Reviewerly/exHarmony huggingface dataset:
Research on Agricultural Enterprise Accounting Information Resource Sharing Model Based On Big Data Technology

The conventional enterprise accounting information resource sharing model has the problem of fuzzy accounting information characteristics, resulting in low schedulability. An agricultural enterprise accounting information resource sharing model based on big data technology is designed. Obtain the goal of agricultural enterprise business process reengineering, establish the standard process of mutual integration of enterprise accounting information, combined with the principle of data mining, extract the characteristics of accounting information by using big data technology, divide the spectrum module according to the deployment density of small cells, design the heterogeneous wireless network resource management model, optimize the resource distribution level, and construct the resource sharing model by using hierarchical scheduling algorithm. Experimental results: the mean schedulability rates of the enterprise accounting information resource sharing model and the other two models are 60.073%, 49.801% and 49.319%, which proves that the enterprise accounting information resource sharing model integrating big data technology has more advantages.
Back to basics

A response to the special issue on quantum technologies, which explored various “quantum 2.0” technologies currently being developed and what impacts they might have if practical, more widely usable devices are achieved.
Crossword buffs beware

An Arxiv paper has outlined the programming behind the Berkeley Crossword Solver – built by a team at the University of California, Berkeley – which in 2021 managed, for the first time, to outscore every human player at the prestigious American Crossword Puzzle Tournament.
Photons stay entangled despite huge jump in frequency

A new technique that keeps two photons entangled while greatly increasing the frequency of one of them has been demonstrated by researchers in Germany. The work could prove useful in quantum computation, imaging and quantum communications.
Machine learning detects seismic activity in noisy cities

A machine-learning algorithm that filters seismic data to remove noise from human activity has been developed by researchers in the US and China.
What Is the Reason for Learning Art?: Changes of Learner’s Recognition on Art through Art Expression Teaching

Objectives This study started from the fact that confinement in the formalism of art expression cannot persuade students to answer the question of ‘What is the reason for learning art?’. Hence, the purpose of this study is to clarify whether learner’s recognition on art can be changed through the case on teaching art expression.&#x0D; Methods This study is done for the objects of the three 6th graders with insufficient confidence in practical skills in art. The researcher planned and practiced art expression class for one 6th grader and analyzed the study result by focusing on the student’s work and the interview details. ‘What type of person am I?’ and ‘What type of person will I want to be?’ are presented as subjects, and the artwork was planned and produced through the story-telling of the student from plan preparation. Only one student’s (alias-Ayeong) work is used with the content of the interview with the student as data due to the limit of space to understand a typical case and do analysis as the qualitative case study on researching the culture pattern.&#x0D; Results A-yeong recognized art as ‘the thing not requiring the use of her head’, ‘making a thing’, and ‘the thing that is not study’ in the pre-interview and was doing an art activity like that of a kindergartener or a low grader even though she was a high grader in the elementary school. Before the actual class, she selected the best photo expressing her to talk about herself and provide the prior experience connecting the meaning and the image. She made the acrylics entitled ‘Vine’ through storytelling and expressed the mind of becoming a warm person to people around her with the huge pot decorated with the vine, the rocker, etc. The changes of learner’s recognition on art could be checked after artwork production. Those changes are ‘A drawing plays the role not replaced by a photo.’, ‘Finding self through art expression’, ‘A good artwork is unique and satisfying.’, and ‘The scope of art expression is wider than that of writing’.&#x0D; Conclusions First, conversation with a student helps a student organize the meaning suitable to the subjects and properly implement the meaning with the formative language. Second, the art creation of an upper elementary school grader should be the creative activity based on the higher quest for the life of self beyond the level of an infantile art activity. Third, the meaning of learning art is convincing only if a teacher cannot directly teach it but a student should experience and realize it for itself.
The programming method of MATLAB language for solving AI based digital health problems

In recent years, the fractional calculus equation has been widely used in many application disciplines, and its numerical solution accuracy and efficiency requirements are also higher and higher. MATLAB is used to solve mathematical problems has been very common, can quickly and intuitively solve the equation. Therefore, we will study the programming method of MATLAB language to solve the digital health problems and linear algebra problems of calculus. According to the general form of fractional order linear constant coefficient multinomial differential equation, the equation model is established. The shifted Jacobi polynomials are deformed by definition and linear algebra theory. Using the function approximation theory, the numerical calculation algorithm of calculus is designed. Use C++ to call the function encapsulation in MATLAB to complete the program design of the AI-based program used for solving health problems and algebra problems. Through the actual calculation example, it is verified that the solution precision of the method used in the study can reach 10-4, which meets the solution precision and can be used to solve practical problems.
PHISHING

In the discipline of computer protection, phishing is the criminally fraudulent method of trying to gather sensitive statistics together with usernames, passwords and credit card information, by way of masquerading as a truthful entity in an digital attempting to accumulate touchy information which includes usernames, passwords and credit card information, by masquerading as a honest entity in an digital communique. Phishing is a fraudulent email that attempts to get you to divulge personal statistics that could then be used for illegitimate functions.
EFFECTIVE MANAGEMENT OF TEACHING TECHNIQUES AT PEDAGOGICAL COLLEGES IN THE CONTEXTOF THE CURRENT INDUSTRIAL REVOLUTION 4.0 AND EDUCATION 4.0 - SITUATION AND MEASURES

Before the trend of industrial revolution 4.0, education 4.0 today, in the process of teaching, technical means of teaching will be both a content and a means of information transmission and at the same time it will also create an learning environment. The interactive pedagogical school helps teachers organize and control students' cognitive activities, in addition, it also helps students have an interest in learning, practice practical skills, thereby forming learning methods. proactive and creative. Technical means of teaching are one of the necessary conditions to help teachers perform jobs related to education, retraining and intellectual development and arouse the inherent intelligent qualities of students. Currently, the management of teaching technology at the Central Kindergarten Pedagogical Colleges has been carried out on a regular basis and achieved certain results, but in fact there are still many problems. Finding out some limitations in the management of teaching technology, thereby proposing measures to overcome those limitations, improve the efficiency of investment, preservation and use technical means of teaching in the trend of Industrial Revolution 4.0, Education 4.0, improving the quality of teaching at the Central Kindergarten Pedagogy Colleges in the current period is a very important task and necessary. Keywords: Management; Effective management; Technical means of teaching; Central College of Kindergarten Pedagogy; Industry Revolution 4.0; Education 4.0.
Comparative Analysis of Rapid Diagnostic Test (ICT) and Microscopy for the Diagnosis of Malaria

Introduction: Malaria is caused by four Plasmodium species (vivax, falciparum, Ovale, and Malariae). Malarial parasites can be diagnosed by examining blood smear under microscope, rapid diagnostic test (ICT), and by detecting nucleic acid of parasite using polymerase chain reaction. This study was conducted to find out the diagnostic accuracy of rapid diagnostic test (ICT) in association with microscopy as a gold standard for the detection of malarial parasites P.vivax, P. falciparum and the effect of parasitic load on both diagnostic methods - ICT and light microscopy. Materials &amp; Methods: The study design was cross sectional validation, done at a private clinical set up in Peshawar, Pakistan. Parasite count was performed to quantify parasitic load strictly according to the rules constituted by World Health organization (WHO). Results: A total of 32 patients were identified to be infected with malarial parasites on light microscopy and 68 cases were found negative. Among the positives, P.vivax was detected in 28 cases and P. falciparum in 4 cases. The ICT results showed 29 of the cases positive for malarial parasites and 71 negative. Among the positives, P.vivax was detected in 25 cases, P. falciparum in 2 cases, while 2 cases showed mixed infection. Thus the ICT result for malaria showed 95% sensitivity and 100% specificity with 97% of diagnostic accuracy. The parasitic count was found to be ≤ 208 in those false negative cases on ICT method which showed the impact of low parasitic load on the diagnostic accuracy of ICT method. Key words: Malaria, Plasmodium falciparum, Plasmodium vivax, ICT
Automatic modulation recognition algorithm based on MSCNN-BiGRU network

In this paper, we propose an automatic modulation recognition (AMR) algorithm based on multi-scale convolutional neural network cascaded bidirectional gated recurrent unit (MSCN-BiGRU) network. Firstly, the network extracts and fuse the multi-scale features of the raw signal I/Q data. and then obtain the global features of the fused features at a larger scale through the BiGRU network. Finally, the softmax classifier is used to complete the recognition. The experimental results show that the average recognition accuracy of the algorithm on the RML2018.01A dataset is 62.42%, surpassing CNN by 3.89% and ResNet by 2.8%, and the maximum recognition accuracy can reach 97.5%.
Blockchain for E-Governance and Tracing Fake News on Social Media

A distributed ledger, or block chain, provides safe and immutable storage. Through the use of cutting-edge information and communication technologies, e-governance enables for the interchange of information between the government and people or organizations. On the one hand, e-governance services provide efficient and quick access to treatment; on the other hand, they heighten the danger of privacy and security breaches. Without the need of a middleman, a blockchain-based e-governance system provides safe and transparent services. Fake news detection and recognition of sources of news has become a hot subject in the news business as well as on social media. Anyone may readily create or edit digital information and broadcast it on social networking networks in the digital age. On the one side, these social networking sites make modern-day communication more easier, but they’ve also introduced new problems to real-world application, such as the viral transmission of false information with malevolent intent. In this chapter, the authors highlight the role of blockchain in e-governance. Moreover, the applications of blockchain in tracing the fake news on social media are also discussed in this chapter.
Current Research Trends and Application of Blockchain in Healthcare and Medical Systems

Blockchain has a number of built-in functions, including a distributed ledger, shared storage, verification, encryption, and immutability, and it has progressed from speculation to realistic implementations in industries including healthcare. Without the need for a central body, blockchain technology allows for a transparent and distributed setting. One of the fields where blockchain is considered to hold a lot of promise is healthcare. Data analysis, which has the potential to merge disparate networks and increase Electronic Health Record (EHR) accuracy, should be prioritised in order to transform healthcare. Blockchain technologies can help with access management, data sharing, and maintaining an audit trail of medical operations. It may also be used to help with opioid prescribing and supply chain management, as well as risk data identification, access monitoring, data storage, and the management of a patient audit trail. The healthcare system is changing to allow for a more patient-centred approach. The healthcare networks based on the blockchain may increase security and resiliency by giving patients control of their data. The biggest problems with prescription protection in the illicit medicine supply chain are related to how the medicines are made in the first place. Since tracing the correct and active pharmaceutical ingredients during actual manufacturing is complex, identifying medications that do not include the expected active ingredients can result in patient injury or death. Blockchain’s unique features enable it to provide a basis for full medication tracking through origin till end customer, and the ability to detect fake medications. This chapter covers the introduction to blockchain technology, its background concepts, and importance of blockchain in the domain of healthcare. A framework for the medical supply chain using blockchain has been proposed. The chapter concludes with various research challenges in the domain of blockchain medical supply chain along with the case study.
Fast grouping for large scale optimization problem

When dealing with the problem of less variables, there are usually many simple and practical methods to solve this kind of problem. However with the scale of the problem continues to grow, the effect of these methods will decline linearly or even exponentially. At the same time, many real problems have the large amount of variables which leads to a significant increase in the dimension of the problem. Usually, we call this kind of problem as large-scale optimization problem. At present, the common idea for large-scale problems is to reduce the dimension of the problem, that is, the decomposition of problem variables. At present, coevolution framework is a very effective method to solve large-scale problems. On this basis, considering the problem of resource allocation, a contribution based CC framework is produced. However, the accuracy and efficiency of problem decomposition are difficult to meet at the same time. In this paper, based on the contribution knowledge of variables, a new decomposition method combining differential grouping(DG) and matrix filling is proposed to realize the fast dynamic decomposition of large-scale problems. Firstly, in the initial stage, DG is used to calculate the correlation between some high contribution variables. These variables are obtained by calculating their contribution ranking to the problem. The correlation between other low contribution variables is finally decomposed by matrix filling. The experimental results on CEC2010 testing benchmark problems confirm the effectiveness of the method proposed in this paper.
The Quality of Electronic Services and their Impact on Banking Performance: An Exploratory Study of the Opinions of A Sample of Employees of Al-Rafidain Bank, Public Administration Branch – Baghdad

The current research is based on a statement of the concept of the quality of electronic services in the bank represented by an independent variable and the concept of banking performance represented by a dependent variable. They were chosen at random, As a sample of (400) employees was taken, and the research was formed as to whether there is an impact in applying the dimensions of the quality of electronic services provided by Rafidain Bank on its banking performance. The practical aspect of the research, and the research tool adopted by the researcher is the questionnaire, The ready-made statistical program (SPSS.v24) was used, and the research reached several conclusions, including: There is a statistically significant effect at the level of significance (0.05) for the variable quality of electronic services in banking performance in Rafidain Bank, Public Administration Branch.
Semiconductor quantum dots as resource for photonic quantum technologies

In this presentation, we will present the progress made within our group towards the establishment of semiconductor quantum dots as a resource for applications in photonic quantum technologies, with a main focus on quantum communication but also briefly addressing universal quantum computing.
A Mobile Robot with the Functions of Visual Navigation and Digital Identification

A robot that uses camera vision to guide movement, enabling pathfinding without relying on sensors other than vision is designed in this paper. Image-based digital recognition is also incorporated, thereby improving the robot’s ability to receive commands and sense the environment. In addition, an adaptive line patrol algorithm based on fuzzy PID control is proposed. The algorithm takes two variables, position deviation and angle deviation between the robot and the guiding line, as inputs to the fuzzy controller, and the robust control effect is obtained and the accurate visual guidance is realized. Finally, it is experimentally verified that the proposed fuzzy PID controller has a better control effect compared with simple PID and traditional fuzzy PID, and the efficient human-robot interaction capability and stable pathfinding function of this robot are proved. The proposed technology is a technical guide for developing robots for hospital environmental monitoring, disinfection, and contactless distribution of items.
Multi-Grouped Grid Map for Semantic-Aware Motion Planning of Mobile Robots

In current navigation systems, path planning is performed on a grid map considering the presence of obstacles in the environment. Although this approach can produce collision-free paths, its performance cannot be guaranteed in dynamic, human-centered environments. The reason for this is that more factors need to be considered, including people’s perceptions, than simply ensuring that one gets from one place to another without colliding with an obstacle. Therefore, the ideal map should be able to manage various types of semantic information in a flexible manner, in addition to tracking obstacles. We propose a multi-group map based on a multi-layer map, which is characterized by allowing the layers to store multiple types of information and be flexibly combined to produce multiple sub-maps that can provide references for semantic navigation. The proposed map has been evaluated through simulation to determine its potential to contribute to the creation of semantic navigation behaviors.
Improving GANs for Long-Tailed Data through Group Spectral Regularization

Deep long-tailed learning aims to train useful deep networks on practical, real-world imbalanced distributions, wherein most labels of the tail classes are associated with a few samples. There has been a large body of work to train discriminative models for visual recognition on long-tailed distribution. In contrast, we aim to train conditional Generative Adversarial Networks, a class of image generation models on long-tailed distributions. We find that similar to recognition, state-of-the-art methods for image generation also suffer from performance degradation on tail classes. The performance degradation is mainly due to class-specific mode collapse for tail classes, which we observe to be correlated with the spectral explosion of the conditioning parameter matrix. We propose a novel group Spectral Regularizer (gSR) that prevents the spectral explosion alleviating mode collapse, which results in diverse and plausible image generation even for tail classes. We find that gSR effectively combines with existing augmentation and regularization techniques, leading to state-of-the-art image generation performance on long-tailed data. Extensive experiments demonstrate the efficacy of our regularizer on long-tailed datasets with different degrees of imbalance.
User-Guided Data Expansion Modeling to Train Deep Neural Networks With Little Supervision

Image segmentation is a challenging and essential task in remote sensing. Deep neural networks (DNNs) have successfully segmented images from different domains, but the models usually require time-consuming and expensive pixel-level data annotation. In this letter, we exploit a recent technique to learn features (an encoder) from a few markers placed by the user in relevant image regions, build an encoder–decoder model from a small set of regions delineated by click-based segmentation, and use that model to annotate the remaining pixels. Such user-guided data expansion modeling can be repeated as the encoder–decoder network improves, and by selecting well-annotated regions, the user considerably expands the pixel set to train DNNs with little supervision. We show the role of feature learning from image markers (FLIM) and that our data expansion model can significantly improve the generalization performance of a state-of-the-art DNN when segmenting buildings in aerial images of distinct cities.
Analyzing the relationship among Airline service students" English Learning Beliefs, Strategies, and Academic Achievement

The purpose of this study is to investigate the relationship of English learning beliefs and strategies on their academic achievement of Airline Service department’s students. The questionnaires from 233 students were collected in April in 2022. Collected data was analyzed by utilizing SPSS 20.0 Statistical Program.BR Firstly, it shows that both
Perencanaan Instalasi Penangkal Petir Pada Bangunan Industri Furniture

In planning external protection systems, the methods that are widely used are conventional and electrostatic lightning rod methods, therefore to study further, the results of observations that have been made to the industry there is still no lightning rod system so the author will try to plan and compare the plans of the two types of rods. lightning rods are Conventional ( Franklin ) and Electrostatic ( EF Lightning Protection System ) lightning rods in terms of protection, technical, economical, advantages and disadvantages. The research and observation methods were carried out in the furniture industry area of PT. Marufuji Kenzai Indonesia, with data collection techniques through direct observation, interviews, and literature studies, so that the building plan data (height, width and length of the building) are obtained and the 2020 thunder day data (IKL 238) then the data obtained is used to calculate the parameters in planning the installation of lightning rods according to the standards of SNI 03-7015-2004 and PUIPP. So that the results obtained for planning the conventional method ( Franklin ) with the placement of air terminations using the rolling ball method obtained 22 finials installed with buildings that can withstand currents of up to 160.1 kA and for the electrostatic method ( EF Lightning Protection System ) with 1 finial the building can withstand currents up to 25,891 kA, and if it exceeds this value a lightning rod will be caught.