Reviewerly/exHarmony huggingface dataset:
THE CORRELATION BETWEEN STUDENTS GRAMMAR MASTERY AND WRITING ABILITY

The Objective of this research was to find out the significant correlation between students’ grammar mastery or language features of recount text and writing recount text ability of the tenth grade of SMK SATRYA BUDI PERDAGANGAN. This research used correlation research design. The population of this research was 88 students and research sample was from X 1 RPL which consisted of 30 students which selected by using cluster sampling technique. Technique of data collection was tests. In analyzing the data, the writer applied Pearson’s Product Moment Correlation Coefficient and Significance of Coefficient Correlation formula. The result of the research showed that tcount is 3,125 and the ttable at significant level 0f 5% (0,05) with 28 degree of freedom (df) was 2,048. It means that tcount is higher than ttable. It can be concluded that alternative hypothesis is accepted and null hypothesis is rejected. It means that there is significant correlation between students’ grammar mastery or language features of recount text and writing recount text ability
Manajemen Mutu Terpadu

The quality of education is basically very important for developed because if it is not developed then quality itself will not experience increases because the computer can develop according to the development of the age so that the quality of education is increasingly improved from year to year.
administrasi pendidikan

This article describe about the importance management of means and infrastructure. because with means and infrastructure adequate education, then the learning process and teach will be going smoothly with efficient and efective
Deep Genetic Programming

We propose to develop a Deep Learning (DL) framework based on the paradigm of Genetic Programming (GP). The hypothesis is that GP non-parametric and non-differentiable learning units (abstract syntax trees) have the same learning and representation capacity to Artificial Neural Networks (ANN). In an analogy to the traditional ANN/Gradient Descend/Backpropagation DL approach, the proposed framework aims at building a DL alike model fully based on GP. Preliminary results when approaching a number of application domains, suggest that GP is able to deal with large amounts of training data, such as those required in DL tasks. However, extensive research is still required regarding the construction of a multi-layered learning architecture, another hallmark of DL.
A Blockchain Policy and Charging Control Framework for Roaming in Cellular Networks

As a technology foundation of cryptocurrencies, blockchain enables decentralized peer-to-peer trading through consensus mechanisms without the involvement of a third party. Blockchain has been regarded as an auspicious technology for future cellular networks. It is able to provide solutions to problems related to mobile operators and user trust, embedded smart contracts, security concerns, pricing (e.g. for roaming), etc. When applying blockchain to cellular networks, there are significant challenges in terms of deployment and application, due to resource-constrained transactions. This article begins by introducing the basic concept of blockchain and then moves on to illustrate its benefits and limitations in the roaming system. Two models of roaming-based blockchain technologies are offered to show their suitability for cellular networks as opposed to traditional technology. Finally, potential issues and challenges of roaming-based blockchains are addressed and evaluated using the roaming use case in the EU.
Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis

The ambiguity of the decision-making process has been pointed out as the main obstacle to applying the deep learning-based method in a practical way in spite of its outstanding performance. Interpretability could guarantee the confidence of deep learning system, therefore it is particularly important in the medical field. In this study, a novel deep network is proposed to explain the diagnostic decision with visual pointing map and diagnostic sentence justifying result simultaneously. For the purpose of increasing the accuracy of sentence generation, a visual word constraint model is devised in training justification generator. To verify the proposed method, comparative experiments were conducted on the problem of the diagnosis of breast masses. Experimental results demonstrated that the proposed deep network could explain diagnosis more accurately with various textual justifications.
Restaurant food ordering method using NFC technology

&lt;span&gt;Mobile apps of various kinds continue to emerge as smartphones become more popular and mobile telecommunication matures. NFC is the most common wireless technology used in smartphones presently. It is used in mobile devices having built-in NFC to allow consumers to input payments, receive messages, and do nearer device recognition, among other things. The paper proposes the NFC technology-based food ordering system for all the customers who want to have food at their residence only. The results show that the application works properly in finding the nearby restaurants and completing the food order without any hustle.&lt;/span&gt;
Educational Philosophy of Professor Deepak Kumar Behera

Abstract: Education is a powerful agent of change, and improves health and livelihoods, contributes to social stability and drives long term economic growth. Like Tagore, Gandhi, Aurobindo, Vivekananda, Professor Deepak Kumar Behera is also a philosopher as well as educationalist. His contribution in the field of education is a milestone in the history of Odisha. His contribution towards Anthropology and Tribal Studies have the tremendous impact. His works and research studies are now selected for college and universities syllabus for respective subjects. He has really a great impact on every field such as anthropology, gender studies, children and childhood and tribal studies. Professor Deepak Kumar Behera, eminent Educationist, ace Anthropologist, able Administrator, and brilliant Author. In the view of Professor Behera education is the most prominent weapon to deal with every situation of life. The objectives of the present study entitled “Educational Contribution of Professor Deepak Kumar Behera” is to know his biography, his educational philosophy and relevance of his educational contribution to the present Indian situation. This qualitative research was conducted by direct interview method with Professor Behera. The conclusion of the research that Professor Behera’s educational system can develop an eminent and marvellous education system whose main aim to provide quality education to all irrespective their individual characters. The purpose of the study to educate every individual and which leads for a non-discriminatory society.
Study of Effective AI Algorithms in Natural Language Processing in Todays Era

Abstract: In today’s modern era of technology, text analysis and Natural Language Processing had become crucial in artificial intelligence. Natural language is a specific language which helps machine in reading the text with the help of stimulating the ability of human for understanding a natural language like English or any other language. It consists of a theory-motivated range of computational techniques which can be useful for automatic text analysis and representation of human language. There has been an advancement in state-of-model in recent years but they have become less interpretable. The research paper gives details about the operations and expansion techniques that are used for NLP model predictions in detail and it focuses on recent developments in NLP to look future of NLP related algorithms and techniques. Also, we find out which NLG (Natural Language Generation) evaluation measures is well related
Learning to Optimize Decision Boundary for One-Shot One-Class Image Recognition

Abstract In this paper, we study One-Shot One-Class classification (OSOC), where the aim is to identify the objects of a specific class amongst all objects by using only one single sample of that class as training data. The key challenge of this task is how to learn a robust decision boundary to distinguish a class in such an extreme case. Towards this goal, we propose a novel meta-learning strategy to meta learn a mapping function from the exemplar image of a target class to the categorical classifier as well as a reference score for this class. The classifier is applied to a testing sample to predict the classification score, which will be compared against the reference score to decide whether this sample belongs to the target class or not. To realize this idea, in the meta training phase, we propose a ranking loss that employs the reference score as the threshold to distinguish samples both from the target class and other non-target classes. On top of this basic loss function, we develop another two terms: a fidelity term to ensure the learned classifier has high fidelity to the exemplar and a learnable slack term to adaptively adjust the reference score on the fly. These two terms compete against each other and consequently seek a balance to optimize the decision boundary. Comprehensive experimental results on five OSOC datasets demonstrate the proposed method can obviously outperform state-of-the-art
Pengembangan Media Pembelajaran Interaktif Articulate Storyline Berbasis Literasi Digital Pada Pembelajaran IPS bagi Siswa Madrasah Ibtidaiyah Negeri

This research aims to analyze the quality, feasibility, and response of educators and students to the Storyline Articulated by interactive learning media based on digital literacy. The study was conducted in the even semester academic year of 2021/2022 at the Madrasah Ibtidaiyah Negeri 12 Cibubur. This research used the ADDIE model's research and development methods (Research and Development). Data from interviews and evaluations in the form of criticism and ideas were used in this research instrument. Meanwhile, data analysis techniques used data based on numbers such as questionnaires and validations. The validation test results by two validators, namely the media expert validator, obtained a percentage value of 72% in the interpretation into the feasible category, and the material expert validator obtained an average percentage value of 80% with a very feasible category. Educators' response to learning media obtained a score of 90% with a very decent category. Based on the research data, interactive learning media for digital Literacy-Based Articulation Storylines in class IV social studies learning at Madrasah Ibtidaiyah Negeri 12 Cibubur is suitable for learning activities.
Implementation of Intelligent Decision Model of Traditional Hanfu 3D Design Based on Internet of Things

In view of the problems existing in the design and application of traditional Hanfu, in order to better promote the application prospect of the Internet of Things in traditional Hanfu, the adaptive estimation model is adopted to analyze the relevant algorithms. Based on the Internet of Things theory, the optimized intelligent decision model is obtained, and the relevant parameters of the model are modified so as to obtain the application of traditional Hanfu in 3D simulation design based on the Internet of Things theory. Finally, the optimized model is used to predict and analyze the design and application of traditional Hanfu. The results show that the value of the Internet of Things in 2021 is 880, an increase of about 8.6 times compared with 102 in 2011, indicating that the industry shows a strong growth trend. In addition, the growth rate of the Internet of Things from 2018 to 2021 is low, indicating that the output value of the Internet of Things in this stage is relatively stable, and it also reflects from the side that the Internet of Things has entered a stable growth period. With the increase in the number of iterations, the corresponding curves of the two original trust degrees show little change, and the original trust degree curves are mainly divided into the stage of slow rise and the stage of steady increase. The influence of the optimization model on the willingness to trust is obvious, showing varying degrees of increase. Different calculation methods describe the test data differently. The mathematical estimation method shows an approximate linear trend. The result estimation method shows the change in the quadratic function. The key decision rules have the characteristic of subsection. Data method has strong fluctuation in the early stage. Objective function method can better describe the characteristics of traditional Hanfu. This study realizes the design and application of traditional Hanfu in the field of 3D simulation based on the Internet of Things theory so as to provide a theoretical basis for the application and research of the Internet of Things in other fields.
Individual versus simultaneous estimation of two parameters in a correlated noisy environment

Abstract Recently, the phase parametric estimation for a single qubit system driven by a phase noisy laser under non-Markovian dynamics has been reported. We here generalize the single-parameter estimation to the two-parameter scenario where a two-qubit system is influenced by correlated phase noisy laser, and compare the performances of two-parameter estimation in both individual and simultaneous strategies by using the quantum Fisher information and the quantum Fisher information matrix, respectively.Our results show the phase parametric estimation precision can be improved due to the memory effect arising from successive applications of the noisy laser. With the memory coefficient $\mu$ increasing, the precision of parameter estimation becomes more accuracy. Besides, the phase parameter estimation precision can be well enhanced by engineering the ratios of classical phase noisy laser rate and system-environment coupling strength dependent of Markovian or non-Markovian regions. What's more, we find the simultaneous estimation of two-phase parameters is not always advantageous over the individual strategy.
Physics-Informed Reinforcement Learning for Motion Control of a Fish-like Swimming Robot

Abstract Motion control of fish-like swimming robots presents many challenges due to the unstructured environment and un-modelled governing physics of the fluid-robot interaction. Commonly used low-fidelity control models using simplified formulas for drag and lift force do not capture key physics that can play an important role in the dynamics of small-sized robots with limited actuation. Deep Reinforcement Learning (DRL) holds considerable promise for motion control of robots with complex dynamics. Reinforcement learning methods require large amounts of data exploring a large subset of the relevant state space, which can be expensive, time consuming or unsafe to obtain. Data from simulations can be used in the initial stages of DRL, but in the case of swimming robots, large numbers of simulations are also infeasible from a time and computational resources perspective. Surrogate models that capture the primary physics of the system can be a useful starting point for training a DRL agent which is subsequently transferred to train with a higher fidelity simulation. We demonstrate the utility of such physics-informed reinforcement learning to train a policy that can enable velocity and path tracking for a planar swimming (fish-like) rigid Joukowski hydrofoil. This is done through a curriculum where the DRL agent is first trained to track limit cycles in a velocity space for a representative nonholonomic system and then transferred to to train on a small simulation data set of the swimmer. The results show the utility of physics-informed reinforcement learning for the control of fish-like swimming robots.
A TWO-LAYER CLASSIFICATION MODEL ON HUMAN ACTIVITY RECOGNITION BASED ON CLUSTERING ALGORITHM AND FEATURE SELECTION

Human activity recognition (HAR) has a wide application in daily life. With wearable sensors, people’s activity can be monitored, recorded and analysed. However, most existing methods did not make full use of human activity data and their features. In this paper, a new method based on feature selection and clustering algorithm is proposed. We established two-layer classification models, respectively, in the ankle, the chest, the wrist and the mixed accelerometer data. K-means clustering algorithm was first used to obtain a broad classification of the activities and then we conducted two rounds of classification, among which feature selection was performed in each layer. A significance analysis was also carried out in the final experiment and we compared the performance of the final model from the mixed accelerometer data with other algorithms, the results showed that the recognition performance of our model was significantly better, and the average [Formula: see text]1 score was as high as 0.969 in publicly available PAMAP2 dataset. Compared with other methods, our model achieved the highest recognition rate. The method proposed in this paper can greatly improve the recognition rate of human activity and effectively evaluate daily activity.
Network Security Analysis Using Virtual Private Network in Vocational School

Every organization chooses a virtual private network (VPN) as their top option since it is a practical and secure solution. Through the use of a virtual private network, this study seeks to evaluate the network in SMK (VPN). The descriptive qualitative research used in this study was carried out at the lab of SMK Negeri 1 Ampek Angkek. Data were gathered through observation, interviews, and document analysis studies. The method of data analysis employed descriptive analysis. The results of this study indicate that the activity on the VPN network is better than the Non-VPN because the activities carried out in the VPN tunnel are not known by others. Users find it simpler to finish their daily agenda as a result.
Nachinarkiniyar History and Textual Ability

Tamil language and literature have flourished with speeches composed by speechwriters. Are greatly aiding researchers who think innovatively. Texts serve as a bridge between linguistic research and e-literary criticism. The texts convey how the Tamil language has changed over time, as well as the living conditions, political changes and customs of the Tamil people. This article explores the history and textual ability of Nachinarkiniyar. Nachinarkiniyar was a knowledgeable and knowledgeable man of various arts, writing semantics for songs, and also possessing the art of religious ideas, music, drama, etc., which are included in the book. He is well versed in grammar, literature, dictionary, epic and puranam in Tamil. He is well versed in astrology, medicine, architecture, and crops. Nachinarkiniyar, who has written for Tamil grammar books, is well versed in the Vedic and phylogenetic theory of Sanskrit and is a university-oriented scholar of Tamil, Sanskrit scholarship, religious knowledge, land book knowledge, life and biology. This article explores the history and textual ability of Nachinarkiniyar.
Analysis Effect of Role-Playing Method Assisted by Audio Visual Media on Speaking Skills in Indonesian Language Learning for Elementary School Students

This study aims to analyze the effect of the role-playing method assisted by audio-visual media on speaking skills in Indonesian language learning at SDN Cibuntu 01. The method used in this research is quantitative as a kind of quasi-experimental research design with One Group Pretest-Posttest Design which only involves one group for initial treatment, treatment, and end of treatment. The research instrument was a speaking skill test consisting of 5 rules with 25 descriptors to measure students' speaking ability which experts had approved. The data analysis technique used in this study was the normality test using the Liliefors test and the homogeneity test using the Fisher test. Then, the hypothesis was tested using the t-test. Data processing was done with the help of SPSS 25 software with a significant level of 5% or =0.05. Based on the results of this study, it was obtained that the tcount &gt; ttable was 2.802 &gt; 1.701. As these results show, H₀ is rejected and H₁ is recognized. Therefore, the role-playing method assisted by audio-visual media affects speaking skills in Indonesian language learning for grade 3 students at SDN Cibuntu 01. The implication for learning Indonesian is that selecting the proper method can affect students' speaking skills. There are differences in speaking skills between Indonesian language learning and those taught before and after using the role-playing method with the aid of audio-visual media. It is seen from the confidence in playing the character; the voice volume becomes louder, the body movements are more flexible, understanding the story content well, pronunciation and proper intonation.
Students’ Perception of YouTube as Authentic Material in Online Learning

The purpose of the study was to analyze students' perceptions of YouTube as authentic teaching material in online classes. The research method uses qualitative. Participants were taken from the Grammar and Speaking classes as many as 50 students from the English Language Program at Universitas Indraprasta Jakarta. Data were collected using interviews and questionnaires. Data analysis used interactive model analysis form Miles and Huberman that were the stages of data reduction, data display, and concluding. The results show that YouTube can become authentic material if it has content, plot, duration, and originality that suit the needs and learning objectives. Videos on YouTube also have quality and clear sound. However, YouTube is not only assumed to be the main authentic teaching material, but it will be better if it is equipped with journals, websites and books. The results of this study have implications that teachers must know the needs of students regarding teaching materials and videos that are made should be by the level of learning needs
The Variation of Rural Humanity Communication

With the social change and economic development, the traditional rural social structure has been unable to adapt to the present rural social environment and the disintegration, as the basis of the villagers daily communication, the rural human relations also quietly changed. Based on the investigation of S village in Guangxi, this paper analyzes the local presentation and formation mechanism of the rural human relationship variation, in order to guide the rural out of the dilemma of the human relationship variation.
Learning Path Constraints for UAV Autonomous Navigation Under Uncertain GNSS Availability

This paper addresses a safe path planning problem for UAV urban navigation, under uncertain GNSS availability. The problem can be modeled as a POMDP and solved with sampling-based algorithms. However, such a complex domain suffers from high computational cost and achieves poor results under real-time constraints. Recent research seeks to integrate offline learning in order to efficiently guide online planning. Inspired by the state-of-the-art CAMP (Context-specific Abstract Markov decision Process) formalization, this paper proposes an offline process which learns the path constraint to impose during online POMDP solving in order to reduce the policy search space. More precisely, the offline learnt constraint selector returns the best path constraint according to the GNSS availability probability in the environment. Conclusions of experiments, carried out for three environments, show that using the proposed approach allows to improve the quality of a solution reached by an online planner, within a fixed decision-making timeframe, particularly when GNSS availability probability is low.