Reviewerly/exHarmony huggingface dataset:
Library book intelligent recommendation system based on artificial intelligence

In order to improve the accuracy of intelligent recommendation of library books, an intelligent recommendation system of library books based on artificial intelligence was designed. The system uses artificial intelligence technology to clean up and normalize the data, automatically extracts the user’s historical evaluation data of books, divides the whole user space into several similar user clusters through the similar user clustering module, constructs the user book evaluation matrix according to the historical evaluation data, and uses the hybrid collaborative filtering algorithm which integrates user based and project-based to predict each user a book evaluation matrix of similar user clusters was used to realize the intelligent recommendation of library books, and the recommendation results were displayed to users through the user interface module. The results show that the average absolute error and root mean square error of the system are always the lowest, and the recommendation accuracy is high. When the control parameter is 0.4, the best intelligent book recommendation effect can be obtained; the recommended recall rate is not affected by the sparse density of the data set, and the stability is strong.
Classification of Attribute Mastery Patterns Using Deep Learning

It is very important to identify the attribute mastery patterns of the examinee in cognitive diagnosis assessment.There are many methods to classify the attribute mastery patterns and many studies have been done to diagnose what the individuals have mastered and or Montel Carl Computer Simulation is used to study the classification of the attribute mastery patterns by Deep Learning.Four results were found.Firstly, Deep Learning can be used to classify the attribute mastery patterns efficiently.Secondly, the complication of the structures will decrease the accuracy of the classification.The order of the influence is linear, convergent, unstructured and divergent.It means that the divergent is the most complicated, and the accuracy of this structure is the lowest among the four structures.Thirdly, with the increasing rates of the slipping and guessing, the accuracy of the classification decreased in verse, which is the same as the existing research results.At last, the results are influenced by the sample size of the training, and the proper sample size is in need of deeper discussion.
Evolutionary Quantum Transition (EQUATe): A Novel Solution to Optimization Problems

Quantum Computing (QC) has often been presented as an esoteric field of computing research. However, the possible advantages offered by the inherent quantum fundamentals beseeches extensive additional ventures into this field. Likewise, Evolutionary Computing (EC) offers a multi-pronged approach by deploying several candidates into the search space with constraints guiding the search process. In this paper, we present Evolutionary Quantum Transition (EQUATe) to provide a unique solution to solving state transition optimization problems using concepts borrowed from QC and EC. The combination leverages compute power to explore the space of candidate solutions by introducing smarter techniques. The solution so obtained provides the optimal transition solution with an automated circuit design and selection process.
Modeling the influence of external factors on students' memorization performance in general chemistry

The article provides theoretical and empirical justification for the use of multivariate methods of analysis to implement different types of models in the study of the productivity of involuntary and combined memorization by students in learning chemistry.
Research on the Innovation and Application of Cloud Technology in the Construction of Management Laboratory

Abstract With the rapid development of information technology, cloud technology is widely used in various industries. In the field of education and teaching, colleges and universities have invested more in the application of cloud technology in laboratory construction. The application of this technology has greatly improved the management effect of university laboratory, improved the overall quality of teaching and promoted the development of school education. This paper focuses on the typical application of cloud technology in the construction of economic management laboratory, and puts forward some existing problems and prospects for the future of cloud technology in the construction of economic management laboratory, so as to promote the cloud technology in the field of education to achieve better development.
THE IMPORTANCE OF FEATURES FORMING THE SYSTEM OF KNOWLEDGE ABOUT NATURE

Today, increasingly more attention is given to knowledge construction. In this article it is discussed how much nature, its objects and phenomena features are important forming a certain system of knowledge about nature. Research show that features cannot be ignored, one should focus and reconsider how our students are guided towards feature identification. During the first years of life, the child already experiences specific environmental features. Later, the knowledge of features (to remember, cognize, define…) and understanding (to retell, explain...) guide further: teach to compare, group, classify. The analysis aim was to give methodological advice to primary school teachers and to all interested in natural science education on how to teach students to cognize and understand the features of natural objects, phenomena and to develop comparison, grouping and classification abilities. Aims: 1) to discuss what it is a feature; 2) to analyse what the feature’s expression is in preschool and pre-primary education programmes; 3) to present activity episodes, task examples on how to teach during nature cognition lessons to recognize features according to which the procedures of comparison, grouping, and classification are possible. Introducing what the feature is, the synonyms of features are introduced as well (feature, peculiarity, criterion, symptom), it is explained what the feature defines, what the difference is between quantitative and qualitative features. Discussing the feature expression in education programmes, 4 programmes were analysed on this question (preschool, pre-primary school, primary school world cognition programme and standardised world cognition programme). Clarity, accuracy, attention to detail of the discussed question has been noticed in the standardised world cognition programme. The questions of how to teach primary school students to be observant and to find natural object features, and what to do having found them are shared in the third part of the article. Several concrete recommendations are given here on how to teach to recognize some or other animate and inanimate natural object and phenomena features. Together are presented orientation questions, objective commentaries, possible students’ answers because the author has already tried these activities with the primary school students. Keywords: natural science education, classification abilities, comparison abilities, object features, primary education.
Data Cleaning of Irrelevant Images Based on Transfer Learning

Considering that there are many irrelevant images in the image data set crawled from the Internet, this paper proposes a new method of cleaning irrelevant images data based on transfer learning. The method consists of two steps that can be done iteratively. Firstly, we use transfer learning to train and get a better image classifier, which makes the single image's recognition effect more accurate. Besides, according to the number of different types of images in the data set, we calculate and determine the threshold value of the image of the minority class and then clean them accordingly. We find that our method can improve the test accuracy of the original data set on VGG16, VGG19, and Inception v3 from 37.41%, 40.62%, and 49.37% to 50.07%, 53.89%, and 65.16%, respectively. Since transfer learning has a good application effect on irrelevant images cleaning, we can use it to create data sets in deep learning.
Dynamic Scene Deblurring of Multi-Scale Progressive Attention Network

Abstract In order to enhance the attention to the image foreground targets in dynamic scenes and better suppress the generation of image edge artifacts, a multi-scale progressive attention network (MSPA) deblurring algorithm is proposed, MSPA is based on the GAN structure. In the feature extraction stage, MSPA designs multi-scale width perception and pyramid perception residual blocks to help our skeleton network better extract multi-scale local features and reduce the difficulty of network training, and In order to better obtain the semantic information of the image, the channel attention mechanism is used to correct the deep features of the blurred image; in the feature fusion stage, the progressive channel and spatial attention network (PCSA) is designed to enhance the receptive field, and selectively integrate multiple levels semantic information and enhance the non-local connection between features in progressive method. A large number of experiments show that the MSPA algorithm is superior to many advanced algorithms on the GoPro and Kohler datasets.
Keywords of Search Engine Optimization based on Corpus

Abstract With the development of network, keywords have become an important way of retrieval, which is the window for academic papers to enter circulation and citation. Therefore, the selection of keywords is very important to improve the retrieval rate. Based on this, this paper carries out a corpus based search engine optimization keyword research. Based on one million English corpora, this paper analyzes and studies 3933 key words from the definition and function of keywords, the characteristics of keywords, selection methods, arrangement order, composition, quantity and writing format. It also analyzes the algorithm and ranking principle of search engine, discusses the optimization of website structure, keywords and single page, the punishment of search engine and the rescue method after being punished by search engine. According to the statistical research on the frequency of key words, the number of keywords is mainly 1-2 words, accounting for 90% of the total number. It can be seen that the search engine optimization technology used in the research of keyword optimization in the website can improve the website ranking and realize the value of the website.
Multidisciplinary approach to the professional development of a teacher in the educational environment

The article investigates the concept, motivation, and mechanisms of the professional development of a teacher. It outlines principles of the teacher's multi-subject interaction: system-wide and private (creative self-fulfilment; development of subjectivity; multi-logical interaction of subjects of the educational process; integration of subject fields; fluctuation autonomy of subjects). Experimental work to create conditions for optimizing professional development was carried out within the framework of the development and implementation of a program for the strategic development of an educational organization. The process of its joint development and implementation, technological and methodological support has become a mechanism for the integration of educational subjects. The results showed a positive trend in educational outcomes, the transformation of the educational environment, and the professional development of teachers. The authors conclude about the developmental influence of multi-subject interaction on the professionalism of the teacher.
Dominance of Deep LSTM in Smartphone Sensor based Human Activity Classification

Human activity categorization is one of the challenging topics on which several types of researches have been conducted. With the improvement of modern technologies and algorithms, there have been many plausible solutions. However, earlier studies had been limited to a few activities based on indoor situations. Conducting the classification of complex continuous activities was an interest off time. As the world progresses, it needs more accurate data visualization in real-time for medical service purposes. Even this prediction can become crucial for the business aspects. For this purpose, in this research, the number of activities have been increased for classification and is attempted to detect with LSTM, XGBoost, SVM and Neural Network techniques. In this work, a smartphone app was developed that performs activity recognition using the embedded accelerometer, gyroscope sensors and based on the selected features, classify activities, especially with distinct signatures.
Chameleon: Image Style Transfer Based on Image Classification Networks

In recent years, deep neural networks enabled computers to extract features of images; this stimulated interest in image style transfer. Since the first NST algorithm, many schemes are proposed to accelerate the process. Yet, these methods more or less sacrifice the visual quality of the output. In this paper, we analyze two precursive algorithms and generalize the results. We propose an Image Style Transfer method based on Image Classification Networks. With Chameleon, we accelerate the runtime by 11 times and we show that general CNN techniques would improve the stylization process. Our work provides novel insights into the classification capacity of CNNs and demonstrates the potential of NST algorithms.
KEEFEKTIFAN PERKULIAHAN DARING PADA MASA COVID-19 DI PRODI PENDIDIKAN BAHASA INDONESIA UNIVERSITAS MALIKUSSALEH

The purpose of this study was to analyze the effectiveness of online learning at the Indonesian Language Education Study Program, Malikussaleh University. This type of research is a combination research (mixed methods) qualitative and quantitative. The population of this study were all students of the Indonesian Language Education Study Program that follows online learning for the 2020/2021 school year. As for the total number of students in the Pend. Indonesian is 300 people. Because of the large population, in this study a random sample was determined. The results found 130 samples (respondents). The data collection technique of this research was carried out by online survey method using a questionnaire instrument through Google Form. The data analysis technique used is the technique proposed by Miles and Huberman. The results of this study indicate that online learning in the Indonesian Language Education Study Program, Malikussaleh University has not been effective. This is indicated by the acquisition of an average value for each indicator, which is <60%. Students prefer face-to-face learning to be carried out immediately after this pandemic ends.
Design of university archives digital management system based on CBS mode

In order to solve the problem of low retrieval accuracy in traditional university archives digital management system, this paper designs a university archives digital management system based on CBS mode. The hardware of the system is composed of paper file digitization module, user management module, file management module, database module and file retrieval module. In this process, CBS mode is used to design the file management module. On this basis, software development and design are carried out. When the request time interval exceeds a fixed value, the user's resource access application needs to be rejected within a fixed time range. Until the time interval is greater than a fixed value, the application is accepted again to ensure the security of the system. The experimental results show that the retrieval accuracy of the system is always above 98%, the average response time is 0.6s, and the practical application performance is better.
Construction and applied research of Cloud Platform for Financial Shared Service Center

Under the background of big data, the financial information of enterprises is increasing rapidly, and the relationship between financial data is intricate. In order to enhance competitiveness, enterprises should build a big data financial sharing system according to their own conditions to ensure that the financial management of enterprise groups is more standardized, High-efficiency, optimized allocation and scientific use of capital resources, and reduce the financial risk of the enterprise. For example, in a company, there were 7 risks in the financial system in 2013. Since the introduction of the financial shared service center cloud platform in 2015, the number of risks has not been less than 3 in three consecutive years, which has significantly reduced the risks in the financial system. This fully shows that the establishment of the cloud platform of the financial shared service center can bring more economic benefits to enterprises and promote the improvement of enterprise competitiveness.
Research on Intelligent Identification of Pivoting Center and Smooth Processing of Test Data for Flying Flexible Joint

In this paper, a method of intelligent identification and data smooth processing of flying flexible joint pivoting center based on machine vision is proposed. The intelligent identification is realized by the following process: first of all the geometric center of the two markers attached to the flying body is located on a straight line at a certain angle to the center-line of the measured pivoting body, secondly then continuous image sampling is carried out by industrial camera when the marker swings with the pivoting body, and image data is transmitted through a data interface to an industrial computer, Finally the image processing module de-noises the image, removes the background and locates the markers to obtain the plane coordinates of the markers in the coordinate system of the test system. The data smooth of obtained coordinates is carried outby Matlab software including the following steps: the coordinates of the mark points detected based on machine vision are optimized to obtain the smooth curve by fitting of the parabola and arc. Then the coordinates of the points on the curve are used to optimize the coordinates of the marked points from measurement. The optimized coordinate values are substituted into the calculation module of pivoting center, so the average pivoting center of the sampling interval of two images is calculated according to the mathematical model to approach the instantaneous pivoting center during the motion of the pivoting body. The result processing module displays and records the curve of pivoting center shift directly and effectively. Finally, it is validated by simulation and experiments that the precision of pivoting center measured by such measuring system is ~0.5%.
Value aspect in forming the foundations of information culture in preschoolers

The purpose of the research described in the article is to consider the values and imperatives that underlie the formation of the information culture of preschool children. The research methods were the study and analysis of psychological, pedagogical and philosophical literature, forecasting and reflection. Based on the results of the study, the concept of antifragile education was proposed, its methodological basis, principles and rules were determined. The practical significance of the results obtained lies in determining the components of the formation of the value basis of the preschool information culture preconditions: cognitive (the process of cognition of adequate actions in the digital space in the process of self-realization; the ability to select criteria for ranking information; awareness in the field of digital space threats that contradict imperatives and impede self-realization), behavioral with sufficient emotional and volitional reinforcement (to plan and perform a conscious system of actions), motivational and value (a conscious system of actions for self-realization in accordance with imperatives). The study revealed that the issue of personal self-identification in the redundant digital space has not been sufficiently studied. It is necessary to introduce the principles of antifragile education and continue the awareness and development of the components of the formation of the value basis of the preconditions of the information culture of preschoolers. The concept of antifragile education can form the basis of the work of teachers, starting from the level of preschool education, and the spiritual and moral values that form the basis of the concept can be very important for parents of preschoolers. For the first time, a concept is proposed that is designed to form and maintain imperatives in children, allowing for self-identification and spiritual and moral self-realization in the context of the formation of information culture of preschoolers. As the conclusions, the use of digital technologies in education was noted, and at the same time the issue of introducing the principles of antifragile education for the spiritual and creative self-realization of the individual in the new global international digital space of human life is being actualized.
SONAS: A System to Obtain Insights on Web APIs from Stack Overflow

In recent years, we have witnessed the rapid development of types and quantities of Web APIs. However, it is challenging for users to select Web APIs that best match their requirements and to learn how to invoke a Web API correctly. Although Web API providers often publish documents that describe the functionalities of Web APIs and how to use them, users still have to collect information to acquire knowledge about the usage information of Web APIs. Stack Overflow, the largest programming-related question-and-answer (Q&A) website, has many posts about Web APIs. Therefore, we have designed and implemented a System to Obtain iNsights on Web APIs from Stack Overflow (SONAS). SONAS collects questions related to Web APIs and classifies them into different categories using a deep learning model. The statistics on the numbers of different types of questions indicate the usage information of Web APIs. Furthermore, SONAS predicts the future usage trends of Web APIs, based on a long short-term memory model with multi-task learning. The experiments on a real-world dataset prove SONAS can provide useful insights on Web APIs.
A spatio-temporal exposure correction neural network for autonomous vehicle

Overexposed and underexposed digital images may occur either by excess or deficiency of lighting during acquisition. These problems are common in uncontrolled environments, specially affecting the visual sensory of autonomous robotic vehicles outdoors. Exposure correction is a traditional task in image processing and computer vision which has been tackled with deep learning techniques lately. In the realm of videos, the naive frame-by-frame application of such methods may cause exposure inconsistencies and flickering between subsequent frames. Therefore, we introduce the V2INet network, a spatio-temporal encoder-decoder architecture which aims for coherent exposure correction in videos. Our architecture synthesises a restored frame taking into account their temporal neighbours in the degraded video. We evaluate the approach on a real dataset acquired by an autonomous vehicle called Berkeley Deep Drive (BDD). We use the SSIM and PSNR metrics, and also introducing a surrogate measurement to assess temporal coherence. The V2INet has shown competitive results in both quantitative and qualitative comparisons with closely related methods, being capable of enforcing temporal consistency in the restored videos.