Reviewerly/exHarmony huggingface dataset:
The Effectiveness of Online Learning in Elementary School Children

In the times of Pandemic Covid-19, E-learning has become an activities learning model that should be done by all educational institutions in Indonesia, no exception at MI Nurul Huda Ngampelsari Candi Sidoarjo, Which a an activities learning model where an interaction between teacher and student through internet media. The e-learning Implementation requires the readiness of a learning management system, a teacher and student ability to use other applications based internet (digital talent), and student learning independence. Therefore, the aim research are; first, to descriptive a Implementation e-learning policy at MI Nurul Huda, and; Second, analysis effectivity e-learning to students at MI Nurul Huda. This Research uses descriptive qualitative approach with Miles - Hubermen Techniques Analysis, and Gibson Effectiveness Analysis Approach. Result this research are; First, The provisions for e-learning at MI Nurul Huda is outlined in Head Madrasah Decision Letter Number: KB/94.B-205/VII/2020 where the implementation to the teacher council by utilizing applications available on social media and utilizing packages by the Ministry of Education and Culture on TVRI. Second, Gibson Effectiveness Analysis show that e-learning system at MI Nurul Huda is not Effective. This is obtained from the low achievement of aim learning around 40%, and the low fulfillment in an effective learning system, around 36,6%. The percentage achievements are measured by the fulfillment of several components in the standardization of effective learning by theachers.
The Influence of Digital Comics in Teaching Grammar for EFL Students during the COVID-19 Pandemic

This research aims to identify the influence of digital comic media on EFL students' grammatical knowledge during the COVID-19 pandemic. The approach used in this research is quantitative which data collection techniques used pre-test, treatment, and post-test. This research used 21 students of class VIII-C as research subjects at MTs Maarif NU Ngaban academic year 2021-2022. The results of this study showed mean score on student learning outcomes increased by 61,4%. Moreover, the results of hypothesis testing show the value of Sig. (2-tailed) was 0,000 &lt; 0,005, so H0 is rejected. This means that the results of the research show that there is a significant impact on the use of digital comics as a medium for teaching grammar at MTs Maarif NU Ngaban academic year 2021-2022.
Secrets of Linear Feedback Shift Registers

SummaryA Linear Feedback Shift Register (LFSR) is a device that can generate a long, seemingly random, sequence of ones and zeroes. This is important in cryptography. We consider the sometimes unexpected periodic properties of LFSRs, how to understand them using linear algebra, and how to relate them to finite fields, another important topic in cryptography. Along the way, we resolve the puzzle of what it means for a polynomial to be primitive.
A Research on Generative Mechanism Of Synthetic Compounds: From a Perspective of Stylistic-Register Grammar (in Chinese)

Both the structural characteristics and grammaticality issues of synthetic compounds OVN and VON have been fully discussed from perspectives of formal syntax, prosodic grammar and multiple interactions among syntax, prosody and semantics. However, there is still a lot of controversy. This paper puts forward a new analysis based on existing studies and aims to provide a more consistent description and explanation on generative mechanism and constraints of all types of synthetic compounds. First, this paper systematically describes the complex and diverse combination types of synthetic compounds as well as their grammaticality and usability, and summarizes the main aspects and problems to be solved in terms of generative constraints of these compounds, which pave the way to propose a new approach from Stylistic-Register Grammar. With Spatio-Temporal Principle and operational rules of Stylistic-Register Grammar, this paper analyzes the specific constraints on the grammaticality and usability of different types of synthetic compounds, and explains the motivations of special types and cases. Therefore, this paper suggests that the so-called exceptions, special examples or counter-examples are essentially the results of multiple interactions, while the differences of typological features of synthetic compounds are the results of interactions between structural characteristics and specific register motivations, which provide a good reference for the establishment of stylistic-register typology. Lastly, this paper points out that the approach focusing on multi-interactive mechanism could inspire us to make new thinking on the methodologies in related research fields.
Using copula information in wavelet estimation of bivariate density function based on censorship observations

This article discusses the nonparametric estimation of a bivariate density function using copula information under right censoring. We propose an adaptive estimator based on wavelet methods and the formulae for the asymptotic mean integrated squared error(MISE) is used to get the near optimal rate on a large functional class of regular densities. In particular, the asymptotic formulae for MISE in the context of kernel density estimators is derived in the case of censoring. Finally, the consistency of the proposed estimators is established and its effectiveness is validated through a numerical simulations.
Texture Image Analysis Based on Joint of Multi Directions GLCM and Local Ternary Patterns

Human visual brain use three main component such as color, texture and shape to detect or identify environment and objects. Hence, texture analysis has been paid much attention by scientific researchers in last two decades. Texture features can be used in many different applications in commuter vision or machine learning problems. Since now, many different approaches have been proposed to classify textures. Most of them consider the classification accuracy as the main challenge that should be improved. In this article, a new approach is proposed based on combination of two efficient texture descriptor, co-occurrence matrix and local ternary patterns (LTP). First of all, basic local binary pattern and LTP are performed to extract local textural information. Next, a subset of statistical features is extracted from gray-level co-occurrence matrixes. Finally, concatenated features are used to train classifiers. The performance is evaluated on Brodatz benchmark dataset in terms of accuracy. Experimental results show that proposed approach provide higher classification rate in comparison with some state-of-the-art approaches.
Tolerant Self-Distillation for Deep Models

In this paper, we propose a self-knowledge distillation approach named Tolerant Self-Distillation (TSD) for alleviating the overfitting issue suffered from the deep neural networks. TSD introduces an online updating memory and selectively stores the class predictions of the samples from the previous iterations, making it possible to distill knowledge across iterations. Specifically, the class predictions stored in the memory bank serve as the soft labels for supervising the samples from the same class for the current iteration in a reverse way, i.e., the correct-predicted samples are supervised with the incorrect predictions while the incorrect-predicted samples are supervised with the correct predictions. Extensive experimental results on six image classification benchmarks, including both small-scale and large-scale datasets, demonstrate the superiority of the proposed TSD.
Image Embedding and User Multi-Preference Modeling for Data Collection Sampling

HighlightsExtension of perceived information metric based samplingto image collection via appropriate image embedding.Large scale image collection sampling scheme driven bythe proposed user multi preference model.Extensive visual evaluation with t-SNE and KNN withreal large scale dataset.This work proposes an end-to-end user-centric sampling method aimed at selecting the images able to maximized the image perceivedby a given user. As main contributions, we first introduce a novel metrics that assess the amount of perceived informationretained by the user when experiencing a set of images. Given the actual information present in a set of images, which is the volumespanned by the set in the corresponding latent space, we show how to take into account user’s preferences in such a volume calculationto build a user-centric metric for the perceived information. Finally, we propose a sampling strategy seeking the minimumset of images that maximize the information perceived by a given user. Experiments show the ability of the proposed approach toaccurately integrate user’s preference while keeping a reasonable diversity in the sampled image set.
Usability of Immersive Technology for Education and Training of Firefighters in Brazil

Practice-based training in realistic environments is important in preparing a firefighter. Live infrastructure simulation training with real fire and smoke, using real buildings and equipment, has been the only practice-based training format accepted and available, but sometimes with limitations. The integration of virtual re-ality and artificial intelligence can provide interaction for different fields of application and possibilities for complementary training. However, there is hesitation, often in doubt as to the real, often in fire situations. This article investigates how immersive VR supports cognitive processes and affects fidelity in firefighter training. The data were collected from 91 participating firefighters, including instructor officers and firefight-ers from Paraná and other states, participated in various scenarios, and real and virtual firefighting simula-tions. With tactile feedback, students used PPE for daily use at work and other equipment such as hoses and nozzles. In this bias, the usability of the immersive technology was compared with the real fire simulation performed in training containers. The results showed that tacit and explicit knowledge compared through VR can be shared correctly within the Fire Department facilities.
Balanced-partitioning treemapping method for digital hierarchical dataset

The problem of visualizing a hierarchical dataset is an important and useful technical in many real happened situations. Folder system, stock market, and other hierarchical related dataset can use this technical for better understanding the structure, dynamic variation of the dataset. Traditional space-filling(square) based methods have advantages of compact space usage, node size showing compared to diagram based methods. While space-filling based methods have two main research directions—static and dynamic performance. We present a treemapping method based on balanced partitioning that enables in one variant very good aspect ratios, in another good temporal coherence for dynamic data and in the third a good compromise between these two aspects. To layout a treemap, we divide all children of a node into two groups. These groups are further divided until we reach groups of single elements. Then these groups are combined to form the rectangle representing the parent node. This process is performed for each layer of a given hierarchical dataset. In one variant of our partitioning we sort child elements first and built two as equal as possible sized groups from big and small elements(size-balanced partition), which achieves good aspect ratios for the rectangles, but less good temporal coherence(dynamic). The second variant takes the sequence of children and creates the as equal as possible groups with-out sorting(sequence-based, good compromise between aspect ratio and temporal coherency). The third variant splits the children sets always into two groups of equal cardinality regardless of their size(number-balanced, worse aspect ratios but good temporal coherence). We evaluate aspect ratios and dynamic stability of our methods and propose a new metric that measures the visual difference between rectangles during their movement for representing temporally changing inputs. We demonstrate that our treemapping via balanced partitioning out performs state-of-the-art methods for a number of real-world datasets.
Real-time multi-objective solar-thermal power dispatch using different photovoltaic materials

The present universal energy framework and expenditure rate are considerably more startling because of the massive increase in electrical power demand. Comprehensive generation through conventional fossil fuels is one of the fundamental causes and contributors to several environmental problems. Therefore, there has arisen a necessity for ecologically sound and sustainable alternative energy sources. Renewable energy resources (RER) may be extremely functional for environmental and economic goals. Solar energy is the most auspicious of RER and this industry has flourished enormously during the last decade. Photovoltaic cells are spreading in popularity because they are sustainable, clean, and efficient modes of power generation. Researchers are endeavoring meticulously to select superior photovoltaic materials. In this paper, a real-time multi-objective solar-thermal power dispatch problem is framed and optimized for two different photovoltaic materials by using the novel α- constrained simplex method (ACSM). The presented problem is developed to satisfy cost and environmental objectives for Tirumala in the Andhra Pradesh state of India, for two test systems. In the first test system, polycrystalline silicon is used as the photovoltaic material, whereas Copper Indium Gallium Selenide is employed in the second test system. The results of both test systems are compared with each other to procure the finer material. Also, the outturns attained by using ACSM are collated with particle swarm optimization (PSO) and the evolutionary method (EM). The results reveal the precedence of ACSM over PSO and EM.
Analysis and Application of an Evaluation Index System for Effectiveness of Senior High School English Teachers’ Classroom Questioning

Classroom questioning; Effectiveness; Evaluation index system
Equilibrium fractionation of S, Fe, and Ni isotopes in Fe-Ni sulfides: A first-principles investigation

Fe, Ni, and S isotopes are important chemical tracers, and they complement each other to improve our understanding of the Earth's evolution. As common accessory minerals in the Earth's interior and the most important group of ore minerals, sulfide minerals display a wide range of Fe and Ni contents. Using first-principles methods, we investigated the equilibrium fractionation factors of Fe, Ni, and S isotopes of the β-(Ni, Fe)S, FeS2-NiS2, and Fe3S4-Ni3S4 solid solutions, to constrain the effect of cation contents on Fe, Ni, and S isotopic signatures. For the β-(Ni, Fe)S solid solution, the iron β-factors, nickel β-factors, and sulfur β-factors all decrease approximately linearly with the increasing Fe content. For the FeS2-NiS2 solid solution, the iron β-factors, nickel β-factors, and sulfur β-factors show a positive linear relationship with the Fe content. For the Fe3S4-Ni3S4 solid solution, the iron β-factors and nickel β-factors have a strong negative correlation with the Fe content, while the sulfur β-factors have a weak negative correlation with the Fe content. In general, the trend of variation of the β-factors with Fe content is the opposite to that of the average bond lengths with the Fe content. Our results provide an improved understanding of Fe, Ni, and S isotopic fractionation in natural systems that may be used to better constrain physical, chemical, and geo-biological evolution on Earth.
Attack-filtered interactive arabic CAPTCHAs

A CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) is widely used challenge–response test intended to distinguish between a human user and a malicious bot. This paper deals with text-based interactive CAPTCHA generation. Solving a CAPTCHA is interactive in the sense that the user is asked to solve a challenge from a pool of challenges. The novelty of the proposed method is that we introduce the concept of attack-filtering as a pre-release step for CAPTCHAs. In other words, the distortion level and challenge type for a particular CAPTCHA image are selected in a way that enables the presented CAPTCHA to withstand certain attacks. In addition, attack-filtering enables us to select an appropriate distortion level, present challenges in the proper order, and apply security thresholds. We implemented the presented model in a novel Arabic handwritten text-based interactive CAPTCHA scheme. The experimental results show that, with an interactive challenge pool, the proposed scheme can attain high usability even after filtering the CAPTCHA sample with pre-set bot attacks.
The Effect of “Directness” of the Distance Metric to Produce Compact and Accurate Associative Classification Models

Machine learning techniques are ever prevalent as datasets continue to grow daily. Associative classification (AC), which combines classification and association rule mining algorithms, plays an important role in understanding big datasets that generate a large number of rules. Clustering, on the other hand, can contribute by reducing the rule space to produce compact models. The above-mentioned facts were the main motivation for this research work. We propose a new distance (similarity) metric based on “direct” and “indirect” measures and explain the overall importance of this method, which can produce compact and accurate models. Specifically, we aim to employ agglomerative hierarchical clustering to develop new associative classification models that contain a lower number of rules. Furthermore, a new strategy (based on the cluster center) is presented to extract the representative rule for each cluster. Twelve real-world datasets were evaluated experimentally for accuracy and compactness, and the results were compared to those of previously established associative classifiers. The results show that our method outperformed the other algorithms in terms of classifier size on most of the datasets, while still being as accurate in classification.
Implementation of Integrated System Related to Website-Based Employee Data Management

The purpose of this study is to implement the restful method of the subsystem into a website-based integrated system for managing employee data and to update the employee data management system in an integrated manner. Data collection methods used are observation, interviews, and library studies. The method used is the Rest method, better known as RESTful, in principle, a request for data to a RESTful web service is actually a reference or basic request in the delivery of resources through the media website. The results of the study prove that the resource itself is interpreted as anything that can be stored on internal digital storage data and displayed as an array of bits in the form of a softcopy of data or a blueprint of data, where the goal is to create a system that has good performance, is fast and easy to use. developed (scale) especially in data exchange and communication. The conclusion of this study is that the results of the implementation of an integrated system of data on periodic salary increases, promotions and employee leave are the solution to the problems that exist at the PSDKU State Polytechnic of Creative Media Makassar
Analysis of Teaching Strategies in Reading Comprehension to Seventh Grade Students in Junior High School

Reading comprehension is a person's ability to know and understand the meaning contained in a reading text. Teaching strategy is a teacher’s scheme in teaching and learning process to achieve a aim which have plan. The study aimed to explore teaching strategies and implementation in reading comprehension in seventh grade students at SMPN 2 Jabon Sidoarjo. This research method is qualitative research. The subject of this research is an English teacher at SMPN 2 Jabon Sidoarjo. The research instrument is a checklist of observations and interviews. The results of the study found that there were three strategies used by the teacher, Text-Based Instruction, Finding the meaning (scaffolding) and exercise (online learning). With Text-Based Instruction the teacher provides several steps to improve students' reading comprehension skills, Finding the meaning (scaffolding) strategies to help students develop ideas that can be read by students, and exercises (online learning) to hone students' skills while studying at home.
Literary Study of the Influence of Monopoly Game Media in Increasing PKN Learning Results in Elementary Schools

This research is a literature study that analyzes the results of various types of previous studies that have relevant titles. This type of research is a literature review and analyzes research that uses quantitative research and classroom action research. This study aims to analyze the use of monopoly game media in improving Civics learning outcomes in elementary schools. The data collection technique in this literature study research uses the documentation method. The source of data in this study is secondary data obtained from the results of research that has been carried out by previous researchers. This shows that the use of monopoly game media helps students in improving student learning outcomes in Civics material. In addition, monopoly game media can help student learning and make it easier for educators to deliver subject matter.
Legal Management of Network Information Security Based on Embedded Real-Time Task Processing

Nowadays, with the continuous advancement of globalization, Internet technology has made an unprecedented leap, which makes the amount of data on the Internet grow exponentially. Data are not only diverse and numerous, but also spread fast and of high value. Therefore, the Internet is entering the era of big data. However, the openness of information network itself, the immaturity of technology, and the imperfection of network security law make it vulnerable to network attacks, resulting in the disclosure of network users' information and the theft of their privacy data. The formulation of network information security law to protect network information security is of great significance for the development of multidomain network information security under the background of big data and ensuring users' online security. Firstly, this article introduces the basic information of embedded real-time task system, analyzes the factors of real-time Linux based on kernel preemption, interrupts mechanism and policy planning, and summarizes the process of some embedded real-time task processing. Integrating legal management, this article puts forward three elements of network security control mechanism, namely, network control personnel, environment, and technology. Taking these three elements as the starting point of the construction of information security control mechanism, this article provides the steps of using evaluation model to evaluate network information security. Finally, it identifies the problems existing in the legal management of network information security and formulates the corresponding strategies. By combining embedded technology and real-time task processing technology, this study applies it to the field of network information security legal management, to promote its development.