Reviewerly/exHarmony huggingface dataset:
Design of a decentralized Internet-based control system for industrial robot robotic arms

Abstract To meet the needs of small and medium-sized processing plants by establishing a software and hardware platform for a robotic arm control system with a hierarchical structure and designing a robot robotic arm motion control system that is adaptable to multiple environments and cost-effective. In this paper, the industrial robot robotic arm motion control system is designed based on the consistent fusion tracking algorithm under the decentralized Internet. While using the VisualC++ development environment for writing human-machine interface programs using the MFC framework, the device controls the robotic arm body to complete predetermined movements or operational tasks based on command information, sensing information, and the robot controller. The AMS1086CD-3.3 low-voltage differential linear regulator chip used in this design effectively implements the circuit design from 4.8V to 3.4V and successfully meets the 3.4V voltage supply required by the controller. The industrial robot robotic arm control system adopts an adaptive backstepping algorithm controller, and the error of the tracking system at 0 seconds in the simulation results is always kept within the range of [-1~1] that meets the requirements, which effectively reduces the control error of the robotic arm position. Therefore, the robotic arm control studied in this paper is composed of a complete control system in hardware and software, and the designed adaptive backstepping algorithm controller achieves the demand for good control performance of the robotic arm proper.
A Study on the Perception of Artificial Intelligence Education and the Artificial Intelligence Teaching Efficacy in Secondary School Technology Teacher

The purpose of this study was to analyze secondary school technology teachers' perception of AI and AI teaching efficacy.To achieve the purpose of the study, a questionnaire was selected and 384 questionnaires were collected through an online survey.For data analysis, frequency analysis and descriptive statistics were conducted, and independent sample t-test and one-way ANOVA were used to analyze differences according to variables.As a result of the study, first, the level of perception of the necessity and possibility of artificial intelligence education in technical education was quite high, and the level of perception of high school teachers, teachers with more than 5 years of experience and less than 10 years of experience in AI-related training was statistically significant.Second, the artificial intelligence teaching efficacy of secondary technology teachers was higher for male teachers than female teachers, high school teachers compared to middle school teachers, and teachers with training experience, and teachers with more than 20 years of experience showed the lowest level.Third, there was a positive correlation between the perception of AI education and AI teaching efficacy.Based on the above research results, in order to raise perception of artificial intelligence education and enhance artificial intelligence teaching efficacy, it is necessary to establish an artificial intelligence education system in middle and high school technical subjects and to provide opportunities for customized artificial intelligence-related training support by gender, school level, and teaching experience will be needed.
mStore: Schema Mining based-RDF Data Storage

The relationship-based approach is an efficient solution strategy for distributed RDF data management. The schema of tables can directly affect the system’s storage efficiency and query performance. Most current approaches are based on fixed schema(e.g., VP, ExtVP). When facing large-scale RDF datasets and complex SPARQL queries requiring many joins, such methods suffer from problems such as long pre-processing time and poor query performance. Schemas with Pareto Optimality between the system’s space consumption and query efficiency are needed but also hard to find. Therefore, we propose mStore, a prototype system with flexible schemas based on schema mining. The intuition behind our approach is that we want to divide the combinations of predicates with higher relevance into the same schema, which can replace costly joins with low-cost selects, improving the query performance. The results show that our system performs better on complex query workloads while reducing the pre-processing time overhead compared to systems with fixed schema partitioning strategies.
FeDDkw – Federated Learning with Dynamic Kullback–Leibler-divergence Weight

Federated learning (FL) has emerged as a promising framework for collaborative machine learning. As one of the most well-known bottlenecks of FL, data heterogeneity, i.e., non-IID data, has seriously hampered the convergence rate and model accuracy of FL. Although there are many works that aim to deal with this problem, none of them have directly exploited the data heterogeneity information, and thus cannot handle the problem effectively in general. In this paper, we try to answer the following fundamental question, whether this data heterogeneity information can be utilized to enhance the system performance. To this end, we propose FedDkw – Federated Learning with Dynamic KL-divergence Weight. Specifically, in every round, while uploading the model to the server, each participated client also piggybacks the distribution of the training data. The server maintains a global distribution, which is updated after receiving the distributions of all clients in each round. Then the weight of each client i is proportional to the \(\frac{1}{KL(i)} \) , where KL ( i ) is the Kullback-Leibler (KL) divergence between the client’s distribution and the global counterpart. This indicates that the clients whose distribution is closer to the global one should be assigned with a larger weight, which coincides with our intuition. Furthermore, since uploading the clients’ data distribution in FedDkw may bring about potential security risks, we further propose FedDkw++ to avoid this procedure. In particular, after each client uploads its model, the server uses its local data as the input to the model, and the obtained outcome is used as an estimation of the client’s distribution (we name this method “data distribution inference”). We have conducted extensive experiments in various scenarios. The results show that our algorithm can significantly accelerate the convergence rate than the current state-of-art algorithms under heterogeneous data.
Analysis of Image Processing Strategies Dedicated to Underwater Scenarios

Underwater images undergo quality degradation issues of an image, like blur image, poor contrast, non-uniform illumination etc. Therefore, to process these degraded images, image processing come into existence. In this paper, two important image processing methods namely Image restoration and Image enhancement are compared. This paper also discusses the quality measures parameters of image processing which will be helpful to see clear images.
Teacher's Perspective on The Implementation of Trans-Languaging in Teaching Deaf Students

Trans-languaging in teaching English to deaf students has different practices and perceptions. This study aimed to explore the perspective of a teacher of deaf students in implementing trans-languaging. Interview results of an English teacher and three recordings of the teaching and learning process were analyzed qualitatively in order to get a deep analysis of this research. The teacher was asked several questions about their experience teaching deaf students. The recordings were collected and analyzed to be the supporting data of the interview result. The result showed that the trans-languaging process is a helpful strategy in teaching deaf students because it really bridges the students and the teacher in learning English.&#x0D; Keywords: translanguaging, deaf students
A Content Validity Study on English Reading Comprehension of the Entrance Examination for Senior High School in Xinjiang Province from 2020 to 2022

Based on Bachman and Palmer’s framework of language task characteristics, from perspective of text input and expected response, this paper studies the content validity of English reading comprehension in Senior High School Entrance Examination in Xinjiang Province from 2020 to 2022. It is found that the overall content validity is relatively high and offers some advice to improve. This study is beneficial for test designers, teachers and students to develop their comprehensive abilities in in their respective fields.
The Study of Implementation of Precise Location of Automotive Digital Key Based on UWB

By reviewing the automotive key development history, to implement precise location is an essential way of automotive digital key system. This study decides to implement precise location method based on Ultra Wide Band(UWB) technology, following the guide line of briefly stating the ranging principle of UWB, designing three points location model, constructing UWB location deployment model of automotive cockpit, describing location algorithm and work flow of implementation of UWB location method in detail. This paper analyzes UWB location error which is within the theoretical precision accuracy range proved by test data. The advanced design idea tutors optimizing the algorithm, and the implementation of distributed computing of this location algorithm makes the study achieve the best system resources balance and power management goals. The study also implements precise location of automotive digital key based on UWB. Meanwhile the paper shows that automotive digital key fusing UWB location technology matches the direction of automobile intelligence developing.
A multiorder feature tracking and explanation strategy for explainable deep learning

Abstract A good AI algorithm can make accurate predictions and provide reasonable explanations for the field in which it is applied. However, the application of deep models makes the black box problem, i.e., the lack of interpretability of a model, more prominent. In particular, when there are multiple features in an application domain and complex interactions between these features, it is difficult for a deep model to intuitively explain its prediction results. Moreover, in practical applications, multiorder feature interactions are ubiquitous. To break the interpretation limitations of deep models, we argue that a multiorder linearly separable deep model can be divided into different orders to explain its prediction results. Inspired by the interpretability advantage of tree models, we design a feature representation mechanism that can consistently represent the features of both trees and deep models. Based on the consistent representation, we propose a multiorder feature-tracking strategy to provide a prediction-oriented multiorder explanation for a linearly separable deep model. In experiments, we have empirically verified the effectiveness of our approach in two binary classification application scenarios: education and marketing. Experimental results show that our model can intuitively represent complex relationships between features through diversified multiorder explanations.
QUALITY ASSURANCE OF EDUCATION AT THE HIGHER EDUCATION INSTITUTION BASED ON THE APPLICATION OF AGILE PRACTICES

One of the goals of sustainable development is quality education, which meets the needs of the economy in highly qualified personnel, thereby contributing to the socio-economic development of countries. The article considers the possibilities of introducing a project approach to quality assurance of education: in the management system of a higher educational institution and in the educational process. On the basis of existing technologies in education using Agile tools, and the classical elements of the Agile approach, the components of Agile practices in the educational process are formulated. The study presents an approbation of the use of Agile practices on the basis of training courses of the Department of Project Management and Quality Management of St. Petersburg State University of Economics.
An Efficient Hybrid Conjugate Gradient Algorithm for Solving Intuitionistic Fuzzy Nonlinear Equations

This paper presents an iterative algorithm for solving intuitionistic fuzzy nonlinear equations (IFNEs). The proposed method is based on the classical conjugate gradient (CG) search direction. An interesting feature of the new algorithm is that it considers problems based on the special triangular intuitionistic fuzzy number. For this purpose, intuitionistic fuzzy quantities are transformed into membership and non-membership parametric forms, and a line search procedure is employed to compute the step length. Preliminary results from numerical experiments are presented to demonstrate the performance of the method. It is observed that the proposed hybrid CG method is highly effective and promising. KEYWORDS Hybrid CG, intuitionistic fuzzy nonlinear equation, parametric form, step length, inexact line search
OSH and the COVID-19 pandemic: A legal analysis

This study provides an analysis of how occupational safety and health (OSH) regulation responded to the circumstances of key workers during the COVID-19 pandemic. It explains the objectives of OSH regulation, including its main elements and how it has evolved over time. It draws from national country studies from Africa (Rwanda); Asia (China, Japan, Republic of Korea); Europe (Italy, Spain, United Kingdom); North America (United States) and South America (Brazil, Colombia) to explain how different jurisdictions address safety and health in their regulatory frameworks and how these frameworks operate in practice, including during the COVID-19 pandemic.
Combining ID’s, Attributes, and Policies in Hyperledger Fabric

This work aims to provide a more secure access control in Hyperledger Fabric blockchain by combining multiple ID’s, attributes, and policies with the components that regulate access control. The access control system currently used by Hyperledger Fabric is first completely analyzed. Next, a new implementation is proposed that builds upon the existing solution but provides users and developers with easier ways to make access control decisions based on combinations of multiple ID’s, attributes, and policies. Our proposed implementation encapsulates the Fabric CA client to facilitate attribute addition and simplify the process of registering and enrolling a newly created certificate (corresponding to a new user). This research, concludes that it is possible to combine multiple ID’s, attributes, and policies with the help of Hyperledger Fabric’s smart contract technology. Furthermore, it could be seen that the performance impact for real-world applications is negligible compared to the insecure case of always providing access to a resource without performing access control.
A Privacy-Preserving and Auditable Scheme for Interfacing Public Blockchain with Consortium Blockchain

With the development of blockchain technology, consortium blockchain is being applied in various scenarios. However, data and related assets are restricted to the closed consortium blockchain environment, and the end-users who do not belong to the consortium are difficult to gain access without extra authentication. Thus, architectures concerning cross-chain interaction appear, while most solutions have only limited functionalities. Moreover, few solutions have considered privacy from multiple perspectives, including the privacy of end-users, consortium members, or the data itself. This paper proposes a privacy-preserving and auditable architecture scheme for interfacing consortium blockchain members with end-users of the public blockchain. Our scheme enables end-users to communicate with the inner consortium in a verifiable, privacy-preserving, and auditable manner. More specifically, we improve the existing cross-chain network architectures to further protect the consortium members’ privacy. Also, the communication and the transactions of the cross-chain interaction are protected and auditable. Concrete protocols are proposed, and security models and corresponding analyses are investigated.
ID-Based Self-encryption via Hyperledger Fabric Based Smart Contract

This paper offers a prototype of a Hyperledger Fabric-IPFS based network architecture including a smart contract based encryption scheme that meant to improve the security of user’s data that is being uploaded to the distributed ledger. A new extension to the self-encryption scheme was deployed by integrating data owner’s identity into the encryption process. Such integration allows to permanently preserve ownership of the original file and link it to the person/entity who originally uploaded it. Moreover, self-encryption provides strong security guarantees that decryption of a file is computationally not feasible under the condition that the encrypted file and the key are safely stored.
Improved Retinex-based low light image enhancement algorithm

Low-light image enhancement is one of the most challenging tasks in computer vision. Traditional unsupervised learning methods typically use an image-to-image transformation model to solve low-light image enhancement problems but are unable to suppress the noise prevalent in images captured in real-world low-light conditions. In order to solve a series of degradation problems such as low brightness, high noise and weak contrast of low light images, this paper proposes a new network architecture to enhance the low light images. The whole network includes three sub networks: decomposition, denoising and enhancement. The decomposition network decomposes the image into illumination image and reflection image. The denoising network denoises the reflection image in the frequency domain. The enhancement network enhances the illumination map through several convolution operations. Finally, the denoised reflection image and the enhanced illumination image are multiplied pixel by pixel to obtain the result image. Experiments show that the proposed method in this paper can effectively improve the brightness and contrast, remove noise and it has obvious advantages in subjective and objective evaluation indexes.
Enhancing Load Balancing in Cloud Computing through Adaptive Task Prioritization

Cloud computing has become an increasingly popular platform for modern applications and daily life, and one of its greatest challenges is task scheduling and allocation. Numerous studies have shown that the performance of cloud computing systems relies heavily on arranging tasks in the execution stream on cloud hosts, which is managed by the cloud's load balancer. In this paper, we investigate task priority based on user behavior using request properties and propose an algorithm that utilizes machine learning techniques, namely k-NN and Regression, to classify task-based priorities of requests, facilitate proper allocation, and scheduling of tasks. We aim to enhance load balancing in the cloud by incorporating external factors of the load balancer. The proposed algorithm is experimentally tested on the CloudSim environment, demonstrating improved load balancer performance compared to other popular LB algorithms.
HRIS with Decision Support for Faculty Appraisal and Promotion

Of all the resources of the organization, manpower is the only live generating resource. It is one that utilizes all other resources and as a matter of fact, without which none of the other resources will be able to produce anything. Being such, manpower should be managed properly in such a way that they would be encouraged to be productive and help in the realization of organization’s goals. This study aimed to come up with an online decision support system that would aide the management of Cagayan State University manage its human resources particularly in terms of appraisal and promotion by providing relevant and timely information. The system is designed to streamline processes in the HR department so as to simplify generation of needed analytics for decision support.Moreover, the study utilized classification data mining technique to classify faculty members to appropriate ranks and sub-ranks based on their CCE and QCE points and identify faculty members whose rank did not improve in the past 6 years. Decision tree was also used to predict the faculty performance based on the three (3) consecutive NBC 461 cycle results. This decision support information is essential for the management to know so that necessary intervention can be made to help the faculty member improve and get promoted. With the use of ISO/IEC 25010:2011 Software Quality Standards, the system was evaluated by IT Experts with a mean 4.70, qualitatively described as “Very Great Extent”.
The Current Situation and Countermeasures of College Students' Career Planning Education

In the face of the current grim employment situation in colleges and universities, the employment rate of college students is getting worse and worse.It is an important way to solve the problem of difficult employment to help college students do a good job in career planning education and improve their comprehensive quality and ability.Teachers should play a leading role in students' career planning education and help students find suitable career types, so as to fully guarantee their employment.This article will start with the current situation of career planning education, through interpretation and exploration, form effective countermeasures.
Multiple Perspectives in Semiotics, Illustrated with Tagmemic Analysis of Tic-Tac-Toe

A semiotic analysis of tic-tac-toe illustrates the use of multiple perspec­tives in human understanding of signs. Tagmemic theory is a semiotic framework that pays explicit attention to multiple perspectives. It can generate multiple complementary analyses of the same semiotic system. We illustrate using the game of tic-tac-toe. Our analysis illustrates how three distinct perspectives or views (particle, wave, and field) can be applied to the same semiotic system, resulting in radically different textures in analysis. At the same time, each analysis is in a sense complete, because all the information in the other analyses can be deduced in principle from the analysis using only one view. The result is suggestive for evaluating the strengths and limitations inherent in monoperspectival programs used for artificial intelligence.
Translation Accuracy Correction Algorithm for English Translation Software

With the development of technology, the speed of information technology in many countries continues to increase, and the demand for translation as the main means of communication between different languages has also increased significantly. The purpose of this paper is to study the design of translation accuracy correction algorithm based on English translation software. First, the dependency tree is briefly introduced, and the string bilingual tree dependency model used in this paper is modeled, and the possibility of applying this structure to the Chinese-English machine translation system example of this work is discussed. Based on the built-in Chinese and English dependency tree string library, the traditional similar snapshot retrieval and translation generation methods are improved accordingly, and the similar case retrieval unit and system translation generator are improved. The method of each unit of the system proposed in this work is organically combined to form a complete example of a translation system. The performance of the system has been effectively verified in a large-scale comparative experiment on a training body. In the 8th round of training, the BLEU value of our model reached 23.6.