Reviewerly/exHarmony huggingface dataset:
PandaDB: Intelligent Management System for Heterogeneous Data

With the development of big data application, the demand of large-scale structured/unstructured data fusion management and analysis is becoming increasingly prominent.However, the differences in management, process, retrieval of structured/unstructured data bring challenges for fusion management and analysis.This study proposes an extended property graph model for heterogeneous data fusion management and semantic computing, and defines related property operators and query syntax.Based on the intelligent property graph model, this study implements PandaDB, an intelligent fusion management system for heterogeneous data.This study depicts the architecture, storage mechanism, query mechanism, property co-storage, AI algorithm scheduling, and distributed architecture of PandaDB.Test experiments and cases show that the co-storage mechanism and distributed architecture of PandaDB have good performance acceleration effects, and can be applied in some scenarios of fusion data intelligent management such as entity disambiguation of academic knowledge graph.
Teaching Media “Powtoon” to Assist Students’ Writing Narrative Text

This research explores teaching media “Powtoon” to assist students’ writing narrative text. This research aims to investigate strategies used by the teacher in using media “Powtoon” to assist students’ writing narrative text and to know how the students perceive about the use of media “Powtoon”. It was argued that the use of media “Powtoon” would provide students with powerful tool to use in the classroom. Bowman (2013) arguessthat “Powtoon offers teachers more creativity and flexibility in students instruction to help introduce, develop, and reinforce key education concept”. So, this strategy is appropriate for the teacher to invite students’ motivation and participation in learning English especially in assessing writing skill. A qualitative descriptive research was used in this research, and this research was conducted at the tenth grade of Vocational School in Karawang, and took an English teacher who teaches writing class and 18 students as samples which were chosen purposively as the sample of the study. The data were collected by using classroom observation, and interview. The result from this study showed a positive perception through strategies which were used by the teacher and help the students in improving their self confidence in practicing writing. Beside that, the students’ spelling, grammar, content and vocabulary improved because the students feel free in conveying and expressing their idea and some sentences in writing the script of the story helped them in arranging the sentences to make a good sentences. Therefore, the researcher analyzed the students’ interview. The data were displayed in four questions.The dominant answer of the students’ interview indicates that they enjoyed and motivated in following teacher’s instruction in every stages of learning process. Based on these research findings, it is recommended that the teacher can use media Powtoon in teaching writing narrative text to Vocational School students. Furthermore, this strategy was not only increasing students’ writing skill, but also increase students’ technology skill through the use of media Powtoon and classroom interaction strategies. In addition, this study also suggests that English teacher to use media Powtoon
Automatic radar antenna scan type recognition based onlimited penetrable visibility graph

To address the problem of the weak anti-noise and macro-trend extraction abilities of the current methods for identifying radar antenna scan type, a recognition method for radar antenna scan types based on limited penetrable visibility graph(LPVG) is proposed. Firstly, seven types of radar antenna scans are analyzed, which include the circular scan, sector scan, helical scan, raster scan, conical scan, electromechanical hybrid scan and two-dimensional electronic scan. Then, the time series of the pulse amplitude in the radar reconnaissance receiver is converted into an LPVG network, and the feature parameters are extracted. Finally, the recognition result is obtained by using a support vector machine (SVM) classifier. The experimental results show that the recognition accuracy and noise resistance of this new method are improved, where the average recognition accuracy for radar antenna type is at least 90% when the signal-to-noise ratio (SNR) is 5 dB and above.
Impossible Open Spaces: Exploring the Effects of Occlusion on the Noticeability of Self-Overlapping Virtual Environments

Impossible spaces uses self-overlapping virtual architecture to in-crease the area of virtual environments that can be explored by walking. This paper details a preliminary study using psychophysical methods to explore if users' ability to detect overlapping virtual spaces changes when the walls of overlapping rooms and the paths connecting them are removed. The results indicate that impossible spaces can be deployed, even when no walls are presented between adjacent rooms. However, the absence of walls appears to make it easier for users to detect impossible spaces.
Age and Gender Detection Using CNN

In recent years, much effort has been put forth to balance age and sexuality. It has been reported that the age can be accurately measured under controlled areas such as front faces, no speech, and stationary lighting conditions. However, it is not intended to achieve the same level of accuracy in the real world environment due to the wide variation in camera use, positioning, and lighting conditions. In this paper, we use a recently proposed mechanism to study equipment called covariate shift adaptation to reduce the change in lighting conditions between the laboratory and the working environment. By examining actual age estimates, we demonstrate the usefulness of our proposed approach.
English Text Classification Using Improved Recursive Feature Elimination (IRFE) Algorithm: تصنيف النص الإنجليزي باستخدام الخوارزمية العودية المحسنة لإزالة الخواص (IRFE)

Documents classification is from most important fields for Natural language processing and text mining. There are many algorithms can be used for this task. In this paper, focuses on improving Text Classification by feature selection. This means determine some of the original features without affecting the accuracy of the work, where our work is a new feature selection method was suggested which can be a general formulation and mathematical model of Recursive Feature Elimination (RFE). The used method was compared with other two well-known feature selection methods: Chi-square and threshold. The results proved that the new method is comparable with the other methods, The best results were 83% when 60% of features used, 82% when 40% of features used, and 82% when 20% of features used. The tests were done with the Naïve Bayes (NB) and decision tree (DT) classification algorithms , where the used dataset is a well-known English data set “20 newsgroups text” consists of approximately 18846 files. The results showed that our suggested feature selection method is comparable with standard Like Chi-square.
Binary Image Random Grid (2, n)-Visual Cryptography Scheme

Majority of the data security ensures scientific systems. What's more related viewpoints on furnish for confidentiality, information security, substance Con-firmation Furthermore information root Confirmation. Visual cryptography may be another system which gives data security utilizing straightforward calculation Dissimilar to the complex, computationally escalated consideration calculations utilized within other systems in customary cryptography. This method permits visual data should make encrypted clinched alongside such an approach that their unscrambling could make performed by those human Visual framework (HVS), without whatever mind boggling cryptographic calculations. Pixel irregular Grids extends those purpose by Moni Naor and Adi Shamir All information in pixel grids will give secrecy and mystery without risking suspicion about an interloper. Those recommended plan complies with the technique for mystery offering plan the place mystery majority of the data is separated under Different greater part done negligible structure Furthermore will be further recouped once covering printed transparencies for the imparted data on it. Each of them will be that point approved for legitimacy. A endeavor need been constructed to utilize hardware rings with implant those mystery data with sure precise revolution. Also acceptance of the single person imparted over order will keep away from deceiving. In this paper we need aid portray each techniques from claiming VCS Also exhibited its similar contemplate utilizing favorable circumstances. What's more Hindrances.
Ensemble-based Semi-Supervised Learning for Hate Speech Detection

Large and accurately labeled textual corpora are vital to developing efficient hate speech classifiers. This paper introduces an ensemble-based semi-supervised learning approach to leverage the availability of abundant social media content. Starting with a reliable hate speech dataset, we train and test diverse classifiers that are then used to label a corpus of one million tweets. Next, we investigate several strategies to select the most confident labels from the obtained pseudo labels. We assess these strategies by re-training all the classifiers with the seed dataset augmented with the trusted pseudo-labeled data. Finally, we demonstrate that our approach improves classification performance over supervised hate speech classification methods.
Toward a Competitive Agent Framework for Magic: The Gathering

Magic: The Gathering is a collectible card game that is traditionally a human-based game: two or more players compete with their own custom deck to eliminate their opponent(s). Magic has again surged in popularity with the release of Magic: Arena, a digital substitute for the physical card game. What Magic: Arena lacks is meaningful gameplay with a competitive, intelligent agent. We propose a strategy-based framework for basic decision making in Magic: The Gathering. We describe these procedures and provide experimental evidence for the effectiveness of our approach.
Ontology-based Chatbot to Support Monitoring of Server Performance and Security By Rule-base

The server is a computer program or a device that provides functionality for other programs or devices, called "clients". Generally, server computers have many resources that can be used by one or more clients through the network with specific permissions and requirements. Therefore, the server needs a monitoring system that can monitor server activity and notify if problems occur. This research focuses on developing a notification and question and answer system to connect the network admin with the monitoring system via chatbot. The developed chatbot can send notifications to the admin if an error occurs and can answer questions about the server's condition. The question and answer system developed implements natural language processing for Indonesian. The process of understanding questions is by classifying each word (token) based on language knowledge stored in the ontology. Then the classification results are processed by rule-base to produce conclusions to take monitoring data and compiled into answers. The test results show that the developed system can auto-notify if any problem in a server, and can answer questions by accuracy 95%.
Message Design of Printed and Digital Material to Meaningful Learning

This article reports on research a message design of printed and digital message in Learning Theory course at Universitas Negeri Padang (UNP). Survey was conducted to collect data on the utilization of instructional materials by students who ware active in the semester in January-June. The results indicated that the print instructional materials written by lecturers have started to consider the design principles message, but still needs to be improved. That principle has not been carried out for digital teaching materials. Digital teaching materials need to be developed to familiarize students studying in 21st century environments by optimizing the campus e-learning facility to be able to perform generative learning, and meaningful learning
Privacy-Preserving Two-Party Computation of Parameters of a Fuzzy Linear Regression

Purpose: describe two-party computation of fuzzy linear regression with horizontal partitioning of data, while maintaining data confidentiality. Methods: the computation is designed using a transformational approach. The optimization problems of each of the two participants are transformed and combined into a common problem. The solution to this problem can be found by one of the participants. Results: A protocol is proposed that allows two users to obtain a fuzzy linear regression model based on the combined data. Each of the users has a set of data about the results of observations, containing the values of the explanatory variables and the values of the response variable. The data structure is shared: both users use the same set of explanatory variables and a common criterion. Regression coefficients are searched for as symmetric triangular fuzzy numbers by solving the corresponding linear programming problem. It is assumed that both users are semihonest (honest but curious, or passive and curious), i.e. they execute the protocol, but can try to extract information about the source data of the partner by applying arbitrary processing methods to the received data that are not provided for by the protocol. The protocol describes the transformed linear programming problem. The solution of this problem can be found by one of the users. The number of observations of each user is known to both users. The observation data remains confidential. The correctness of the protocol is proved and its security is justified. Keywords: fuzzy numbers, collaborative solution of a linear programming problem, two-way computation, transformational approach, cloud computing, federated machine learning.
Effectiveness of Archives Management Reviewed from Management Factors in Pattallassang Sub-District Office in Gowa regency

Archives has a role as a center of memory, resources and as a necessary means of supervision in every organization in order to carry out all its activities both in the offices of state and private institutions.The purpose of this research is to find out the management of archives reviewed from management factors in the District Office Pattallassang Gowa Regency.The research method used is qualitative by survey method, namely research that intends to obtain facts from the phenomena in order to understand and solve problems related to factors that affect the effectiveness of archive management in the District Office Pattallassang Gowa Regency. Data collection techniques used are observations, documentations and interviews. Conclusion in this research is a management factor, it is expected to do the purging of archives above 5 years with clear recording and supervision and evaluation of activities after the activity in order to know the advantages and disadvantages of the activity so that the next activities can be maximized better.
Pedestrian age estimation based on deep learning

The large-scale distribution of camera networks in the traffic area resulted in the increasing popularity of video surveillance systems. As pedestrian detection and tracking are the critical monitoring targets in traffic surveillance, many studies focus on pedestrian detection algorithms across cameras. This paper addressed the effect of using the age estimation based on deep convolution neural network (CNN) as a convenience for pedestrian monitoring who is crossing at intersections. Two popular deep convolutional neural networks (DCNNs) pre-trained models have been used in this work, which have recently achieved the best performance in facial features extraction tasks: VGG-Face and ResNet-50. We combined these two models to increase the efficiency of the proposed system. We ran our experiments to evaluate the system based on the VGGFace2 dataset consisting of 3.31 million face images. From the experimental results, we observed a gap in the detection performances between those age groups: children from (00-10) years and elderly with 55 years and more. Moreover, it noted that the proposed pedestrian age estimation model performance is high, also a good result can be obtained by using the model for new purpose.
The Role of Model Selection in Preference Learning

Learning preferences of an agent requires choosing which preference representation to use. This formalism should be expressive enough to capture a significant part of the agent's preferences. Selecting the right formalism is generally not easy, as we have limited access to the way the agent makes her choices. It is then important to understand how ``universal" particular preference representation formalisms are, that is, whether they can perform well in learning preferences of agents with a broad spectrum of preference orders. In this paper, we consider several preference representation formalisms from this perspective: lexicographic preference models, preference formulas, sets of (ranked) preference formulas, and neural networks. We find that the latter two show a good potential as general preference representation formalisms. We show that this holds true when learning preferences of a single agent but also when learning models to represent consensus preferences of a group of agents.
TEHNIK PEMERIKASAAN DAN PENGOLAHAN HASIL ASESMEN

This study aims to explain the technique of examining the assessment results using the library research research method, namely the library approach by examining books, journals, and other relevant sources. The result of this authorship is a technique of checking the results of the assessment, which is a technique of scoring the raw assessments that have been carried out by educators during the teaching process or the results of examinations. The examination of the results of the assessment carried out is to score in the cognitive, psychomotor and affective domains. The processing of the assessment results is the stage of converting the score into a value using the benchmark reference assessment (PAP) approach or the norm reference assessment (PAN) approach
Draft of the Third National Standard of Russia for Fast Automatic Learning of Large Correlation Neural Networks on Small Training Samples of Biometric Data

The aim of the study is to show that a biometrics-to-access code converter based on large networks of correlation neurons makes it possible to obtain an even longer key at the output while ensuring the protection of biometric data from compromise. The research method is the use of large «wide» neural networks with automatic learning for the implementation of the biometric authentication procedure, ensuring the protection of biometric personal data from compromise. Results of the study - the first national standard GOST R 52633.5 for the automatic training of neuron networks was focused only on a physically secure, trusted computing environment. The protection of the parameters of the trained neural network converters biometrics-code using cryptographic methods led to the need to use short keys and passwords for biometric-cryptographic authentication. It is proposed to build special correlation neurons in the meta-space of Bayes-Minkowski features of a higher dimension. An experiment was carried out to verify the patterns of kkeystroke dynamics using a biometrics-to-code converter based on the data set of the AIConstructor project. In the meta-space of features, the probability of a verification error turned out to be less (EER = 0.0823) than in the original space of features (EER = 0.0864), while in the protected execution mode of the biometrics-to-code converter, the key length can be increased by more than 19 times. Experiments have shown that the transition to the mat space of BayesMinkowski features does not lead to the manifestation of the “curse of dimension” problem if some of the original features have a noticeable or strong mutual correlation. The problem of ensuring the confidentiality of the parameters of trained neural network containers, from which the neural network converter biometrics-code is formed, is relevant not only for biometric authentication tasks. It seems possible to develop a standard for protecting artificial intelligence based on automatically trained networks of Bayesian-Minkowski correlation neurons.
Developing the Mandarin e-Module of “Small Talk at Workplace” for MFL learners in Malaysia

Research carried out in Malaysia identified that learners of Mandarin as a foreign language (MFL) in Malaysia were keen "to make small talk" and "to communicate with Mandarin speaking colleagues at workplace".However, a survey of the Mandarin learning materials available in the market indicates that there are plenty of learning materials for "General Mandarin" for daily use and materials for "Business Mandarin" but none for "Workplace Mandarin".Hence, there is an urgent need for Mandarin experts to produce suitable and comprehensive materials specifically for Mandarin for workplace communication.In contributing to the needs of "Workplace Mandarin", the Mandarin e-Module of "Small Talk at Workplace" was developed to fulfil Mandarin language learners' needs "to make small talk" and "to communicate with Mandarin speaking colleagues at workplace".For this purpose, ADDIE Model, a systematic approach consists of instructional design principles was adopted to develop the e-module of "Small Talk at Workplace".The ADDIE model consists of five phases: analysis, design, development, implementation, and evaluation.It is a strategic plan for designing an e-module and video clips incorporated with Powtoon animation were selected as the platform to deliver the e-content developed for the e-module.The completion of the Mandarin e-Module of "Small Talk at Workplace" is important as it can be marketed as an e-learning toolkit.The toolkit will help learners to make effective small talk as a social skill where effective small talk can be a useful tool to help them achieve goals they set in their work life.
Ferramentas tecnológicas como suporte ao compartilhamento de informações e conhecimento: um estudo de caso em uma instituição federal de ensino durante a pandemia de Covid-19

In 2020, the world experienced one of the most chaotic scenarios in terms of public health in recent times, as the pandemic caused by the new coronavirus (COVID-19) led to the death of thousands of people. In order to minimize the spread of the virus, organizations had to readjust their ways of working due to the recommendations coming from the World Health Organization, and, as a consequence, it is now common to carry out activities in home office to maintain social distance. Given this scenario, this research aimed to analyze the importance of technological tools to support the information and knowledge sharing in a Federal Educational Institution (FEI) during the COVID-19 pandemic. From a methodological point of view, this is a case study, in which semi-structured interviews were used with managers of an IFE Campus. As a data analysis technique, content analysis was used from the perspective of Bardin (2011). To speed up the content analysis, the IRaMuTeQ software was used. As main results, it was found that the routine of information and knowledge sharing among the managers of this institution changed during the pandemic, taking place with the intensive use of technological tools. However, this, in addition to bringing advantages such as the continuity of work and flexibility in administrative flows, brought disadvantages such as the change in daily life, impacting on the distribution of hours for the personal/private and working hours. This research can contribute to other institutions that have been experiencing similar situations during the pandemic.