Reviewerly/exHarmony huggingface dataset:
The Use of Information Technologies in Primary School Students Teaching: the Experience of Japan

The article considers the problem of using information technology in teaching primary school students in Japan. It was studied the experience of Japan, where age is not an obstacle to the study new technologies in educational institutions. The Japanese are convinced that the basics of robotics should be learned by children at an early age. The study of IT as a discipline is aimed at instilling an interest in mathematics, science and technology. Children make the first attempts to create robots in elementary school. The need for the implementation of the studied areas and ways of teaching IT in Japan in the primary school of Ukraine is determined, the recommendations of the training and continuous support of teachers are formulated.
Transfer Learning and Language Model Adaption for Low Resource Speech Recognition

We train an end-to-end recurrent neural network along with an integrated n-gram language model to perform automatic speech recognition on a low resource dataset containing telephone speech.The dataset presents many challenges because it is highly disfluent, contains unique accents and word choices, and is of poor audio quality.Our proposed method uses both transfer learning and language model adaption to obtain a 42.27% Word Error Rate (WER), which improves on existing models (60.18%WER, 74.89% WER) and low resource models (79.82%WER).Nonetheless, the WER is much higher than the current benchmark for high resource languages.Thus, more research is needed to overcome the obstacles low resource speech presents to high quality automatic speech recognition.
Linear Gantry Robot Control System

A Linear Robot comprises of a controller mounted onto an overhead framework that permits development over a flat plane. Gantry are likewise called Cartesian or straight robots. Direct Gantry Robot will be made which will pick and place the things in ideal spot. Initially, a robot with two hub primarily x-hub and y-hub will be made utilizing different electronic instruments after that one programming will be made will provide guidance to the robot to move according to the necessity. The product will be configuration utilizing python programming and will be constrained by Linux working framework and furthermore by Raspberry pi’s GPIO header. By making this framework the effectiveness will expand, takes less floor space, will be lower in expense and it can accomplish substantial payloads.
APPLYING MURDER TECHNIQUE TO IMPROVE STUDENTSâ€™ READING COMPREHENSION OF THE SEVENTH GRADE STUDENTS OF SMP SANTO THOMAS 1 MEDAN IN THE ACADEMIC YEAR OF 2020/2021

The aim of this study is to find whetever Murder technique works effectively on improving studentsâ€™ reading comprehension and to describe the studentsâ€™ responses towards the application of Murder technique in the learning teaching processes of reading comprehension. The subject of the study in the seventh grade students of SMP Santo Thomas 1 Medan in the Academic Year of 2020/2021 consisting 32 students of class VII-A. This studyÂ was conducted by applying Classroom Action Research (henceforth CAR). The result of the test showed that the studentsâ€™Â meanÂ score in pre-test is 55,00Â with 16% of students got â‰¥ 70, in formative test is 68,13 with 38% of students got score â‰¥ 70, and 83,13 in post testÂ with 100% of students got score â‰¥ 70. The qualitative data analysis showed that the studentsâ€™ responses after being taught by using Murder Technique are very good. The percentage students who strongly agree is 26 (81%), agree is 27 (84%), there is no students choose strongly disagree and disagree. It is suggested that the English teachers use Murder Technique as an alternative technique in teaching reading to the students in Junior High School in order to improve their studentsâ€™ reading comprehension.
Professional Learning for Teachers in a Period of Crisis

Professional learning is acknowledged as key to improving teaching practice. With the great disruption to educational continuity induced by the COVID-19 pandemic, requiring a rapid shift from face-to-face learning to a learning-from-home model required teachers to change their practice. In ‘normal’ times, carefully planned professional learning would be a key element to support changes in teaching practice. However, in the ‘new normal’ this time was not available and professional learning for teachers was evident in quite different formats and forms. Drawing on the notes taken from meetings with the leaders of learning within a national network of more than 50 non-government school leaders as they navigated their way through this new space, we sought to develop an understanding of the extent to which the key elements of effective professional learning were evident in the professional learning available to teachers during this time. What has become clear is that teachers have been willing to make rapid and radical changes to their teaching practice and that professional learning was key to supporting teachers to successfully make these changes.
The Most Likely Steady State for Large Numbers of Stochastic Traveling Salesmen

A class of continuous time, ergodic Markov processes having extremely large state space is considered, and a technique for numerical determination of high-probability components of the steady-state solution vector is proposed. The technique first identifies the steady-state solution as a maximum entropy Gibbs measure, and then maps the associated energy function onto a Hopfield neural network. A numerical iteration scheme based on an unresolved conjecture due to Hillam is used to complete the computation. An application of this technique to computer graphics is offered in the form of a new algorithm for digital halftone resolution.
Multi-Nyström Method Based on Multiple Kernel Learning for Large Scale Imbalanced Classification

Extensions of kernel methods for the class imbalance problems have been extensively studied. Although they work well in coping with nonlinear problems, the high computation and memory costs severely limit their application to real-world imbalanced tasks. The Nyström method is an effective technique to scale kernel methods. However, the standard Nyström method needs to sample a sufficiently large number of landmark points to ensure an accurate approximation, which seriously affects its efficiency. In this study, we propose a multi-Nyström method based on mixtures of Nyström approximations to avoid the explosion of subkernel matrix, whereas the optimization to mixture weights is embedded into the model training process by multiple kernel learning (MKL) algorithms to yield more accurate low-rank approximation. Moreover, we select subsets of landmark points according to the imbalance distribution to reduce the model’s sensitivity to skewness. We also provide a kernel stability analysis of our method and show that the model solution error is bounded by weighted approximate errors, which can help us improve the learning process. Extensive experiments on several large scale datasets show that our method can achieve a higher classification accuracy and a dramatical speedup of MKL algorithms.
High-Quality Diversification for Task-Oriented Dialogue Systems

Many task-oriented dialogue systems use deep reinforcement learning (DRL) to learn policies that respond to the user appropriately and complete the tasks successfully. Training DRL agents with diverse dialogue trajectories prepare them well for rare user requests and unseen situations. One effective diversification method is to let the agent interact with a diverse set of learned user models. However, trajectories created by these artificial user models may contain generation errors, which can quickly propagate into the agent's policy. It is thus important to control the quality of the diversification and resist the noise. In this paper, we propose a novel dialogue diversification method for task-oriented dialogue systems trained in simulators. Our method, Intermittent Short Extension Ensemble (I-SEE), constrains the intensity to interact with an ensemble of diverse user models and effectively controls the quality of the diversification. Evaluations on the Multiwoz dataset show that I-SEE successfully boosts the performance of several state-of-the-art DRL dialogue agents.
Discourse of Ornament in Twentieth-Century Architecture

The article provides an overview of the ornament in the architecture of the twentieth century, indicating the points that form the direction of the axial trajectories of the development of the ornamental form in the context of architecture. The research is based on the method of analytical description of ornament positions extracted from the the practice and textual corpus of architect’s statements. Utterances synthesize the discursive range of ornament in the field of architecture. The body of the ornament covers the subject aspects of human life and is present in the spatial form of the architectural environment. Traditionally, the location of the ornamental form is associated with the art of decoration, so the standard ornament is considered as an additive, additional in relation to the singularity of the form. The same principle applies to architecture. This study actualizes the problem of redefining the ornamental form, marking point inversions of the additivity of ornamen tal decoration as a formative aspect involved in creating the results of architectural activity. The formative principle of the article is a chronological sequence of twenty positions outlining the discourse of ornament in the context of twentieth-century architecture.
An Upcycling Tokenization Method for Credit Card Numbers

Internet users are increasingly concerned about their privacy and are looking for ways to protect their data. Additionally, they may rightly fear that companies extract information about them from their online behavior. The so-called tokenization process allows for the use of trusted third-party managed temporary identities, from which no personal data about the user can be inferred. We consider in this paper tokenization systems allowing a customer to hide their credit card number from a webshop. We present here a method for managing tokens in RAM using a table. We refer to our approach as upcycling as it allows for regenerating used tokens by maintaining a table of currently valid tokens. We compare our approach to existing ones and analyze its security. Contrary to the main existing system (Voltage), our table does not increase in size nor slow down over time. The approach we propose satisfies the common specifications of the domain. It is validated by measurements from an implementation. By reaching 70 thousand tries per timeframe, we almost exhaust the possibilities of the 8-digit model for properly dimensioned systems.
Research on Cruise Tourists Satisfaction Based on Web Text and IPA Analysis: A Case Study of Royal Caribbean Cruise

This paper takes Royal Caribbean cruise ship as the research object, and combines the analytic hierarchy process and content analysis, constructs the satisfaction index system of Royal Caribbean cruise ship from four aspects: cruise accommodation, cruise catering, cruise service and cruise entertainment.The paper uses Houyi Caijiqi to capture the comments of Royal Caribbean cruise ship on Ctrip tourism network, aiming at 24, 150 effective comments, word frequency analysis and IPA analysis were carried out to provide feasible suggestions for Royal Caribbean cruise ship to improve tourists' satisfaction.
Analisis fungsi dan isi pantun masyarakat Desa Kopong dan relevansinya terhadap pembelajaran Bahasa Indonesia di SMP

This research aimed to analyze the function and content of community limerick in Desa Kopong, Kecamatan Kewapante, Kabupaten Sikka. Spoken literature works are largely practiced in this community to transfer messages among people. This research employed a qualitative approach with a descriptive method. The obtained data were in the form of rhymes and verses obtained from engagement techniques and participant observation. The obtained data were analyzed through content analysis techniques and data validation with triangulation methods. Results showed that the community limerick in Desa Kopong functions as a means of recreation, aesthetics, didactic, morality, and religion. The content of the limericks includes reinforcement of the values ​​of grit, resilience, politeness, volunteerism, and religion. Finding from this research is significant for Bahasa Indonesia (BI) lesson in junior high schools (SMP), especially related to students’ competency in analyzing the forms and contents of community limerick.
Hands-On Guidance for Distilling Object Detectors

Knowledge distillation can lead to deploy-friendly networks against the plagued computational complexity problem, but previous methods neglect the feature hierarchy in detectors. Motivated by this, we propose a general framework for detection distillation. Our method, called Hands-on Guidance Distillation, distills the latent knowledge of all stage features for imposing more comprehensive supervision, and focuses on the essence simultaneously for promoting more intense knowledge absorption. Specifically, a series of novel mechanisms are designed elaborately, including correspondence establishment for consistency, hands-on imitation loss measure and re-weighted optimization from both micro and macro perspectives. We conduct extensive evaluations with different distillation configurations over VOC and COCO datasets, which show better performance on accuracy and speed trade-offs. Meanwhile, feasibility experiments on different structural networks further prove the robustness of our HGD.
Automatic Sexism Detection with Multilingual Transformer Models.

Sexism has become an increasingly major problem on social networks during the last years. The first shared task on sEXism Identification in Social neTworks (EXIST) at IberLEF 2021 is an international competition in the field of Natural Language Processing (NLP) with the aim to automatically identify sexism in social media content by applying machine learning methods. Thereby sexism detection is formulated as a coarse (binary) classification problem and a fine-grained classification task that distinguishes multiple types of sexist content (e.g., dominance, stereotyping, and objectification). This paper presents the contribution of the AIT_FHSTP team at the EXIST2021 benchmark for both tasks. To solve the tasks we applied two multilingual transformer models, one based on multilingual BERT and one based on XLM-R. Our approach uses two different strategies to adapt the transformers to the detection of sexist content: first, unsupervised pre-training with additional data and second, supervised fine-tuning with additional and augmented data. For both tasks our best model is XLM-R with unsupervised pre-training on the EXIST data and additional datasets and fine-tuning on the provided dataset. The best run for the binary classification (task 1) achieves a macro F1-score of 0.7752 and scores 5th rank in the benchmark; for the multiclass classification (task 2) our best submission scores 6th rank with a macro F1-score of 0.5589.
Video Frame-Based Deep Learning Face Detection-A Review

Face detection is hotly discussed issues in computer vision, not just because of the difficult nature of the face as an object, mostly because of the numerous implementations that require the incremental approach of the face detection program. Important progress has been made over the last 15 years due to the accessibility of data in unrestricted capturing situations (so-called' in-the-wild through the Internet, the public's initiative to establish freely accessible standards, and even success in creating robust machine vision algorithms). Because of the explosive increase of video content, the face detection issue has attracted extensive interest among researchers. In this study, we look at the most recent advancements in real-world face detectors, beginning with the technique of the pioneering Viola-Jones face detector. This strategies are classified into two sections: rigid structures, which are taught primarily via strategies based on deep learning that are boosted or implemented, and deformable structures, which are defined by their elements and characterize the face. Fair representation techniques will be outlined in detail, as well as a few other efficient strategies that will be discussed shortly after the end. Finally, the most important resources for analyzing face detection algorithms and recent optimization efforts are addressed, as well as the potential of face detection.
A HISTORY OF ASSESSMENT SYSTEM DEVELOPMENT OF STUDENTS’ ACADEMIC ACHIEVEMENTS

Nowadays the assessment of academic achievements is one of the multifaceted aspects of school education, playing the role of a formative, motivating, determining, guiding factor in the learning process. Learning assessment is constantly developing and expanding. The history of the development of students’ academic achievementsstarts from teachers’ activity, which is written in works of ancient philosophers. Currently assessment is being considered in accordance with a new stage, adapting to the technical progress of our time. The article examines the history of the development of the learning assessment system in the global educational space, as well as studies the opinions of great Kazakh thinkers and teachers.
Structured DropConnect for Uncertainty Inference in Image Classification

With the complexity of the network structure, uncertainty inference has become an important task to improve the classification accuracy for artificial intelligence systems. For image classification tasks, we propose a structured DropConnect (SDC) framework to model the output of a deep neural network by a Dirichlet distribution. We introduce a DropConnect strategy on weights in the fully connected layers during training. In test, we split the network into several sub-networks, and then model the Dirichlet distribution by match its moments with the mean and variance of the outputs of these sub-networks. The entropy of the estimated Dirichlet distribution is finally utilized for uncertainty inference. In this paper, this framework is implemented on LeNet$5$ and VGG$16$ models for misclassification detection and out-of-distribution detection on MNIST and CIFAR-$10$ datasets. Experimental results show that the performance of the proposed SDC can be comparable to other uncertainty inference methods. Furthermore, the SDC is adapted well to different network structures with certain generalization capabilities and research prospects.
Blockchain Oracle Design Patterns

Blockchain is a form of distributed ledger technology (DLT) where data is shared among users connected over the internet. Transactions are data state changes on the blockchain that are permanently recorded in a secure and transparent way without the need of a third party. Besides, the introduction of smart contracts to the blockchain has added programmability to the blockchain and revolutionized the software ecosystem leading toward decentralized applications (DApps) attracting businesses and organizations to employ this technology. Although promising, blockchains and smart contracts have no access to the external systems (i.e., off-chain) where real-world data and events resides; consequently, the usability of smart contracts in terms of performance and programmability would be limited to the on-chain data. Hence, \emph{blockchain oracles} are introduced to mitigate the issue and are defined as trusted third-party services that send and verify the external information (i.e., feedback) and submit it to smart contracts for triggering state changes in the blockchain. In this paper, we will study and analyze blockchain oracles with regard to how they provide feedback to the blockchain and smart contracts. We classify the blockchain oracle techniques into two major groups such as voting-based strategies and reputation-based ones. The former mainly relies on participants' stakes for outcome finalization while the latter considers reputation in conjunction with authenticity proof mechanisms for data correctness and integrity. We then provide a structured description of patterns in detail for each classification and discuss research directions in the end.
Improving Performance Through the Combination of Planning Tools, Autonomy, and Group Dynamics: A Case Study in the Field of Education

Based on the Cognitive Evaluation Theory and on the Self-Determination Theory, this study aims at testing the effectiveness of planning tools combined with autonomy and group dynamics. The purpose of these tools is to increase intrinsic motivation because of their informational effect. More specifically, thanks to the use of a monthly planner, we tested the improvement of school performance in students attending upper secondary schools.
Use of Digital Image Watermarking to Enhance the Security of Graphical Password Authentication

There are several techniques for implement an authentication system for computers that most commonly use the clear text password. One of the security problems is the use of a text password, the lack of choosing a complicated password by users due to forgetting, and being guessable and retrieved by attackers. One of the methods for passing text passwords is the use of graphical password authentication systems that increase the amount of forgetting passwords by using images instead of text characters. In this paper, the security challenges of using a graphical password are discussed. Then, explain a method for using the watermarking digital image for the authentication process and providing an algorithm suitable for watermarking and enhance the security of graphical password authentication system, and its quantitative and qualitative security parameters will be examined.
World Map Asia clustering using Agglomerative Clustering Algorithm

Cluster analysis is one technique that can be correlated. Explain the closeness or similarity between objects and variables. The analysis is divided into 2 methods, namely hierarchical and non-hierarchical. In the hierarchical method, there are several methods, including the complete linkage method, average linkage, single linkage and ward linkage. This method is applied in the grouping of the Asian continent. The purpose of this study is to classify countries based on GDP and population in Asia. In this study, the hierarchical method used is Agglomerative Hierarchical Clustering (AHC). 177 countries with 6 columns of country data around the world and sorted into 47 country data columns and 6 country data columns in the Asian continent. The per capita state income is obtained by dividing GDP by population. Then made 4 clusters. Cluster 1 consists of the United Arab Emirates, Kuwait and Qatar. Cluster 2 consists of Brunei, Oman, Saudi Arabia and Taiwan. Cluster 3 consists of 34 countries and the last cluster is Cyprus, Israel, Kazakhstan, South Korea, Malaysia.
Utilizing Youtube Videos To Enhance Students Speaking Skills In Teaching English To The 1st Semester English Department Students, Universitas Muhammadiyah Surakarta

This study focuses on the utilizing YouTube video to enhance student’s speaking skills in teaching English to the 1st semester who joined interpersonal speaking class. The purpose of this study is to describe how the students’ perceive their experience in using YouTube videos in learning English. This type of research is phenomenology. The research recruited two students who had joined Interpersonal Speaking subject as the object of the research. The data was collected through interview. The results of this research were; 1) the students feel confident in speaking using YouTube video 2) the students feel enjoyable learning interpersonal speaking by using YouTube videos