Reviewerly/exHarmony huggingface dataset:
Analysis of learning behavior characteristics based on process evaluation data

Strengthening process evaluation is an important topic in the current educational evaluation reform. Applying process evaluation to the current popular blended teaching will help promote the process of educational evaluation reform. In this paper, a process evaluation index system for blended teaching is constructed based on the Delphi method and the AHP. The evaluation data are collected in the actual course, and the K-means clustering algorithm is used to mine and analyze the characteristics of students&apos; learning behavior under the blended teaching mode. It is found that under the process evaluation model, students can be divided into five types of learners: independent research type, confident and flexible type, digital ability type, cooperative and mutual aid type, and learning difficulty type. The main learning behaviors are learning engagement, classroom performance, online learning, assignment submission, unit assessment and problem solving. The polarization is severe, and the comprehensive ability of high-level skills is poor. Accordingly, the author puts suggestions to provide a practical reference and theoretical basis for front-line teachers to carry out the process evaluation of blended teaching.
Globally Explainable AutoML Evolved Models of Corporate Credit Risk

Corporate credit ratings are one of the most relevant financial indicators in credit risk analysis. These are generated by different rating agencies which base their methodologies on the use of various financial variables of each company and the experience of their analysts; their impact on the market is of such importance that their mismanagement can trigger major financial crises such as the one that occurred in 2008. Along with this, the great importance of internal models for calculating credit risk and international regulations and agreements seek to have a level of explainability of the methods used to perform this risk management. This paper proposes the use of Automatic Machine Learning (AutoML) as a tool for the generation of a machine learning model that performs a prediction of corporate credit ratings using data from balance sheets, financial statements, and descriptive information about the company. The main contribution of this work lies in the development of the level of interpretability of each model as a second goal to be optimized with a global measure of explainability, allowing the generation of models that can better explain their results.
Building the Basic Blog System

After completing this chapter, you will be able to create a basic blog using Model-View-Controller (MVC). The basic blog accepts and stores blog entries and displays them to the user.
The extension of Moore-Penrose generalized inverse for extreme learning machine in forecasting USD/IDR exchange rate as impact of COVID-19

Since the beginning of the COVID-19 pandemic, the Indonesian economy has undergone changes which have caused the Rupiah exchange rate against the US Dollar to increase and weaken. Throughout March to September 2020, the Rupiah exchange rate depreciated by 2.75% - 4.57% with a range of Rp. 14,000 – Rp. 16,575 per US Dollar. Depreciation has a negative impact on the Indonesian economy because it makes product prices relatively cheaper for other countries. Therefore, it is necessary to do a forecast to determine the exchange rate of the Rupiah against the US Dollar in the future. Forecasting is an activity carried out using existing data to predict something in the future. In forecasting USD/IDR Exchange Rate using the Extreme Learning Machine method. This method does not require parametric assumptions and has a faster learning speed feedforward by determining the weights and biases on the network randomly. The ELM formulation leads to solving a system of linear equations in terms of unknown weights connecting the hidden layer to the output layer. The solution of this general system of linear equations is obtained using the Moore-Penrose Pseudo Inverse. The results of data analysis resulted in the optimum ELM network architecture (17-49-1), namely 17 neurons in the input layer, 49 neurons in the hidden layer, and 1 neuron in the output layer. The network is obtained from MSE which produces the smallest error value, which is 0.000338 on training data and 0.000139 on testing data. With this network, the results of forecasting USD/IDR Exchange Rate with MAPE are 0.2383951%. Forecasting results show that the Rupiah exchange rate has appreciation and is quite stable as expected by the government in strengthening the Indonesian economy during the COVID-19 pandemic.
The Effectiveness of Macromedia Flash Digital Media in Improving Students’ Mathematics Reasoning

This study aims to describe the effectiveness of students' mathematical reasoning abilities through Macromedia flash digital learning media. Learning media is an educational product that can bridge students' interest in learning mathematics so that with this interest, they can increase their mathematical thinking skills at a higher level. The population of this study was students of SMPN Rokun Hulu. The research sample was one of the junior high schools in Rokan Hulu Regency, which was selected purposively according to the problems found in the field, namely SMPN 1 Ujungbatu. The research instrument is an essay test. Research data analysis using quantitative descriptive data analysis, correlational, and paired t-test. The results showed that there was a significant difference between ordinary learning used by teachers and learning using Macromedia flash digital media. These results can be seen from the results of the descriptive test and paired t-test with a significance of 0.00. These results indicate that Macromedia flash-based digital learning media effectively improves the reasoning of SMPN 1 Ujungbatu students
Collaborative Representation Based Graph Embedding Discriminant Analysis for Face Recognition

Considering the collaborative representation projection constructs the adjacent graph based on the collaborative representation relationship of samples and many samples from different classes may be gathered together after projection, a discriminative competitive and collaborative representation projection is proposed. Firstly, each sample is competitively and collaboratively represented by all samples in the dataset to calculate the similarity of samples. Then, an intraclass graph is constructed to characterize the intraclass compactness, and an interclass graph is built to characterize the interclass separability. On this basis, the label propagation algorithm is applied to calculate the soft label information of unlabeled samples to eliminate the influence of unlabeled samples on the recognition results. Additionally, the nonlinear mapping is used to replace the linear inner in the graph embedding framework to solve the linearly inseparable problem of the original samples in the low-dimensional space. Experimental results on ORL, AR, FERET and Yale B face datasets show that compared to the CRP method, the proposed method improves the maximum recognition rate by 1%~4% and improves 2%~6% in noise and blur results, respectively.
Research on High-Quality Data Screening Algorithm for Sign Language

This paper introduces the collection process of a Chinese sign language dataset for practical applications, and makes a statistical analysis of word frequency, sentence frequency and other data information. For this dataset, and for the subsequent larger-scale sign language data collection, an artificial intelligence application-oriented sign language video high-quality data screening algorithm is proposed to realize an intelligent solution for machine screening of sign language video quality through the process of noise filtering, multi-feature extracting, and screening framework designing. It is experimentally verified that the solution can completely screen out misplaced videos, but it is consistent with the sensitivity of human eyes to observing hand changes, it is not easy to distinguish subtle omissions, wrong sign and redundant sign, and the order exchange. This scheme can basically meet the screening requirements in the subsequent large-scale sign language video recording process, which can increase the robustness of the data set and improve the data quality while saving labor costs, thus providing a high-quality data base for establishing an accurate sign language recognition and translation model.
Clone Detection Method Selection Based on Bandit Algorithm: a Preliminary Analysis

Various clone detection methods have been proposed in the past, with results varying depending on the combination of the methods and parameters setting used (i.e., configurations). To help with the selection of a suitable clone detection configuration, we propose Bandit Algorithm (BA) approach that can help in evaluating the configuration used dynamically while using detection methods. Our preliminary analysis showed that our approach is able to identify the best configurations from four used code clone detection methods.
Mineral Chemistry and Crystal Size Distributions of Mafic Dikes and Sills on the Eastern Border of the Parnaíba Basin, Ne Brazil

Crystal size distributions (CSDs) and mineral chemistry of plagioclase and clinopyroxene are used to to quantify textures and estimate timescales of Mesozoic mafic sills and dikes exposed on the eastern margin of the Parnaíba Basin (NE Brazil). The diabases consist of plagioclase, clinopyroxene (augite and pigeonite) and Fe–Ti oxides and show textures that vary from holocrystalline, to locally porphyritic, glomerophyric and hypocrystalline. Plagioclase and clinopyroxene CSDs are similar in sills and dikes, with clinopyroxene slopes systematically steeper than plagioclase slopes. Plagioclase usually records nearly log-linear negative slopes that tend to steepen approaching the wall rock contact. Plagioclase typically shows normal zoning with anorthite contents varying (An84-25) from the center to the grain margin. Calculated residence times for the log-linear segments vary from ca. one month (τ = 10-7 mm.s-1) for samples situated 20 cm from the contact to ca. four months for samples ~3 m away from the contact. Clinopyroxene consists of augite (dominant) and pigeonite and shows relatively uniform compositions. Unlike plagioclase, clinopyroxene CSDs tend to be flat or slightly positive towards the finer grains suggesting that the texture was modified during the crystallization of the residual melt. Plagioclase microlites (L < 0.5 mm), in contrast, show kinked CSDs with the steeper negative slopes including minute crystals of sanidine (orthoclase component) and alkali-sodic feldspar in addition to Ca-Na plagioclase. The modified textures and high nucleation density of microlites in mostly static, deep-seated magmatic reservoirs therefore resulted from chemical disequilibrium between largely solidified tholeiitic magma and late, highly fractionated alkali-rich residual melts. The estimated time for the full crystallization of the largest sills is one and one-half years.
Pengembangan E-Modul Sistem Peredaran Darah pada Manusia Berbasis Multiple Intelligence untuk Kelas IX SMP

This study aims to produce a product or e-module based on multiple intelligence using the 4-D model in the circulatory system material that is suitable for use in the learning process. This research was developed with a 4-D model consisting of definitions, planning, development and dissemination. Data collection using a questionnaire. Products that have been developed have been validated by material experts and validated by media experts.The results of the evaluation of the material expert validator were content quality getting 3.83, learning quality getting a score of 4.5, interaction quality getting a score of 3.67 and display quality getting 4 and media experts, namely the simplicity aspect getting a score of 4, the integration aspect getting a score of 4, 67, the learning interaction aspect gets a score of 4, 83, the balance aspect gets a score of 4, 2, the form aspect gets a score of 4, the color aspect gets a score of 5 and the language aspect gets a score of 4 and the average score is 4.38. From the results of the validation of media experts and material experts, it is stated that the e-module is suitable for use. Based on the results of the research, it can be said that students and teachers are expected to really take advantage of teaching materials in order to create interesting and fun learning in biology learning.
A Smart Ecosystem to improve Patient Monitoring using Wearables, Intelligent Agents, Complex Event Processing and Image Processing

Our work describes a smart-ecosystem able to mon-itor patients' health condition, even at home or at work, by ex-ploiting a creative blend of Medical Wearables, Intelligent Agents, Complex Event Processing and Image Processing. With the help of a smart application, that links together the Wearables and the power of Artificial Intelligence, patients will be continuously and actively supervised during their daily activities. This can even save their lives, in case sudden or gradual issues should occur. Using our system, patients with non-severe though potentially unstable chronic diseases will no longer overburden first aid services. This is also useful for containing the spread of COVID-19. Specifically, in this paper we focus on automated vitals monitoring, electrocardiogram (ECG) analysis, and Psoriasis detection.
PROSOCIAL MEDIA RESOURCE: PROJECT-EDUCATIONAL INTENSIVE OF STUDENTS

Relevance. The relevance of the topic comes from its various aspects and manifestations of behavior aimed at helping other people and society, including using the digital environment. The article discusses the technology of students’ project activities for the creation of prosocial media. The experience of prosocial design on the platform of the University 20.35 is presented.&#x0D; The purpose of the study. Based on the study of the attitude of young people to media resources promoting assistance to other people and society, to develop and implement educational intensive technology to create by students prosocial media.&#x0D; Methods and methodology. The methodological basis of the study was the approaches of media education. Methods: data selection and systematization, document analysis, Internet media monitoring, quantitative analysis of publications in youth Internet media, design. Empirical data are comprehended and processed using content analysis, quantitative analysis methods (descriptive statistics, percentage analysis. The calculations were made on the basis of the SPPS 22 statistical software package.&#x0D; Results. The preferences of young people in the choice of forms of presentation of prosocial topics in the media space are revealed, their reactions to prosocial posts, blogs, videos, publications are studied, the structure of the projected prosocial media environment is determined. An educational intensive technology has been implemented to create a test prototype for the prosocial media “Let’s make the world a better place!”.&#x0D; Conclusion. Based on the results of the study of the attitude of young people to prosocial media, a technology for teaching the design of prosocial media was developed and implemented, which allowed students to form attitudes to prosocial activity.&#x0D; The scope of the results. The results of the study should be taken into account when developing programs and projects aimed at ensuring the digital socialization of young people.
A Study of the Legal Construction of Data Property Rights

With the rapid changes in technology and the rapid development of the Internet, data as important information materials have unique value attributes in some fields and create social wealth that cannot be ignored.As a new type of property right object, it can not only be protected as a civil right, but also have enough theories to support the development of data property.Therefore, it is important to consider data as property rights and construct the relevant legal system.
Synchronous Federated Learning Latency Optimization Based on Model Splitting

Federated Learning (FL) is a distributed machine learning approach which is suitable for edge computing environment. While in this environment, how to take full advantage of the computing resources on end devices and edge servers is still a difficult problem. Especially for the synchronous federated learning, computing resources among different participants will lead to extra time cost and cause resource waste. In this paper, we try to reduce the time cost and the computing resource waste by using model splitting and task scheduling. We first establish the mathematical model and find it can not be solved directly. Then we design our algorithm which we name as the Federated Learning Offloading Acceleration (FLOA) algorithm to obtain a sub-optimal solution. The FLOA algorithm first uses the Partition Points Selection method to reduce the size of the solution space, then proposes a task offloading method based on matching theory. Experiments and simulations show that compared to the other three calculation methods, the single iteration time is reduced by $$47\%$$ , $$28\%$$ , $$14\%$$ under our algorithm in turn.
FRDVDnet: Towards Fast and Robust Deep Video Denoising

With the rapid development of the technology of deep learning, lots of deep video denoising networks emerged. The mainstream deep video denoising(DVD) algorithms with the property of excellent detail preservation and the ability to process a wide range of noise level, such as FastDVDnet, are only based on the fixed datasets without enough generalizability. In order to deal with this problem, a network for fast and robust DVD called FRDVDnet, is proposed in this paper for optimizing FastDVDnet by utilizing dense-scale feature fusion mechanism and CReLU activation scheme. We compare FRDVDnet with FastDVDnet tested in the mid-scale bayer benchmark dataset visually and quantitatively. The simulation result shows that the denoising performance of FRDVDnet is improved with the increasing of the noise intensity compared to FastDVDnet. Moreover, in terms of perservation of key detail, FRDVDnet is superior to FastDVDnet on the denoised video.
Analysis of large image sets for automated monitoring tasks

A technique for automating the analysis of large sets of images for monitoring and remote sensing of the Earth has been developed. It is based on the fact that each observed object is defined as a dynamic object with specified characteristics. Objects whose characteristics are similar or fall within the specified ranges are combined into dynamic groups and monitoring is performed for the selected group.
Pengembangan Buku Ajar Berbasis Hasil Penelitian Profil DNA Forensik (Fingerprinting) pada Matakuliah Bioteknologi

The purpose of developing this book is to produce textbooks on biotechnology subjects, especially in the field of medicine (forensic) as a complementary (supporting) book. The method used refers to the Hannafin Peck development model which includes three stages starting from the needs analysis, the design stage (designing textbook products), and the development stage through making textbooks and validating material and media experts. The instruments used were questionnaires and interview technique. The results were analyzed descriptively qualitatively. The results showed that both material and media experts scored very valid categories, so it could be concluded that the textbooks developed were very suitable for use in learning activities.
Development of a wearable lower limb force feedback device that can move freely in VR space

In recent years, force feedback devices have been developed to improve the reality of VR experiences. However, most of the general force feedback devices target the upper limbs. By realizing force feedback to the lower limbs, it will be possible to realize an experience in VR space that cannot be achieved by force feedback to the upper limbs alone. Therefore, the authors aim to develop a lower limb force feedback device. In this study, we developed a lower limb force feedback device based on a physical model at soccer kicking. The output torque of the developed device was confirmed that met the target torque by the experiment.
Extracting Weighted Finite Automata from Recurrent Neural Networks for Natural Languages

Recurrent Neural Networks (RNNs) have achieved tremendous success in sequential data processing. However, it is quite challenging to interpret and verify RNNs’ behaviors directly. To this end, many efforts have been made to extract finite automata from RNNs. Existing approaches such as exact learning are effective in extracting finite-state models to characterize the state dynamics of RNNs for formal languages, but are limited in the scalability to process natural languages. Compositional approaches that are scablable to natural languages fall short in extraction precision. In this paper, we identify the transition sparsity problem that heavily impacts the extraction precision. To address this problem, we propose a transition rule extraction approach, which is scalable to natural language processing models and effective in improving extraction precision. Specifically, we propose an empirical method to complement the missing rules in the transition diagram. In addition, we further adjust the transition matrices to enhance the context-aware ability of the extracted weighted finite automaton (WFA). Finally, we propose two data augmentation tactics to track more dynamic behaviors of the target RNN. Experiments on two popular natural language datasets show that our method can extract WFA from RNN for natural language processing with better precision than existing approaches. Our code is available at https://github.com/weizeming/Extract_WFA_from_RNN_for_NL .