller for architecture adaptation.We also accelerate learning by transferring weights from the previous learning step thus saving a large amount of computational resources.We evaluate CNAS on the CIFAR-100 dataset in several incremental learning scenarios with limited computational power (1 GPU).We empirically demonstrate that CNAS can mitigate capacity saturation and achieve performances comparable with full architecture search while being at least one order of magnitude more efficient.
Nighttime Single Image Dehazing Based on the Structural Patch Decomposition

Images acquired from the outdoors are often degraded owing to bad weather conditions, such as haze, fog, and rain. Various algorithms for dehazing daytime images have been introduced to eliminate the effects of haze. However, unlike daytime conditions, where sunlight is the main source of light, there are multiple regional light sources in nighttime conditions. Thus, it is difficult to effectively eliminate the effects of haze at night using dehazing algorithms that primarily target daytime hazy images. In addition, most nighttime dehazing algorithms adopt the dark channel prior (DCP) to estimate the transmission, but this approach has problems due to the spatially variant illuminations. In this paper, we propose a nighttime single image dehazing algorithm based on the structural patch decomposition. First, atmospheric light is estimated by separating the intensity and color of light. To estimate the transmission, the candidates for the transmission values are set, and the final transmission value is obtained based on a weighted summation of these candidates for each image pixel. The weights of the candidates are determined according to the structural patch information of the scene radiances estimated with these candidates. Using this approach, an appropriate transmission map can be obtained, and the dehazing procedure is shown to be robust to the mixed illumination conditions observed in the nighttime conditions. The experimental results show that the proposed algorithm effectively eliminate the effect of haze in images based on quantitative and qualitative evaluations.
The Effectiveness of Instructional Media to Improve Early Childhood Creative Thinking Skills

Instructional media is a tool used in the learning process to convey material, information and communicate with students to achieve learning effectiveness.The use of instructional media is also expected to increase students' understanding of the subjects being taught.This study aims to develop instructional media in improving creative thinking skills in early childhood ages 5-6 years.The number of samples in this study was 36 divided into three groups.The first group consisted of three students with different characteristics for the implementation of the one-to-one trial.The second group consists of 6 students for the small group trial.At the same time, the third group consisted of 25 students for field trials.The instruments used were pre-test and post-test.The stages of developing instructional media are carried out in three main stages, namely; 1) conduct needs analysis, 2) develop initial products, 3) expert validation, revisions, and trials.The results showed that effective instructional media could improve early childhood creative thinking skills.For that, it is suggested that teachers become learning developers, not just educators.Therefore an understanding of development research is needed to support the learning process.Implications Educational institutions can take advantage of learning media that have been developed because of the obstacles that students have faced in increasing their creative thinking skills.
Media-Reality As Epiphenomenon Of Digital Technologies In Media-Philosophical Discourse

The relevance of understanding the phenomenon of media and the effects of the influence of media on various sociocultural practices and relevance as a methodological basis for research in various fields of public life is substantiated.It is argued that the significance of the media philosophical discourse is explained by a medial turn in socio-humanitarian and applied research and is not limited to individual discourses that themed the subject and methodology of their research in connection with the awareness of the epistemological and ontological role of media.In the media-and-philosophical discourse, an important problem is the justification of the ways of forming media reality that is safe for socialization.This requires the constructive use of digital technology to implementation of preventive functions for the development of media competencies; coordination of media-and-information and sociocultural functions based on the formation of personal media culture.The development of modern theoretical knowledge of media reality is presented on the basis of the analysis of the philosophical foundations of reflection, which are defined as intellectual-and-technical, "communicative" as virtual-and-network, and philosophical and cultural as media-cultural and media-worldview on the "post-industrial" stage measurement of this process.A special mission of the media reality concept has been explicated, which sets the humanistic perspective for the logical reconstruction of the cultural and spiritual integrity of the textual, intellectual-and-technical, information-and-network dimensions of the social being of modern human.
THE EFFECTIVENESS OF ANDROID-BASED EDUCATIONAL GAME TOWARD HIGH SCHOOL STUDENTS’ PREPAREDNESS IN FACING MERAPI ERUPTION

Background: mount Merapi has the most active eruption frequency in Indonesia and even in the world, located on the border of two provinces, Yogyakarta and Central Java. Based on this, it is necessary to improve the preparedness program, especially for high school students who are active and productive communities. In improving disaster preparedness, capacity building in the form of sufficient knowledge requires effective educational methods, one of which is educational games. Purpose: the research objective was to determine the effectiveness of educational games compared to coventional methods for eruption disaster preparedness. Methods: this research is a quasy experimental design with a pretest - posttest nonequivalent control group. This study compares the effectiveness of the educational game method with the conventional method, on students' preparedness to face eruptions disaster. The sampling technique was a cluster sample, with a total sample of 42 people in the control group and 42 people in the intervention group who were students at school in disaster prone area. The stages of the research method are starting from the preparation stage which includes game design, software development stage, trial phase, data collection stage, processing stage and data analysis. Result: the results show that there is an effect of educational games on the level of knowledge, seen from the p value &lt;0.05 . Conclusion: It is necessary to develop android-based educational media to generate interest and good attitudes towards disaster preparedness in school age children
Automatic Identification of Ruptures in Transcribed Psychotherapy Sessions

We present the first work on automatically capturing alliance rupture in transcribed therapy sessions, trained on the text and self-reported rupture scores from both therapists and clients.Our NLP baseline outperforms a strong majority baseline by a large margin and captures client reported ruptures unidentified by therapists in 40% of such cases.
The Research on the English Reading Teaching Mode Aiming at the Improvement of Thinking Quality

In English reading teaching, it is important and necessary to promote the development of students' thinking quality, an important element of key competence of the English subject. However, the current English reading teaching overemphasizes language knowledge input, the extraction of basic information and the application of reading strategies, but neglects the cultivation of students' thinking quality. Therefore, how to develop students' thinking quality in English reading teaching has become an important issue to be solved.
Morphology-Aware Meta-Embeddings for Tamil

In this work, we explore generating morphologically enhanced word embeddings for Tamil, a highly agglutinative South Indian language with rich morphology that remains low-resource with regards to NLP tasks.We present here the first-ever word analogy dataset for Tamil, consisting of 4499 hand-curated word tetrads across 10 semantic and 13 morphological relation types.Using a rules-based morphological segmenter and meta-embedding techniques, we train metaembeddings that outperform existing baselines by 16% on our analogy task and appear to mitigate a previously observed trade-off between semantic and morphological accuracy.
Stress filed data visualization by using earthquake focal mechanism and shear wave splitting results in the Makran region northern Iran

In this study, a new method is introduced for stress data visualization. As an example, the SHmax variations in the Makran region are mapped by using this new approach. Maximum horizontal stress directions in the study area are extracted from earthquake focal mechanism data. The anisotropy study results in terms of shear wave splitting fast direction axes are added to the dataset to show its effect. The results show substantial variation in SHmax directions, which reflects the complicated tectonic nature of the region. Adding the shear wave splitting data improved the results' accuracy and showed the correlation between the two quantities in the study area.
Understanding the concepts of digital learning approaches: An empirical analysis of schools in developing countries

The rising popularity of learning via e-learning platforms, coupled with the accelerated pace in mobile devices’ uptake during the post-COVID-19 environment, presents tremendous learning demands on mobile and wireless networking infrastructure. Some fundamental principles and components are essential to maximizing user experience and online program success. This paper will explore the gaps between traditional learning and mobile learning by presenting a comprehensive survey of the two areas’ cusps. We trace the practices in mainstream schools with potential online program development and delivery strategies to meet the school’s ratification. We identify the critical learning and performance support. We then discuss several techniques and platforms that facilitate the efficient deployment of learning onto mobile systems. Finally, we discuss how to tailor learning to mobile environments, propose a set of activities that would augment online presence for students, and open directions for future research.
Designing a Minimal Retrieve-and-Read System for Open-Domain Question Answering

In open-domain question answering (QA), retrieve-and-read mechanism has the inherent benefit of interpretability and the easiness of adding, removing, or editing knowledge compared to the parametric approaches of closedbook QA models.However, it is also known to suffer from its large storage footprint due to its document corpus and index.Here, we discuss several orthogonal strategies to drastically reduce the footprint of a retrieve-andread open-domain QA system by up to 160x.Our results indicate that retrieve-and-read can be a viable option even in a highly constrained serving environment such as edge devices, as we show that it can achieve better accuracy than a purely parametric model with comparable docker-level system size. 1
Intelligent learning systems for LLL courses

The proposed model for organizing blended and distance learning involves the creation of an individual learning path, which makes it flexible. The learning model is represented using an ontological model, and the decision rules for the model are logical rules. This training model is used to train teachers in digital literacy and distance learning in the context of Covid 19