Reviewerly/exHarmony huggingface dataset:
PERSEPSI MAHASISWA TERHADAP PENGAJAR PENUTUR JATI DALAM PEMBELAJARAN KETERAMPILAN BERBICARA BAHASA MANDARIN

Recently in the era of globalization, mastery of foreign languages ​​is immensely needed. In addition to English, a foreign language that has been recognized as an international language is Chinese. Universitas Negeri Jakarta (UNJ) is one of the educational institutions that opened the Chinese Language Education Study Program/Program Studi Pendidikan Bahasa Mandarin (PSPBM) since 2014. In 2015, PSPBM UNJ began to involve native Chinese speaker teachers to teach Chinese speaking skills. Native speaker teachers are one of the influential factors in foreign language teaching, merely because native speaker teachers are considered as a role model of perfect pronunciation, regularity of language structure and vocabulary, also the performer of communicative learning. The purpose of this study was to determine the student's perception of the native Chinese speaker in teaching Chinese speaking skills which includes three aspects, namely pedagogical aspects, linguistic aspects, and socio-cultural aspects. This research is a descriptive research with survey method. The results showed that the perception of PSPBM UNJ students towards native Chinese-speaking teachers was generally positive, students generally assumed that the teachers met the indicators contained in the pedagogical aspects, linguistic aspects, and socio-cultural aspects. Positive perceptions of native Chinese teachers have a good influence on the Mandarin language learning process at PSPBM UNJ.
TEENAGER’S SOCIAL ATTITUDES: POSSIBILITIES OF DIGITAL PEDAGOGICAL DIAGNOSIS

The relevance of the topic depends on the particular importance of asocial attitude diagnosing for the educational process in connection with the cyberspace development and the organization of a digital educational environment.&#x0D; The purpose of the article is a description of a diagnosis method of students’ social attitudes for teaching purposes.&#x0D; The method materials: the opportunities of using of metaphorical cards of association for the diagnosis of peculiar properties of students’ social attitudes in relation to five key objects: network security, cooperation, creativity, self-determination, continuing education. The authors offer the MAK operation algorithms as a way ofthesocial attitude diagnosing and the technology of a result interpretation.&#x0D; Conclusions and areas of application: the diagnosis gives the opportunity to analyze the level of a social attitude formation based on a three-pillar model (cognitive, affective, connotative components), the produced results can be used to implement the Training and Universal Educational Actions Development Program.
Implementasi Algoritma Naive Bayes Classifier (NBC) Dan Information Gain Untuk Mendeteksi DDoS

In this study, feature selection was also carried out using the information gain method, the result of feature selection improve the performance of DDoS attack detection against the Naive Bayes Classifier classification algorithm. The results obtained in this study are system testing on the results of the comparison of data performance that has been selected using 17 features and without the application of information gain feature selection using 43 features of course different, there are superior results from the application of Information Gain feature selection with an average accuracy value of 75.81 %, while the results obtained without the application of feature selection are 75.57%. The average precision level system performance using 17 features is 91.61%, while average precision result using 43 features is 92.20%. For the average recall value using 17 features, it is 57.63%, and results recall uses 43 features by 57.31%. In terms of execution time, the time required to execute the program using 17 features is faster and more effective, namely 89.17 seconds, while the program execution time using 43 features is longer, namely 205.34 seconds.
Applicability Analysis of Knowledge Graph Embedding on Blended Threat

With the advent of massive IoT in which objects and humans form highly dense interconnections, the development of new technologies and platforms has been significantly accelerated. In addition, networks and sensing technologies are being blended in various contexts, such as smart factories, digital health, and smart grids, etc. This hyper-connectivity of the blended environment has caused the diversification of the IoT environment and architecture and led to attack surface. Accordingly, the complexity of analyzing and responding to the security breaches is increasing. Hence, recent research has been focused on responding to the potential attack routes using a knowledge graph, a concept that is used to analyze the correlations between the threat data and potentially attackable asset data. However, as the analysis utilizes a single dataset, it has limitation in analyzing and predicting complex threat information. Therefore, to predict and respond to the potential complex security risks on IoBE, a knowledge graph embedding model applicable to blended threats is analyzed in this study.
Web 3.0: The Risks and Benefits of Web 3.0 no Web 2.0, Web 1.0

The internet as the most important international data media through which purchaser can percentage, read, and composes information thru pcs related to internet. In maximum current couple of years, net has had a number of development from web1.0 to web 3.0. The fundamental emphasis of the web or web 1. 0, in any other case referred to as the Exemplary net, confirmed up in 1991. Web1. 0 become tied in with associating and getting statistics at the net. Then, at that point, got here internet 2.0, empowering clients to peruse as well as make and convey content. because of the read and compose functionalities of net 2.0, there was a fantastic development within the at the web, uniquely in digital amusement close by net based totally enterprise. Web3.0 is by using and big viewed as the development of the semantic internet, wherein pc will creating and thinking new information in preference to human beings. utilizing man-made awareness and AI advances, clients will really want to speak with facts. The Metaverse is the destiny emphasis of the net. it'll consolidates numerous distinct virtual spaces that gives admittance to an assorted collection of diversion and undertakings using the overall scope of elevated reality
Evaluation of Traditional Machine Learning and Mixed Effect Machine Learning Model Performances by Simulation Study

Objective:The aim of this study is to examine the effectiveness of linear mixed-effects (LME) model, one of the traditional models used in the classification of clustered data, and mixed-effects machine learning models, which are the latest approaches.Material and Methods: For the simulation, various data sets were created with different number of groups (250, 500, 1000) and different sample sizes (5000, 10000, 15000).Within the scope of the simulation, LME model, mixed-effects random forest (MERF) and Gaussian process boosting (GPBoost) models were compared in terms of root mean square error (RMSE) on two functions.Results: When the error variance (EV) is 4 for the linear function, sample size is small and the number of groups is high, RMSE of MERF model is smaller.In all other scenarios, RMSE of the linear model was smaller and.In cases where EV for the nonlinear function is 1, the sample size is small and the number of groups is high, RMSE of MERF is smaller.In all other scenarios (while EV was 1), RMSE of GPBoost model was small, p<0.05 for the difference with LME, and p>0.05 for MERF.In cases where EV is 4 for the nonlinear function and the sample size and groups is high, RMSE of MERF is smaller.In all other scenarios (while EV was 4), RMSE of GPBoost model was smaller.Conclusion: As a conclusion, for a nonlinear function, GPBoost performed better than MERF and LME methods in terms of RMSE and time.However, when a linear function is considered, LME gives a better result.
MAN OF A NEW AGE IN THE CONTEXT OF DIGITAL SOCIALIZATION

The article characterizes the key competencies, qualities of a new person of a new era and ways to achieve this image. The risks of digital socialization are noted, and ways to overcome them in the educational process based on the moral principle of a person’s personality. The technosystem is an integral part of the ecosystem of the formation of a child’s personality, and digitalization is the system of work of an educational organization. At the present stage, personnel training is associated with the creation of technoparks of universal pedagogical competencies.
Research and design of a digital twin-driven smart river basin platform

&lt;p&gt;River basin management involves many issues including water resources, water ecology, water environment, and water disasters. Digital twins integrated with GIS, BIM and IoT are applied to river basin management, to establish digital twin data and model integration and visual expression methods, study the digital twin operation mechanism, build smart river basin twin, and design a digital twin-driven smart river basin platform. Taking the joint flood dispatching management and control of the reservoir group in the Duhe River Basin as an example. The application has proven that: compared with the problems of incomplete information, insufficient accuracy, lagging feedback and single expression in traditional basin database management or two-dimensional plane management, the digital twin-driven smart basin platform, on the basis of the integration and interaction of GIS data, BIM data and IoT data, and on the basis of data and model two-way drive, can realize simulation, decision making, optimization and visualization in external environment, and the control effect is better than traditional means. The research and practice of the platform can realize real-time monitoring, diagnosis, analysis, decision making and prediction for river basin management, providing a new solution for its intelligent operation, precise control and safe operation and maintenance.&lt;/p&gt;
The Contribution of Customer Satisfaction towards Company Image and its impact in Revolution Industry 4.0 Era

Purpose: This study focus on the crucial factors with the development of the workflows for enhancing the corporate images in the public company.&#x0D; Design/methodology/approach: The reviews were quantitative studies using three primary data (i.e., Observation, Questioners, and Interviews) with followed by data analysis and model with the workflow determination.&#x0D; Findings: The result shows company image variable is positively influenced by customer satisfaction variable. Also, the Quality of service reach the level of excellence expected and meet customer needs. The Quality of service becoming very important due to its understanding market and customer needs to and the product or services which should be given by the company. Quality of service is the crucial factor for a company to sustainable and make quality improvements in processing, packaging, and distributing for better company images. &#x0D; Research limitations/implications: Although, the Regional Water Management Company (PDAM) as a local monopoly company providing water for society, improvements in service need to be implemented for enhanced the company images. They should be developing to improve public services, in terms of qualities that qualify as clean water and quantity in the sense that more communities can be served.&#x0D; Practical implications: Therefore, it is necessary that the implementation of quality control management is planned, and implemented systematically and continuously, for the stability of the company. &#x0D; Originality/value: This paper is originality&#x0D; Paper type: Research paper
Universities and other Institutions – not Hate Speech Laws – are a threat to Freedom of Political Speech

One of the strongest arguments against hate speech legislation is the so-called Argument from Political Speech. This argument problematizes the restrictions that might be placed on political opinions or political critique when these opinions are expressed in a way which can be interpreted as ‘hateful’ towards minority groups. One of the strongest free speech scholars opposing hate speech legislation is Ronald Dworkin, who stresses that having restrictions on hate speech is, in fact, illegitimate in a liberal democracy. The right to express oneself freely concerning any political decision is, according to Dworkin, a core democratic principle; it is what self-governance – and hence liberal democracies – are built upon. Dworkin and many other free speech scholars based in the United States see hate speech legislation as a threat to expressing oneself freely and critically. I argue that Dworkin and other US-based free speech scholars tend to overlook actual hate speech legislation in countries where such laws have been implemented and have functioned for decades. Furthermore, I argue that the real threat against political speech lies not in hate speech legislation but rather outside of the law, namely, in private institutions such as universities and museums. Restrictions on political speech in various societal circumstances have been on the rise over the last decades – first and foremost in the US. I analyse why these restrictions on political speech are more widespread in the only Western country without laws against hate speech than they are in countries with implemented hate speech laws. Keywords: political speech, hate speech, hate speech legislation, private institu-tions, universities, USA
UPAYA MENINGKATKAN KEMAMPUAN MENULIS PUISI BEBAS KELAS V SD NEGERI 16 RANTAU BAYUR KABUPATEN BANYUASIN DENGAN MENGGUNAKAN MEDIA GAMBAR

This study aims to improve the ability to write free poetry for fifth grade students of SD Negeri 16 Rantau Bayur, Banyuasin by using picture media.This type of classroom action research (PTK).The stages of this research start from planning, implementing actions, observing and reflecting.The first cycle research took place on 15 January 2018 and the second cycle took place on 22 January 2018.The number of students as research subjects was 23 students.The data collected consists of data from student observations and the results of the last cycle test.The research success indicator is 75%.Students have been able to write poetry and have reached the KKM.Then the percentage of student learning outcomes is measured through observation during the study using image media.In cycle I and cycle II get an average final score, namely cycle I of 72% and cycle II of 86%.From the results of the analysis, it was concluded that the ability to write free poetry of fifth grade students of SD Negeri 16 Rantau Bayur, Banyuasin using image media could be improved.
DSPOTE: Density-induced Selection Probability-based Oversampling TEchnique for Imbalanced Learning

In imbalanced learning, oversampling is incredibly prevalent. However, it is disappointing that existing oversampling methods have their own limitations, such as new synthetic samples may be uninformative or noisy. To better address imbalanced learning tasks, this paper proposes a novel over-sampling method, named Density-induced Selection Probability-based Oversampling TEchnique (DSPOTE). To increase the number of samples in the minority class, DSPOTE designs a novel scheme for filtering noisy samples based on the Chebychev distance and a new way of calculating selection probability based on relative density. DSPOTE first filters noisy samples and then gets borderline ones from the minority class. Next, DSPOTE calculates the selection probabilities for all borderline samples and applies these probabilities to pick up borderline samples. Finally, DSPOTE generates synthetic samples for the minority class based on the selected borderline ones. Experimental results indicate that our method has good performance in terms of metrics, recall and AUC (Area Under the Curve), when compared with other eight methods.
Research on State Evaluation Method of Electronic Transformer Based on Multi-parameter Transient Strong Electromagnetic Disturbance

The electromagnetic compatibility environment of electronic transformer in substation is a parameter that the operation and maintenance personnel are concerned about while it’s difficult to be obtained. The research on state evaluation technology of the electromagnetic compatibility of electronic transformer can effectively prevent the electromagnetic compatibility faults and reduce the fault rate of electronic transformer. This paper proposes a method for evaluating the electromagnetic compatibility state of electronic transformer. Firstly, an evaluation index system is established. This index system covers two key sources of transient electromagnetic disturbance that affect electromagnetic compatibility state of electronic transformer, namely power port and signal port. The electromagnetic disturbance includes surge, electrical fast transient burst and damped oscillatory wave. Secondly, the weight of each factor is determined by the analytic hierarchy process method, and the membership function of each index is determined by fuzzy comprehensive evaluation method. This method makes up for the gap in the field of state evaluation of electromagnetic compatibility of electronic transformer, and gives early warning of electromagnetic compatibility faults and the evaluation results can guide the decision of electronic transformer in terms of operation and maintenance.
A Study on the Impact of Media Differences on Literature Acceptance

Since 20th century, with the expansion and rise of media, the constructiveness of media to human society and culture has become more and more significant.At present, many researchers agree that different channels of literary sources will affect literary activities, and help to get rid of the thinking inertia of confrontation between print media and new media, and theoretically explore the possibility of dialogue, integration and overall generation.This research is to try to find out the role of media in perceiving, judging and understanding literary activities through reading experiments of the same text in different media and channels, so that literary activities can become an important cultural form for improving our intellect and independence and a resource for perfecting human nature in the collision of media.
DPSB model-based clustering algorithm for mineral mapping in hyperspectral imagery

The Dirichlet Process based on Stick-Breaking (DPSB) model-based clustering algorithm was instigated to Hyperion remote sensing imagery to map alteration minerals related to porphyry copper deposit. The DPSB clustering results were evaluated using Normalized Information Distance (NID), Normalized Mutual Information (NMI) and Modified adjusted Rand index (MARI) methods. Comparing the DPSB results with the geological map of the study area reveals the MARI of 0.2253, NMI of 0.1799 and MARI of 0.2253, respectively. To verify the extent of propylitic, argillic, advanced argillic, propylitic-argillic and sericite alteration zones associated with porphyry copper deposit, a total number of 24 rock samples were measured by a SVC XHR-1024i field portable spectroradiometer. Results derived from the DPSB model-based clustering algorithm show high accuracy in distinguishing propylitic, argillic, advanced argillic, propylitic-argillic and sericite alteration zones. It is advocated that the DPSB model-based clustering algorithm can be broadly implemented to hyperspectral and multispectral remote sensing data for detecting alteration zones associated with porphyry mineralization systems in other metallogenic provinces around the world.
Multi-behavior recommendation algorithm based on multi-view item semantic learning

Multi-behavior recommender systems model and learn user preferences by using users' multiple behavioral interbehavior. However, the existing multi-behavior recommendation models usually ignore the independent features of each independent behavior and the personalized interbehavior relationship of users, and cannot fully model the item features. To this end, this paper proposes a multi-behavior recommendation algorithm based on multi-view item semantic learning. Graph neural network is used to mine item semantics from the perspective of independent behavior, and multi-head attention mechanism is used to learn the correlation between different behavior under the graph learning paradigm. At the same time, iterative propagation captures the collaborative signals in the user's multi-behavior information, and learns item semantic information from multiple perspectives. Compared with existing multi-behavior recommendation algorithms on Yelp and BeiBei datasets, considering item multi-view semantic learning can effectively improve recommendation accuracy.
A Nondeterministic Temporal Planning Model for Generating Narratives with Continuous Change in Interactive Storytelling

In this paper, we propose a temporal planning model for real-time generation of narratives in Interactive Storytelling systems. The model takes into account continuous branched time and the specification of constraints defined as temporal formulae over dramatic properties of the narrative (e.g. joy or tension). In order to address real-time generation, dramatic properties are modeled as varying linearly and events go through a preprocessing stage. As proof of concept, the model is incorporated into an existing storytelling system, LOGTELL, which provides a way to logically specify genres; allows user interaction to influence events in the unrolling narrative; and dramatizes the story in a 3D computer graphics world. To illustrate the generation of narratives, we present a simple narrative example in the Swords and Dragons genre.
Global Mittag-Leffler Synchronization Fractional Complex Value Neural Networks

This paper introduces the use of the separation method to solve the fractional complex-valued time-varying delay neural network. The number domain is divided into the real number domain and complex-valued domain, and the Lyapunov direct method is used to study it so that the drive-response system can achieve global Mittag-Leffler synchronization under a suitable controller. It is particularly emphasized that the controller adopts an adaptive control scheme, and then by constructing an appropriate Lyapunov function proof, and then using theoretical analysis, such as fractional-differential inequality, Li's rule, etc. Sufficient criteria for global Mittag-Leffler synchronization are available, as well as some sophisticated analysis techniques. Eventually, numerical simulation is used, and then the simulation is carried out. By simulating its behavior, it is proved that our theoretical analysis and results are correct.
Self-learning Methodology Based on Degradation Estimation for Underwater Image Enhancement

Underwater images suffer from degradation caused by water turbidity, light attenuation and color casting. An image enhancement procedure improves the perception and the analysis of the objects in the scene. Recent works based on deep learning approaches require synthetically paired datasets to train their models. In this work, it is present a self-learning methodology to enhance images without a paired dataset. The proposed method estimates image degradation from the input image and attenuate it from the image. The output image of an autoencoder is replaced in the loss function by a synthetically degraded version during the training. This procedure drives the network learning to compensate additional degradation. Thereby, the output image is an enhanced version of the input image. It is highlighted that the proposed algorithm requires only one image as input during the training. The results obtained using our method show its effectiveness of color preservation, color cast reduction, and contrast improvement.