Reviewerly/exHarmony huggingface dataset:
Multiple Intelligences Profile of Grade IV Elementary School Students in Magelang

This study aims to determinant the profile of student's multiple intelligences.The sample in this study was the fourthgrade students at State Elementary School of Pasuruhan 1, Mertoyudan District, Magelang Regency for the Academic Year 2021/2022 as many as 28 students.Female student consists of 18 students and male student consist of 10 students.The method used in this research is survey.The result of this study is, both male and female students have different multiple intelligences with different learning styles.Based on student's gender, male students consist of 2 students dominant in logic mathematic intelligence, 7 students were dominant interpersonal intelligence, and 1 student was dominant in intrapersonal intelligence.Meanwhile female students consist 11 students dominant in interpersonal intelligence, 1 student was dominant in intrapersonal intelligence, and 2 students are dominant in interpersonal intelligence.Overall, fourth grade students are interpersonal intelligence.The highest score is 584 with a percentage of 17,99% on interpersonal intelligence and the lowest score is 290 with 8,93% intelligence on logic mathematic intelligence.
Object Detection in Aerial Images with Uncertainty-Aware Graph Network

In this work, we propose a novel uncertainty-aware object detection framework with a structured-graph, where nodes and edges are denoted by objects and their spatial-semantic similarities, respectively. Specifically, we aim to consider relationships among objects for effectively contextualizing them. To achieve this, we first detect objects and then measure their semantic and spatial distances to construct an object graph, which is then represented by a graph neural network (GNN) for refining visual CNN features for objects. However, refining CNN features and detection results of every object are inefficient and may not be necessary, as that include correct predictions with low uncertainties. Therefore, we propose to handle uncertain objects by not only transferring the representation from certain objects (sources) to uncertain objects (targets) over the directed graph, but also improving CNN features only on objects regarded as uncertain with their representational outputs from the GNN. Furthermore, we calculate a training loss by giving larger weights on uncertain objects, to concentrate on improving uncertain object predictions while maintaining high performances on certain objects. We refer to our model as Uncertainty-Aware Graph network for object DETection (UAGDet). We then experimentally validate ours on the challenging large-scale aerial image dataset, namely DOTA, that consists of lots of objects with small to large sizes in an image, on which ours improves the performance of the existing object detection network.
Scene-Adaptive Temporal Stabilisation for Video Colourisation Using Deep Video Priors

Automatic image colourisation methods applied independently to each video frame usually lead to flickering artefacts or propagation of errors because of differences between neighbouring frames. While this can be partially solved using optical flow methods, complex scenarios such as the appearance of new objects in the scene limit the efficiency of such solutions. To address this issue, we propose application of blind temporal consistency, learned during the inference stage, to consistently adapt colourisation to the given frames. However, training at test time is extremely time-consuming and its performance is highly dependent on the content, motion, and length of the input video, requiring a large number of iterations to generalise to complex sequences with multiple shots and scene changes. This paper proposes a generalised framework for colourisation of complex videos with an optimised few-shot training strategy to learn scene-aware video priors. The proposed architecture is jointly trained to stabilise the input video and to cluster its frames with the aim of learning scene-specific modes. Experimental results show performance improvement in complex sequences while requiring less training data and significantly fewer iterations.
The Effect of Top Technology Enterprises’ Investment to Small Tech Businesses on Government funding in America

Do top tech companies affect the government funding to small businesses?This study collects and reorganizes the data corresponding to wealthy elites from 2015 to 2020 in the United States of top 50 technology industries and applies panel data model to examine whether the grants from top technology companies to small tech business will affect the funding from government to small businesses.The result statistically reflects an impact of the top tech industries toward policy and finds that the growing power of technology companies has a certain degree of influence on the policy of government funds.
Compound Protection of Network Short Video Copyright Based on Blockchain and DRM

Short video has developed rapidly in recent years due to its fragmented characteristics, but the establishment of industry regulation system fails to keep pace with the development of the market, resulting in serious copyright infringement.In view of the infringement phenomenon and market governance pain points in the short video industry, this paper proposes that a "third party" should build an independent "copyright protection and service platform".The platform is characterized by a blend of DRM and blockchain technology.At the same time, this paper hopes to use integration to realize copyright-based "rights item" transactions and services, fundamentally solve the phenomenon of "small, scattered, and chaotic" short video rights protection, and propose a multi governance model for copyright circulation.
Study of the Impact of Chinese Policies and Regulations on Bitcoin

Bitcoin is an emerging digital virtual currency with both decentralization, high degree of freedom and high bookkeeping efficiency, and the transaction scale of its market has developed rapidly in recent years.This paper selects the closing price of Bitcoin from May 2015 to August 2022, divides the data into two groups to establish a GARCH model based on the date of promulgation of the Bitcoin ban, and determines the impact of the regulation on the fluctuation of Bitcoin transactions by comparing the volatility characteristics of the two sets of models.This study is conducive to determining the direction and size of the impact of some policy changes on bitcoin fluctuations, and has a reference effect on the determination and adjustment of bitcoin-related policies and regulations.
The Application of Data Mining Technology in the Evaluation of English Teaching

The evaluation of teachers' teaching quality in colleges and universities can effectively improve the quality of teaching and regulate teaching behavior.This article has conducted an in-depth study of the current data mining technology, and analyzed the advantages and disadvantages of each data mining technology.This article constructs a scientific English teaching evaluation system, through this system, students can evaluate teachers' teaching behavior from multiple dimensions.This paper establishes a data mining system that can find valuable information that can improve teaching quality from a large amount of teaching-related data and a large number of students' evaluations of teachers, and pass this information to teachers.This system mainly regards teaching attitude, teaching content, and teaching methods as the main aspects of teaching evaluation, which reduces the attributes of mining libraries and improves the efficiency of mining.This system mainly uses decision tree algorithm to mine teaching information.First, the teaching evaluation decision tree is constructed, and then the decision tree is pruned, the corresponding rules are extracted, and the knowledge base is created.
The Impact of Business Model Innovation on The Dynamic Capabilities of Online Literature Reading Platforms

With the rapid development of online literature, the needs of readers are increasingly diversified, and the competition among online literature reading platforms is constantly intensifying.Facing the dynamically changing external environment, each platform can only continuously enhance its own dynamic capabilities and carry out business model innovation, in order to develop continuously.This paper takes the Chinese online literature reading platform as the research object to study the relationship between business model innovation and the dynamic capabilities of the online literature reading platform, and collects 201 questionnaires for employees of nine online literature reading platforms.Through regression analysis, the following conclusions are drawn: Business model innovation has a significant positive effect on the perception ability of the platform, the development ability of emerging technologies, the creativity of employees, and the ability to improve user transfer costs.
Prediction of Students' Academic Learning Performance Based on Big Learning Data

With the development of computer technology and network technology, intelligent teaching platform is gradually applied to the field of education, such as classroom on rainy days.It collects and records a large amount of classroom data, such as student attendance, classroom interaction and classroom tests.How to analyze and make use of these data is very important to understand students' learning status and predict their final achievements in the future.In this paper, 171 students majoring in electrical engineering in a university are selected to evaluate the correlation between their final grades and the three indicators in the classroom data.The results show that there is a strong correlation between the final score and the test score.The students with lower class test scores may have a higher risk of failing the final score, which provides useful experience for the judgment of failing the final score, and actively takes preventive and control measures in the follow-up teaching process to urge students to pay attention to classroom teaching.
Intelligent Question Answering System Based on Domain Knowledge Graph

This paper introduces an intelligent question answering system based on the domain knowledge graph of military battle cases.Through the collection and accumulation of military big data, we first build a domain knowledge graph for military battle cases, and then use natural language processing related technologies to understand natural language problems, mainly intention recognition and slot filling.On problem intent identification.In this paper, BERT+TextCNN model is proposed to realize the intention classification of questions.LAC is used to segment the natural language questions and extract the entities in the question sentence in slot filling.The answer is then retrieved from the knowledge graph.The test results show that the accuracy of question comprehension in the question set is more than 90%, and it can answer most of the questions in the field quickly and accurately.
Integration of Big Database of College Teachers' Talent Construction from the Perspective of Human Resource Management

In order to improve the ability of college teachers' talent management and human resource management, and build a large database integration method of college teachers' talent construction from the perspective of human resource management, a large database integration method of college teachers' talent construction from the perspective of human resource management is proposed based on Internet of Things scheduling of information resources.A distributed detection model of university teachers' talent construction database from the perspective of human resource management is constructed.The Internet of Things is used to optimize the node deployment design during the integration process of university teachers' talent construction database from the perspective of human resource management.The routing node distributed detection protocol of multimedia sensor network is constructed.The semantic association rule detection method is used to extract the association features of university teachers' talent construction big data from the perspective of human resource management.In this paper, the extracted features of university teachers' talent construction big data from the perspective of human resource management are integrated and fused, and the fuzzy clustering method is used to realize the optimal integration and adaptive scheduling of university teachers' talent construction big database from the perspective of human resource management.The hierarchical scheduling and integration model of university teachers' talent construction big database from the perspective of human resource management is constructed to realize the optimal design of university teachers' talent construction big database from the perspective of human resource management and improve the efficiency of database access and scheduling.The simulation results show that this method has high integration degree, good data clustering and good data checking performance in the large database integration of university teacher talent construction from the perspective of human resource management.
Individual innovativeness and leadership self-efficacy among the teacher candidates

A limited number of studies have assessed the relation between individual innovativeness and leadership self-efficacy among the teacher candidates. This research investigated the mediating effects of Self-Regulation Approach, to determine the direct and indirect relationships between teacher candidates' individual innovativeness and leadership self-efficacy. Participants (between 17 and 24 years old) at a university (N=350) participated in this study, 68.9% (241) of the participants were female, 31.1% (109) were male. The correlational survey model was used. Instrument of the study was comprised of four sections: (i) Personal Information Form containing demographic variables, (ii) Individual Innovativeness Scale (IIS), (iii) Leadership Self-Efficacy Scale (LSES) and (iv) Self-Assessment Approach Scale (SAAS). The results show that regression analysis was used to test the hypotheses, and leadership self-efficacy scale was defined as dependent, individual innovativeness as independent, self-assessment approach as mediator, and gender and grade level as control variables.
Educational Policy Formulation Based on Blockchain Technology

The inherent decentralization and security properties of blockchain make it well suited to address today's educational decision-making challenges.The educational policy evaluation index system is built on the basis of blockchain technology.The purpose of this paper is to study and implement educational policy formulation based on blockchain technology, and to systematically analyze the theoretical basis for educational policy formulation and evaluation using blockchain technology-related theories, combined with normative research, comparative research, and empirical research methods.This paper analyzes the process of educational policy formulation, designs an educational policy evaluation model, and provides a systematic and standardized management method for educational policy formulation and evaluation.The indicator weight calculation results show that the policy effect accounts for 43%, the policy effect accounts for 23%, and the policy effect accounts for 25%.In addition, taking the education policy of M province as an example, to verify the scientificity and operability of the education policy evaluation model established in this paper, the results show that the actual effective rate of the province's education policy is 63.56%, and the overall result is "average".It belongs to the middle and lower classes, which is in line with the actual situation.
Optimization Analysis of Public Policy Online Voting System Based on Data Mining Algorithm

Correct decision-making and effective implementation of public policies will bring positive results to the development of the economy and the advancement of society.Errors in decision-making or improper implementation will bring certain negative effects.In a sense, the issue of public policy is the most important issue of a country's legislation and judiciary, and it is also the main method used by the authorities to safeguard the interests of the people.With the development of the Internet, a lot of information can be effectively processed through computer network technology, so information feedback has become the goal that people pursue, and the online voting system is one of the products.In this paper, a network online voting system is specially designed for public policy formulation and decision-making.The association rules algorithm in the data mining algorithm is introduced, and the system can exclude the user IPs who voted repeatedly.Through the algorithm statistical data results, the voting rate and voting items can be found.The algorithm also optimizes the statistical process of voting results, making voting data processing more convenient and accurate.After the system is tested, the operational reliability of the system in this paper is also verified.
Research on the Design of Teaching Guidance APP Based on the Peak-End Rule

In view of the current problems such as confusing interface, cumbersome operation, and poor experience of educational learning APPs, this paper proposes a design method for educational learning APPs based on the peak-end law, so as to improve the overall user experience.Based on the peak-end law, the literature research method and user interview method are used, combined with the service design theory, to find the peak and end time of users in the process of use, and fully explore the needs and preferences of users.On this basis, the design principles of educational learning APPs are determined, and the design practice is carried out.This method is used to help users get the best experience in the whole service process, so that they can generate continuous usage behavior.In particular, it enables student users to have an efficient learning experience, so that they can gain a sense of learning achievement, and provide a variety of design ideas for the design of such APPs.
Current Status and Trends of Blended Learning in Higher Education

Blended learning can be seen as an important opportunity to redesign and redevelop the higher education curriculum, due to its advantages such as greater flexibility, autonomy, participation, and more effective learning effects.This paper analyzes both current state and trends of blended learning in higher education.It highlights that learning management systems, social media and many other tools have been developed to implement blended learning in higher education institutions.The future of blended learning in higher education will be more personalized, diverse, innovative, student-centred, and high-quality.
Student Achievement Motivation: In Relation to Socio-Economic Conditions

This study aims to prove the relationship between the socioeconomic conditions of parents and the motivation for learning achievement of students of the Tourism Business Management Study Program during the Covid-19 pandemic.The problems that want to be studied in this research are: What is the socioeconomic conditions of the parents of the Tourism Business Management Study Program students in a pandemic situation and is there a relationship between the socioeconomic conditions of the parents on the achievement motivation of the students of the tourism business management study program during the covid 19 pandemic.Research population This is the entire batch of students of the Tourism Business Management Study Program with a total sample of 87 students using the Slovin Formula.In this study using the questionnaire method and using auxiliary methods, namely the documentation method and the interview method.The analysis used is descriptive percentage analysis and Chi Square analysis.The results showed no significant relationship between the socioeconomic conditions of parents and the achievement motivation of the students of the Tourism Business Management Study Program.This is caused by several factors other than socio-economic factors, where the students who are the research sample are generation Z students, are the generation who understand and master technology.
Blueprint for Integration of University Archive Resources Based on City Memory

The specifications of archive resource construction in one university are usually different from others, and university archive resources are mostly scatted outside of city memory.The origin, necessity and basis of university archive resource integration are analyzed in this article, and it is proposed that based on city memory network platform, university archive resources can be integrated by constructing work mechanism, formulating technical standards, coordinating resource construction, and establishing data integration platform.The blueprint proposed here for university archive resource integration helps the establishment of systematic and complete city memory besides the improvement of university archive resource construction itself.
Teaching Reform and Exploration of Computer Network Course Oriented to Output and Blended Method

The traditional offline classroom teaching method of Computer Network is difficult to effectively improve students' learning interest and independent learning ability, and it is not easy to track or analyze students' learning progress as well.Combined with the advantages of online teaching and the output-oriented concept, blended curriculum teaching reform and exploration are carried out to enhance students' participation, enthusiasm and initiative in learning, and promote the continuous improvement of teaching quality.
Developing Folklore Learning Media Based on Digital Comics Sasakala Ngaran Cianjur

The Covid-19 pandemic has inclusively changed people's lives.Various activities carried out in public spaces have turned into virtual activities that are increasingly massive and entrenched in the community.One of the sectors affected is the education sector.Everyone is required to learn adaptively through digital technology instruments.Thus, acceleration in the use of technology cannot be avoided.This study aimed to develop folklore learning media based on digital comic Sasakala Ngaran Cianjur using Clip Studio Paint software.The method used in this study was the Research and Development (R&D) method with the ADDIE development model consisting of five stages, including: analysis, design, development, implementation, and evaluation.The data collection techniques and instruments used were questionnaires and tests.The results showed that the digital comic content of Sasakala Ngaran Cianjur had one cover page and 26 pages of story content with a total of 178 panels.Based on the validation of the three experts, it showed that the learning media for digital comics based on the folklore of Sasakala Ngaran Cianjur was very feasible to use.Similarly, the trial's results on small group students obtained a percentage of 93.8%, which means that the digital comic Sasakala Ngaran Cianjur was very suitable for learning.Thus, the digital comic media of Sasakala Ngaran Cianjur can be used as an alternative material for learning to read literary works of folklore (saga).