Reviewerly/exHarmony huggingface dataset:
Construction of College Chinese Writing Practice Teaching Platform under the Background of Information Technology

Writing has always been one of the difficulties in teaching Chinese as a foreign language. With the advent of the information society, modern information technology, represented by networking, digitalization, multimedia, and intelligence, has become a creative tool to expand human capabilities, changing people’s traditional way of life, study, and work. Guided by the theory of integration of information technology and curriculum, combined with the characteristics of Chinese writing, this paper puts forward that the goal of integration of information technology and Chinese writing is to change the traditional teaching structure, create an ideal environment for students to learn Chinese and improve their Chinese information literacy. A practical teaching platform for college Chinese writing is constructed, which is designed from the user role and based on user access control. The design includes the roles of graduate students, foreign teachers, administrators, and users. Each user has different operation rights, module rights, and management rights. According to a small amount of online information and the results of a previous course, we can make a good prediction. The results show that this method is effective, and we can predict students’ academic performance through their online behavior.
The Potential of Agricultural Land Drought Using Normalized Difference Drought Index in Ciampel Subdistrict Karawang Regency

Karawang Regency, known as the National Rice Reserve, is experiencing drought on farmland.The Regional Disaster Management Agency (BPBD) of Karawang Regency noted that drought in 14 villages spread across three sub-districts in Karawang Regency has developed in 2019, such as Ciampel sub-district.Rice production decreased in 2015-2019 by 19 percent.The purpose of this study is to analyze the drought area of agricultural land using the Normalized Difference Drought Index (NDDI) and analyze the relationship between agricultural land drought and rainfall in Ciampel Sub-District, Karawang Regency in 2015 and 2019.The study used Landsat 8 OLI/TIRS in August-September 2015 and 2019.Agricultural land drought using the NDDI method is the ratio between the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Wetness Index (NDWI).The results showed a map of the distribution of agricultural land drought in Ciampel Sub-district, Karawang Regency during 2015 and 2019 with three classes of agricultural land drought (dry, rather dry, normal).The total area of agricultural drought in August 2015 was 11,166 hectares and as of September 2019 was 3,119 hectares.While as of September 2015, it was 3,086 hectares, and in 2019 was 3,158 hectares.The drought that hit Ciampel Sub-District in September 2019 hit almost all areas and dry areas in the middle eastern part of the Ciampel Sub-District.The drought, which is included in the classification of dry that hit irrigated rice field, was 20.19 %.Meanwhile, the rainfed rice field was 32.79%, and in dryland was 24.83%.
A Mathematics Teacher’s Training to Create a Maker Space in Mathematics Lessons by Means of GeoGebra

The article presents a description of the experience of training mathematics teachers to create a maker space in mathematics lessons using the GeoGebra package in the context the program and methods of advanced training courses.A survey of mathematics teachers was conducted, which shows the need to create refresher courses for the implementation of STEAM practices in the educational process of general secondary education. The survey allowed us to establish that the main means of implementing STEM education in mathematics lessons teachers consider the creation of a maker space using the GeoGebra math package.The statistical processing of experimental results on the effectiveness of training mathematics teachers in advanced training courses to create a maker space in mathematics lessons using GeoGebra shows that mathematics teachers have a high level of motivation to implement various ways to implement the concept of STEAM education and acquire relevant STEAM competencies. But the level of their knowledge and the level of their reflection of the relevant professional activity need to be improved through the creation of advanced training courses on the implementation of STEAM practices in the educational process of general secondary education institutions.
Application of Ontology Matching Algorithm Based on Linguistic Features in English Pronunciation Quality Evaluation

Traditional English classroom teaching is difficult to meet the oral learning needs of most learners. Thanks to the continuous advancement of speech processing technology, computer-assisted language learning systems are becoming more intelligent, not only pointing out learners' pronunciation errors but also assessing their overall pronunciation level. Method. This paper uses the method of tree kernel function to measure the similarity of two ontology trees. According to the features of nodes in ontology tree, methods to calculate the external features and internal features of nodes are proposed, respectively. External features are mainly obtained by calculating the hierarchical centrality, node density, and node coverage of nodes in the ontology tree; internal features are mainly obtained by measuring the richness of internal information. According to the similarity of ontology tree and the external features and internal features of nodes, the calculation formula of structural comprehensive similarity is improved, and the features of ontology itself can be fully considered in the calculation. According to the difference of the structure, the weights of the corresponding features during the calculation are adjusted autonomously, so that the calculation results are closer to reality. In spectral image preprocessing, endpoint detection utilizes the harmonic characteristics presented by narrowband spectrograms with high frequency resolution and eliminates useless nonspeech segments by detecting the presence of voiced segments. When building the neural network model, four convolutional layers, two fully connected layers, and one softmax output layer were conceived, and dropout was used to randomly suspend the work of some neurons to avoid overfitting. Results/Discussion. Through the data analysis of mean and variance and verified by one-way analysis of variance, it proves that the sentiment evaluation method in this paper is effective. The traditional multiple linear regression method is not suitable for the corpus and application scenarios of this paper. This paper proposes a decision tree structure, which is similar to the overall scoring process of raters, and uses the Interactive Dicremiser version 3 (ID3) algorithm to build a comprehensive evaluation decision tree for pitch, rhythm, intonation, speech rate, and emotion indicators. It is proved by experiments that the accurate consistency rate of the human-machine evaluation in this paper is 93%, the adjacent consistency rate is 96%, and the Pearson correlation coefficient value of the human-machine evaluation results is 0.89. The data results prove that the evaluation method in this paper is credible.
Canteen Food Ordering and Managing System

The ‘Canteen Food Ordering and Managing System’ digitalize the operations inside a canteen. Initially the user must create an account, and then he can make use of the services. A menu will be provided from which the customer can choose the desired item then he can make payment online. In the proposed system the paper works will be less. The users can also make use of the virtual queue technique by which they can avoid long queues. The digitalization of the canteen system will help the management to provide a best-in-class service to the customers and which will also contribute in better time management. At last, the customers can also share their feedback through the website according to which the management can improve their services. Thus the online system will be helpful for both customers and the canteen.
Copula-based Risk Aggregation with Trapped Ion Quantum Computers

Copulas are mathematical tools for modeling joint probability distributions. Since copulas enable one to conveniently treat the marginal distribution of each variable and the interdependencies among variables separately, in the past 60 years they have become an essential analysis tool on classical computers in various fields ranging from quantitative finance and civil engineering to signal processing and medicine. The recent finding that copulas can be expressed as maximally entangled quantum states has revealed a promising approach to practical quantum advantages: performing tasks faster, requiring less memory, or, as we show, yielding better predictions. Studying the scalability of this quantum approach as both the precision and the number of modeled variables increase is crucial for its adoption in real-world applications. In this paper, we successfully apply a Quantum Circuit Born Machine (QCBM) based approach to modeling 3- and 4-variable copulas on trapped ion quantum computers. We study the training of QCBMs with different levels of precision and circuit design on a simulator and a state-of-the-art trapped ion quantum computer. We observe decreased training efficacy due to the increased complexity in parameter optimization as the models scale up. To address this challenge, we introduce an annealing-inspired strategy that dramatically improves the training results. In our end-to-end tests, various configurations of the quantum models make a comparable or better prediction in risk aggregation tasks than the standard classical models. Our detailed study of the copula paradigm using quantum computing opens opportunities for its deployment in various industries.
How First and Second Languages Influence Indonesian Students’ English as a Third Language: Transfer and Interference Analysis

In the face of the scarcity of linguistic studies on Batak Toba language, this study tries to contribute by investigating how Batak Toba students’ local language and mother tongue’s elements are transferred to their English as a foreign language output. Specifically, this study aims to describe the transfer and interference of Batak Toba and Indonesian as first and/or second language and how they influence learners’ English as a third language. The study observed 15 English Education programme students who were selected via snowball sampling and analysed the way the students speak and translate a set of phrases to their respective second and third languages. Results indicated that the students’ first and second languages interferes with their English language performance at the phonological, morphological, and syntactic levels. The novelty of this study is that it offers a detailed investigation of how students whose first language, Batak Toba, and second language, Indonesian, interfere with the foreign language that is a mandatory subject in the country’s formal education system, namely English. Recommendations for teachers, particularly teachers of Indonesian learners whose students are primarily bilingual and multilingual, are discussed.
Node to Node Communication Security in IOT Networks

Abstract: The Internet of Things (IoT) describes the network of physical objects or things that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet. Although several IoT devices are openly accessible to all in the network, it is extremely vital to be aware of the security risks and threats of cyber- attacks; therefore, it should be secured. In Cryptography, plain text is converted to encrypted text before it is sent, and it is converted to plain text after communication on the other side. Steganography is a method of hiding secret data, by embedding it into an audio, video, image, or text file. One technique is to hide data in bits that represent the same color pixels repeated in a row in an image file. In this project it proposes to encrypt the IoT networks data by Cryptography method and hide the encrypted message inside an image file using Steganography method as well increases the number of bits that can be saved within a pixel of an image. To incorporate the usage of convolutional neural networks in traditional steganography method to drastically increase the payload that can be transmitted through an image. Thus, in this project the convolutional networks algorithm will be developed and trained in such a way to increase the payload of the data to be encrypted as well as safely decrypted to view the original message.
Quantum Linear System Algorithm for General Matrices in System Identification

Solving linear systems of equations is one of the most common and basic problems in classical identification systems. Given a coefficient matrix A and a vector b, the ultimate task is to find the solution x such that Ax=b. Based on the technique of the singular value estimation, the paper proposes a modified quantum scheme to obtain the quantum state |x⟩ corresponding to the solution of the linear system of equations in O(κ2rpolylog(mn)/ϵ) time for a general m×n dimensional A, which is superior to existing quantum algorithms, where κ is the condition number, r is the rank of matrix A and ϵ is the precision parameter. Meanwhile, we also design a quantum circuit for the homogeneous linear equations and achieve an exponential improvement. The coefficient matrix A in our scheme is a sparsity-independent and non-square matrix, which can be applied in more general situations. Our research provides a universal quantum linear system solver and can enrich the research scope of quantum computation.
Offline and Online Evaluation for Recommender Systems

Recommender systems aim to facilitate decision making for users by offering them information according to their preferences, they are now popular in several application domains. The evaluation of recommender systems is very important to have an effective application in practice. In addition, it focuses to find better algorithms and evaluate their performance. However, researchers did not give much attention to it in this field. There are various ways to evaluate a recommender system. In this paper, we will discuss the main types of evaluations in this domain, which are offline and online evaluation, we will start with an overview of recommender systems, then we will present each type of evaluation, and we will compare the offline and the online evaluation for recommender systems. We will base on ten factors which are, the reproducibility, the reliability of the results of each type of evaluation, the preparation cost, the evaluation, the stability, the possibility of extensibility that’s mean if we can add new metrics or not, the scalability, the passed time, deep analysis, and the sparsity metric. Finally, we will discuss the factors which are presented in the comparison.
English Learning Using Infographics for Balinese Secondary School Students

As the facilitator of the learning process, the teacher needs to utilize appropriate media for students to help them learn better. Visual media is considered a great help as it provides information more attractively. Thus, this study aimed to develop infographics as an English teaching medium for 8th-grade junior high school students. The subject of this research was the English learning media used by the 8th-grade teacher at SMPN 4 Singaraja. This study was a Design and Development research which consisted of 3 steps of research, namely design, development, and evaluation. The researcher's notes, infographic development blueprints, instrument validation, and expert judgment were used to collect the data. The data were from the researchers' notes, and the infographic development blueprint was described qualitatively. Meanwhile, the score obtained from the instrument validation and the results of the expert judgment were explained quantitatively. The final result indicated that the infographic is very good to be used as an English teaching medium, especially for the 8th grade of junior high school students. Therefore, this research concluded that by developing books containing infographics, English teachers could have another type of visual media to teach all topics in grade 8 by using infographics.
Dialectal Arabic sentiment analysis based on tree-based pipeline optimization tool

The heavy involvement of the Arabic internet users resulted in spreading data written in the Arabic language and creating a vast research area regarding natural language processing (NLP). Sentiment analysis is a growing field of research that is of great importance to everyone considering the high added potential for decision-making and predicting upcoming actions using the texts produced in social networks. Arabic used in microblogging websites, especially Twitter, is highly informal. It is not compliant with neither standards nor spelling regulations making it quite challenging for automatic machine-learning techniques. In this paper’s scope, we propose a new approach based on AutoML methods to improve the efficiency of the sentiment classification process for dialectal Arabic. This approach was validated through benchmarks testing on three different datasets that represent three vernacular forms of Arabic. The obtained results show that the presented framework has significantly increased accuracy than similar works in the literature.
The Legal Regime of Smart Contracts in Public Procurement

This article attempts to critically review the applications of smart contracts in public procurement. The literature on the topic is characterized by an emphasis on potential advantages and uses of this emerging technology, while it lacks in the concrete practical implementations of smart contracts in public procurement. In this context, we wish to realistically outline the legal regime of smart public procurement contracts. For this, we analyze the potential use of blockchain and smart contracts in public procurement at two stages: contract award and contract execution. Our article also discusses case studies of smart public procurement contracts, in order to assess their compatibility with and their impact on the EU public procurement system.
The Effect of Comic Strip as Instructional Method in Enhancing Students’ Writing Skills

There have been several studies on the use of comics as an instructional tool for reading, but less on their use to writing, particularly descriptive text. The goal of this study is to find out how the comic strip affects the writing skills of students in the Islamic education program STAIN Mandailing Natal, especially their descriptive writing skills . This study was a experiment method. It was done by two classes, one called the experimental class and the other called the control class. Each class had 35 students. The Random sampling is used in the sampling of this study. This study uses SPSS windows 10 or statistical analysis to construct the T-Test while analyzing the data. This research finds that comic strips may improve students' descriptive writing. Through comic strips, students enhanced their writing abilities in all areas. The learning process is more efficient because of the use of simple language, clear images, and follow-up questions to ensure that everyone understood what was going on.
Dominating set based arbitrary oriented bilingual scene text localization

&lt;p&gt;Localizing and recognizing arbitrarily oriented text in natural scene images is the biggest challenge. It is because scene texts are often erratic in shapes. This paper presents a simple and effective graph representational algorithm for detecting arbitrary-oriented text location to smoothen the text recognition process because of its high impact and simplicity of representation. An arbitrarily oriented text can be horizontal, vertical, perspective, curved (diagonal/off-diagonal), or even a combination. As a pre-processing step, image enhancement is performed in the frequency domain to improve the representation of images that are invariant to intensity. It is necessary to draw bounding boxes for each candidate character in the scene images to extract text regions. This step is carried out by utilizing the advantage of the region-based approach called maximally stable extremal regions. A typical problem with curved text localization is that non-text objects may occur within localized text regions. Our method is the first in the literature that searches for dominating sets to solve this problem. This dominating set method outperforms several traditional methods, including deep learning methods used for arbitrary text localization, on challenging datasets like 13&lt;sup&gt;th&lt;/sup&gt; international conference on document analysis and recognition (ICDAR 2015), multi-script robust reading competition (MRRC), CurvedText 80 (CUTE80), and arbitrary text (ArT).&lt;/p&gt;
Analysis and assessment software for multi-user collaborative cognitive radio networks

&lt;span&gt;Computer simulations are without a doubt a useful methodology that allows to explore research queries and develop prototypes at lower costs and timeframes than those required in hardware processes. The simulation tools used in cognitive radio networks (CRN) are undergoing an active process. Currently, there is no stable simulator that enables to characterize every element of the cognitive cycle and the available tools are a framework for discrete-event software. This work presents the spectral mobility simulator in CRN called “App MultiColl-DCRN”, developed with MATLAB’s app designer. In contrast with other frameworks, the simulator uses real spectral occupancy data and simultaneously analyzes features regarding spectral mobility, decision-making, multi-user access, collaborative scenarios and decentralized architectures. Performance metrics include bandwidth, throughput level, number of failed handoffs, number of total handoffs, number of handoffs with interference, number of anticipated handoffs and number of perfect handoffs. The assessment of the simulator involves three scenarios: the first and second scenarios present a collaborative structure using the multi-criteria optimization and compromise solution (VIKOR) decision-making model and the naïve Bayes prediction technique respectively. The third scenario presents a multi-user structure and uses simple additive weighting (SAW) as a decision-making technique. The present development represents a contribution in the cognitive radio network field since there is currently no software with the same features.&lt;/span&gt;
Mobile-based Primate Image Recognition using CNN

Six out of 25 species of primates most endangered are in Indonesia. Six of these primates are namely Orangutan, Lutung, Bekantan, Tarsius tumpara, Kukang, and Simakobu. Three of the six primates live mostly on the island of Borneo. One form of preservation of primate treasures found in Kalimantan is by conducting studies on primate identification. In this study, an android app was developed using the CNN method to identify primate species in Kalimantan wetlands. CNN is used to extract spatial features from primate images to be very efficient for image identification problems. The data set used in this study is ImageNets, while the model used is MobileNets. The application was tested using two scenarios, namely using photos and video recordings. Photos were taken directly, then reduced to a resolution of 256 x 256. Then, videos were taken in approximately 10 to 30 seconds with two megapixel camera resolution. The results obtained was an average accuracy of 93.6% when using photos and 79% when using video recordings. After calculating the accuracy, the usability test using SUS was performed. Based on the SUS results, it is known that the application developed is feasible to use.