Reviewerly/exHarmony huggingface dataset:
The Path of Digital Government and University Asset Intelligence Value-Added Service Driven by Block Chain Technology

At present, the government and universities mainly adopted the centralized management mode in the sharing of asset digital resources, which not only had low work efficiency, but also cannot make full use of resources. Block chain technology played an important role in enterprise asset data resource data sharing, but there was less research on asset data sharing services applied to digital government and universities. Therefore, this paper proposed the research on the path of government and university asset value-added service driven by block chain technology, in order to provide reference for improving the utilization rate of state-owned assets and better meeting the needs of users. Based on the research on the problems existing in the digital resource management of relevant government agencies and university departments at home and abroad, combined with the application advantages of block chain technology in relevant fields, this paper analyzed the composition of block chain and the application technical characteristics of value-added services. Starting from the current situation of asset data information management by the government and universities and the needs of users for resources, this paper expounded the important impact of block chain technology on digital government and university asset information management. By analyzing the elements of block chain value-added service, this paper put forward the block chain-driven asset intelligent value-added service mode. In order to track and manage users’ transaction information and meet users’ demand for resources, the application path of asset intelligent value-added service driven by block chain technology was constructed based on the asset value-added service mode, and combined with the characteristics of users’ request for resources, the implementation method of asset intelligent value-added service path was proposed. The experimental results showed that the method proposed in this paper had more advantages than the existing data sharing service system, and can effectively provide technical support for asset value-added services.
Application of Grammar Error Detection Method for English Composition Based on Machine Learning

With the development of grammar-checking technology and algorithms, the grammar-checking system has been widely used in various fields. This paper designs and implements a grammar-checking system for English composition. The grammar-checking system adopts a multimodule design. The grammar-checking system is composed of a multilayer rule error-correcting module and a machine learning error-correcting module. This study aims to build a machine learning algorithm model that can detect English grammar errors by analysing and comparing different algorithm models currently applied in the field of education and then apply the trained model to the English composition grammar detection system. The results show that the system can save a lot of time and labor cost of manual marking, liberate teachers from heavy and repeated evaluation activities, and put more time and energy on teaching. At the same time, it can provide learners with more objective and timely feedback so that learners can intuitively and clearly know that they are prone to make grammatical mistakes in the process of English learning. It plays a certain assisting and guiding role in English learners’ autonomous learning.
Investigation of Factors Influencing Malaysian ESL Learners’ English Speaking Skills

Speaking skills are crucial to develop effective communication. It is known to be the most important skill to be equipped with among all the four language skills to enable one to communicate in this global world. ESL learners find it difficult to speak in English despite learning the English Language in primary and secondary school. This can be clearly seen especially if they do not have good command of the language. This study aims to examine ESL learner’s perception of the factors influencing their English-speaking skills. Qualitative research design was employed whereby the data was collected through semi structured interviews. Forty students and four teachers were selected from an independent Chinese school in Malaysia as participants of this research. The students were interviewed to identify the factors that influence the students’ English-speaking skills and the teachers were interviewed to gain their perspective on the ways to help improve the English-speaking skills among the ESL learners. The findings of this research indicate students have fear of making mistakes, low confidence, shyness, introversion and are anxious when speaking in English. The teachers believe having topic-based discussion, role play and pair work are essential in helping the ESL learners to show improvement in the speaking skills.
Detection of Solar Panel Farms Using Machine Learning

There has been an increase in the number of solar energy farms all over India in an effort to move toward more sustainable sources of energy. The installation of these is widely unregistered, and thus, they are popping up everywhere without any official record. So, we can use satellite images taken over remote villages to map and find out the number of solar farms. We can train our machine learning algorithm over these images to correctly identify solar farms. This can then be used in the future to keep a tab if and when the number of solar farms changes.
Machine Learning for Job-flow Online Scheduling and Resources Allocation in Distributed Computing

In this work we study a promising approach for efficient online scheduling of job-flows in high performance and distributed parallel computing. The majority of job-flow optimization approaches, including backfilling and microscheduling, require apriori knowledge of a full job queue to make the optimization decisions. In a more general scenario when user jobs are submitted individually, the resources selection and allocation should be performed immediately in the online mode. In this work we consider a neural network prototype model trained to perform online optimization decisions based on a known optimal solution. For this purpose, we designed MLAK algorithm which implements 0-1 knapsack problem based on the apriori unknown utility function. In a dedicated simulation experiments with different utility functions MLAK provides resources selection efficiency comparable to a classical greedy algorithm. The interdisciplinary nature of the considered problem and its solution (including elements from the areas of optimization, scheduling algorithms, machine learning and simulation) makes it a good study example in technical disciplines.
A Study on the Effect of Video Resolution on the Quality of Sound Recovered using the Visual Microphone

The visual microphone recovers sound from object vibrations in a video by detecting the small vibrations of the objects caused by sound and converting the vibrations back into sound. Such vibrations are often very small and unnoticeable to humans. This difficulty is compounded by the fact that the target objects for sound recovery may not occupy a sizeable area of the video frame to allow for more pixels to be used in sound recovery. Changing the video resolution by up scaling and down scaling the video changes the number of pixels available for the visual microphone algorithm to work with and these effects on the quality of sound recovery are yet to be explored. This paper investigates the gap in the literature by up scaling videos with neural networks, down scaling videos, and a combination of both, on the recovered sound quality. It was found that changing the video resolution by any means gives insignificant improvements to the recovered sound quality at best and degrades the recovered sound quality in most of the studied cases. As a result, it is suggested not to apply any change in video resolution prior to sound recovery using the visual microphone.
Nexus between financial development, FDI, industrial structure change and innovation in the post-financial crisis era: Evidence from China

China began to strive for innovation-driven economic growth by encouraging technological progress since the recent world financial crisis. Further, Chinese economy exhibited several different features since the crisis. Its financial credits surged. Meanwhile, it began to adjust its industrial structures. In order to test the effect of these policies, this paper investigates whether financial development, FDI and industrial structure changes affect innovation in this new era. The results find that provincial variation in innovation performance enlarged during this period. Further, financial development significantly improves innovation. However, FDI hampers innovation. Lastly, industrial structure adjustment promotes innovation. The paper suggests that China improve the technological skills of local firms and its human capital and introduce high-quality and efficient foreign direct investment to achieve an innovation-driven economic growth.
Dance Movement Interference Suppression Algorithm Based upon Contour Model and AdaBoost Algorithm

With the development of computer technology, digital image processing, as an emerging field, has become an indispensable tool in the information society. The action interference effect refers to the phenomenon that an individual reacts to a dangerous object longer than a safe object in the state of preparation for action. Currently, there is no effective anti-jamming technology to recognize dance movements. In order to solve these problems, this paper proposes a dance movement interference suppression algorithm based on contour model and AdaBoost algorithm, which aims to realize the application of gesture-based behavior recognition technology in the field of dance steps and complete the recognition of some dance steps. The method of this paper is to study the contour model based on the image and then propose to use the AdaBoost algorithm to detect body movements and finally use the radio frequency interference suppression algorithm to ensure the smooth operation of the technology. The function of the method is to propose a dance action recognition and anti-interference algorithm based on contour model and AdaBoost algorithm. This article describes the contour model and then uses the AdaBoost algorithm to recognize the action and adjust the action interference effect. The results show that the algorithm proposed in this paper can accurately identify dance movements, with an average recognition accuracy rate of 99.2%.
An asymmetric controlled bidirectional remote preparation protocol of three- and four-qubit equatorial states

A scheme for asymmetric controlled bidirectional remote state preparation by utilizing 15-qubit entangled state as quantum channel is proposed. In this paper, we present a new protocol that Alice prepares a three-qubit equatorial state for Bob and Bob prepares a four-qubit equatorial state for Alice at the same time under Charlie’s control. The success of this scheme cannot be separated from the controller Charlie. In addition, we give some analysis and conclusions by comparing with other schemes. The auxiliary qubits and operations are not required in our scheme. In particular, the success probability of the receivers restoring the prepared states can reach 100%. Thus, our scheme has better performance.
Enhancement of Dijkstra Algorithm for Finding Optimal Path

The Dijkstra algorithm is a common method used when solving shortest path problems.It is a graph-based method that compares node distances, selects the shortest subsequent node, and generates an ideal path.However, it is seen that the method struggles with memory utilization, particularly when working with substantial amounts of data or graphs.The purpose of this research work is to improve the algorithm to tackle the current problem.The enhancement is accomplished by providing an approach in which the two closest nodes are combined in each iteration.With this, the conclusion of the study is that the enhanced Dijkstra algorithm, with the application of node combination, was able to reduce the memory usage in comparison to the existing Dijkstra algorithm.
How Blockchain Will Change Leadership Strategies for Effectively Managing Organizational Change

OPINION article Front. Psychol., 02 June 2022Sec. Organizational Psychology https://doi.org/10.3389/fpsyg.2022.907586
Development of Design and Implementation Strategies of Synchronous Blended Learning for Adult Learners

This study aims to develop the design and implementation strategies and examine learners’ perceptions of synchronous blended learning classes. To this end, this study applied the Design and Development research method and developed 12 strategies and 44 detailed guidelines. 15 adult learners participated in synchronous blended learning classes for two days and 13 participants’ survey data were collected. As a result, it was found that learners positively perceived class satisfaction, participation mode satisfaction, flow, perceived learning outcomes, teaching presence, collaborative process satisfaction, and socially shared regulations. Based on the results, we discussed what to consider when designing and implementing synchronous blended learning. Through the study, it is expected to improve the quality of synchronous blended learning conducted by educational institutions in the era of COVID-19.
Construction Method of Industrial College in Vocational Colleges Based on Cluster Analysis Algorithm

In the context of the combination of industry and education, the construction of industrial colleges in vocational colleges can drive the scientific development of specialty settings in colleges and universities, and promote the way for colleges to expand students’ practical teaching under the teaching of theoretical knowledge, and it is also an effective way for students to stimulate their learning enthusiasm and innovation enthusiasm. Colleges and universities can increase the direction and characteristics of specialist settings in colleges while enhancing instructors’ professional level through school-business collaboration, and growing measures of talent training in colleges and universities plays a significant guiding role. The way to set up industrial colleges in vocational colleges reflects the development characteristics of talent training mode in the new era, and it is also an effective way to meet the practical training of students and the actual needs of society. It is a new school running mode of transforming productivity, cooperation, and mutual benefit, which is very worthy of promotion and development. This paper analyzes the problems existing in the construction of industrial colleges in vocational colleges in China and finds out the corresponding solutions. A path method of industrial college construction in vocational colleges based on the cluster analysis algorithm is proposed. The validity of this model is verified by experiments, which lays a foundation for the construction of industrial colleges in vocational colleges.
Pengenalan Dan Pelatihan Implementasi Tool Mendeley Guna Efektivitas Penyususnan Karya Ilmiah

&#x0D; &#x0D; &#x0D; The management of citations and reference articles from various scientific journals must be mastered by students in order to produce good scientific works. One of the tools that can be used in managing scientific work citations is the Mendeley tool. The Mendeley tool provides a lot of convenience in improving the quality of writing scientific articles, namely making it easier for students to make citations and bibliography. This training is held at the Universitas Islam Indragiri (UNISI) by involving students who will carry out final research (scripts).&#x0D; &#x0D; &#x0D;
Antileakage Technology of Computer Video Information Encryption Based on Digital Information Method

In order to better realize and ensure the security performance of network information itself, various possible risks must be prevented in advance, and a more reliable operation environment must be created as much as possible. A research study on computer video information encryption and antileakage technology based on the digital information method is proposed. Through the research on the working principle of computer display systems, this paper analyzes the common leakage sources. On this basis, it analyzes how to protect the electromagnetic wave leaked from the computer display system. Finally, it is proposed that the protective measure adopted in this paper is (transient electronic pulse emission survey technology, tempest) technology; that is, the image is processed at the source of image information to reduce the electromagnetic radiation of useful information. The corresponding digital filter is designed by fir, and the combined filtering of digital filter and analog filter is applied to eliminate the inherent disadvantage of periodic fluctuation of insertion loss of the digital filter. The research results show that the receiver before filtering can receive video images at multiple frequency points such as 20 MHz, 160 MHz, 220 MHz, 250 MHz, 270 MHz, 290 MHz, and 340 MHz, while the receiver after filtering can only receive video images at two frequency points of 20 MHz and 250 MHz, but it cannot recognize the words in the figure, and its effect is very obvious. It verifies the correctness and effectiveness of the theoretical analysis in this paper.
Task Transfer and Domain Adaptation for Zero-Shot Question Answering

Pretrained language models have shown success in various areas of natural language processing, including reading comprehension tasks.However, when applying machine learning methods to new domains, labeled data may not always be available.To address this, we use supervised pretraining on source-domain data to reduce sample complexity on domainspecific downstream tasks.We evaluate zeroshot performance on domain-specific reading comprehension tasks by combining task transfer with domain adaptation to fine-tune a pretrained model with no labelled data from the target task.Our approach outperforms Domain-Adaptive Pretraining on downstream domainspecific reading comprehension tasks in 3 out of 4 domains.
Continual Digital Twin Technology Application on the Construction of English-Chinese Bilingual Teaching Mode

This study enhances the conventional English-Chinese bilingual teaching mode by incorporating the continuous digital twin technology into its design. In addition to the typical video and audio transmission teaching functions, the English-Chinese bilingual distant education system also includes a number of interactive capabilities. The approach described in this study is mostly utilized for bilingual instruction in colleges and universities, and the instruction is enhanced by digital twin technology. In this work, the system performance test focuses primarily on the evaluation of teaching quality and teaching satisfaction. Among these are the evaluation of teaching quality by questionnaires, the evaluation of this paper's system by a number of senior teachers, and the calculation of student satisfaction through teaching experiments. The results of the study indicate that the strategy described in this work has a particular effect.
Invariants for determining entanglements pattern

Abstract We propose an invariant, i.e., a trace of the square of reduced density matrix, to determine the entanglement pattern of <?CDATA $n$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>n</mml:mi> </mml:math> -qubits state. The substate is separable from its complement substate if the invariant of this substate equals one. Meanwhile, the substate is entangled if the invariant is less than one. Only two types of states that the number of evaluations can be determined. First, a completely separable state can be determined after evaluating <?CDATA $\left(n-1\right)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mfenced close=")" open="(" separators=""> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:mfenced> </mml:math> invariants of a reduced single qubit. Second, a completely separable state can be indicated after <?CDATA ${2}^{n-1}-1$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:math> consecutive calculations of invariants, which started from reduced single state, then was followed with reduced double state, reduced triple state, etc, where each invariant is less than one. On the other hand, the number of evaluations of invariants cannot be determined and depend on the pattern of the compound entangle state.
Hybrid Student Authentication System Using RFID Reader and Face Biometrics Using Deep Learning Techniques

Every school, college, and university maintains a record of each student's attendance. Faculty are required to retain accurate and current attendance records. Because it takes a long time to organize records and determine each student's average attendance, the manual attendance record system is inefficient. As a result, a system for organizing student records and calculating average attendance is required The proposed system should be able to save student attendance records in digital format, making attendance management easier. Even in the twenty-firs t century, students' attendance is recorded on attendance forms presented in the classroom by staff members, which takes time and is completely manual. Despite the fact that RFID-based and face recognition-based systems have been shown, they are implemented separately. A RFID reader is integrated with a Face recognition system in the suggested system for student attendance. RFID readers, as well as facial recognition cameras, would be placed throughout campus and in classes. When a student walks onto campus, the reader communicates their id to the server, allowing them to be easily tracked After that, using a deep learning approach known as HAAR Cascade and Neural network algorithm, recognize the face from a real-time camera and match it to a database. This strategy ensures that the attendance records of the pupils are stored correctly and efficiently. As a result, the system will generate a list of kids who have been assigned to detention. It's a small-scale automated programme that's easy to operate, redeemable in time, and dependable.
Optimizing and dimensioning a data intensive cloud application for soccer player tracking

Abstract Cloud-based services revolutionize how applications are designed and provisioned in more and more application domains. Operating a cloud application, however, requires careful choices of configuration settings so that the quality of service is acceptable at all times, while cloud costs remain reasonable. We propose an analytical queuing model for cloud resource provisioning that provides an approximation on end-to-end application latency and on cloud resource usage, and we evaluate its performance. We pick an emerging use case of cloud deployment for validation: sports analytics. We have created a low-cost, cloud-based soccer player tracking system. We present the optimization of the cloud-deployed data processing of this system: we set the parameters with the aim of sacrificing as least as possible on accuracy, i.e., quality of service, while keeping latency and cloud costs low. We demonstrate that the analytical model we propose to estimate the end-to-end latency of a microservice-type cloud native application falls within a close range of what the measurements of the real implementation show. The model is therefore suitable for the planning of the cloud deployment costs for microservice-type applications as well.