Reviewerly/exHarmony huggingface dataset:
Application of Q-learning virtual network and embedded processor in Chinese English translation sentence accuracy analysis

The widely available data is exploding with the continuous development of the Internet, leading people to the era of big data. Nowadays, massive amounts of English information can generate information redundancy, which to some extent affects the user's information browsing and filtering process, and Chinese-English translation needs to be further improved in accuracy. Therefore, it is very important to summarize the information in English source text and accurately translate it into Chinese to help people effectively and quickly grasp the key points and basic knowledge of English articles, among which satisfying sentence accuracy is the most important. Based on this point, this article proposes a new type of Chinese-English translation system, which is equipped with a Q-learning virtual network and built-in processor. With the development of recent years, language recognition technology has become increasingly mature. In a sense, this technology can be said to be the “cornerstone” of fully realizing artificial intelligence technology. Nowadays, the background of embedded processor based language recognition systems is microprocessor platforms (such as chips), which can create language training models in language databases, and can match the models with speech fragments to be tested to complete recognition. In particular, recognizing non specific language has evolved from a laboratory environment to become increasingly practical. This paper designs and implements a class of Chinese-English translation systems based on embedded processors and Q-learning algorithms to optimize system speed and achieve efficient and convenient system processing.
PENGARUH MEDIA MONOPOLI BERBASIS APLIKASI GENIALLY TERHADAP HASIL BELAJAR PESERTA DIDIK KELAS V SD TEMA 6 SUBTEMA 1

Abstract: This study aims to determine the effect of the use of genially application based monopoly media on the learning outcomes of grade V elementary school students theme 6 subtheme 1. This research is a quantitative research, type of Quasi Experiment Design.The research instrument used is a multiple-choice test of 20 questions. Data analysis techniques using t-test. The results of statistical test showed that the average learning outcomes of students using genially application-based monopoly media were 78.13, while the learning outcomes of students using conventional learning models were 69.32. The results of the t-test obtained results were 2.885 &gt; 2.015 and Sig. (2-tailed) values &lt; 0.05 which were 0.006 &lt; 0.05. it was stated that rejected and accepted . Concluded that there is an influence of genially application-based monopoly media on the learning outcomes of grade V elementary school student theme 6 subtheme 1.Keyword: Media Monopoly, Genially, Learning Outcomes Abstrak: Penelitian ini bertujuan untuk mengetahui pengaruh penggunaan media monopoli berbasis aplikasi genially terhadap hasil belajar peserta didik tema 6 subtema 1 kelas V SD. Penelitian ini merupakan penelitian kuantitatif, jenis Quasi Experimen Design tipe Non-Equivalent Control Group. Instrumen penelitian yang digunakan yaitu tes berupa pilihan berganda sebanyak 20 butir soal. Tehnik analisis data menggunakan uji-t. Hasil uji statistik menunjukkan rata-rata hasil belajar peserta didik menggunakan media monopoli berbasis aplikasi genially sebesar 78,13, sedangkan hasil belajar peserta didik ymenggunakan model pembelajaran konvensional sebesar 69,32. Hasil uji-t diperoleh hasil 𝑡ℎ𝑖𝑡𝑢𝑛𝑔 &gt; 𝑡𝑡𝑎𝑏𝑒𝑙 yaitu 2.885 &gt; 2.015 dan nilai Sig.(2-tailed) &lt; 0.05 yaitu 0.006 &lt; 0.05. dinyatakan bahwa 𝐻0 ditolak dan 𝐻𝑎 diterima. Disimpulkan ada pengaruh media monopoli berbasis aplikasi genially terhadap hasil belajar peserta didik kelas V SD tema 6 subtema 1.Kata Kunci: Media Monopoli, Genially, Hasil Belajar
Domain Analysis and Audit of IT Governance Based On COBIT 5 at Denpasar Industrial Training Center

Information technology has become a key element of organizations and one of institutions’ added value and competitive advantages. Therefore, IT must be properly managed and measured. Denpasar Industrial Training Center (BDI) has implemented the IT Governance Education and Training Information System, SISDIKLAT. These applications have never been evaluated from an IT governance perspective. This study aimed to determine domains and assess SISDIKLAT using methods relevant to COBIT 5. To assist the organization in focusing on its main objectives and strategies, a tailored governance system based on the specificities of SISDIKLAT is required. This research assist BDI Denpasar in establishing healthy governance and IT management by utilizing the COBIT 5 framework. Both qualitative and quantitative approaches are used to select relevant governance/management objectives. Four domains and nine subdomains were chosen based on the domain analysis. According to the assessment results, the capability value of each subdomain was 2--3, with a gap value of 0.2--0.8. To reach the target level, the nine subdomains were advised.
Investigating the Impact of Yaw Pose Variation on Facial Recognition Performance

Facial recognition systems often struggle with detecting faces in poses that deviate from the frontal view. Therefore, this paper investigates the impact of variations in yaw poses on the accuracy of facial recognition systems and presents a robust approach optimized to detect faces with pose variations ranging from 0◦ to ±90◦ . The proposed system integrates MTCNN, FaceNet, and SVC, and is trained and evaluated on the Taiwan dataset, which includes face images with diverse yaw poses. The training dataset consists of 89 subjects, with approximately 70 images per subject, and the testing dataset consists of 49 subjects, each with approximately 5 images. Our system achieved a training accuracy of 99.174% and a test accuracy of 96.970%, demonstrating its efficiency in detecting faces with pose variations. These findings suggest that the proposed approach can be a valuable tool in improving facial recognition accuracy in real-world scenarios.
Research on Offline Interaction in English Education from the Perspective of Mobile Media

The popularization and widespread application of mobile media has had a profound impact on English education.The convenience and attractiveness of mobile devices makes students more inclined to use electronic devices for learning, but it can also lead them to neglect interactions with others during the learning process.In the context of mobile media, offline interaction has become particularly important to help students practice language skills, improve their presentation skills, and promote the development of thinking and creativity.However, teachers in the mobile media environment may face some challenges, such as lack of effective teaching strategies and resources to promote offline interaction, this paper aims to explore the mobile media field of English education offline interactive research, through the rational use of mobile media, develop effective teaching strategies and resources, and the strengthening of teacher training and support, can maximize everyone's interaction, improve students' learning outcomes and comprehensive literacy, and promote offline interaction and cooperation among students.
A Chronological Analysis of the Validity of English Cloze Test in Guangdong High School Entrance Examination 2017-2021

The cloze test is a kind of classic test used to test and evaluate students' comprehensive language application ability.Based on Li Xiaoju's test point validity theory, this paper makes a textual and quantitative analysis of the test point validity of cloze tests from 2017 to 2021, in order to provide a reference for experts and teachers.It is finding that the validity of the cloze test questions in the past five years is relatively high for it mainly focuses on the discourse level and meaning factor in the cloze test.For the topic is closely related to students' life and rich in humanities and modernity, it could to some extent arouse students' interest and has positive value guidance for their growth.
Research on Action Obstacles in Practice Guidance of Teachers in Early Childhood Education in Higher Vocational Colleges of China

Teachers' practice guidance is very important in the process of training professionals in early childhood education for children at the age of 0-3 in higher vocational colleges.To promote teachers to better conduct practice guidance, it is necessary to study and remove teachers' action obstacles existing in practice guidance.According to the interviews with 21 teachers in the X College of China, it was found that the teachers were faced with multiple internal and external action obstacles, including weak practice awareness, difficult knowledge structure adjustment, lack of practice quality, insufficient time and energy insufficient attention, lack of support systems, single evaluation mechanisms, unclear industry standards, low admission barriers, and excessive business behavior.
Evaluation and Design of Textbook

English textbook is of great significance in students' language input.The purpose of this research is to evaluate the quality of textbook from the perspective of authenticity, which is related to students' engagement with the tasks in the textbook.The study found that reading tasks may not be authentic to meet students' learning needs.Then some suggestions on material adaptation and supplementation will be given in this paper.
Impact of Risk Management in an Organizational Context

In Portugal, the Council’s Minister Resolution 41/2018, presents a complement to the General Data Protection Regulation (GDPR), which, in order to comply with it, defines technical guidelines for the Public Admin­istration concerning the security architecture of networks and information systems. The objective of the paper is to present risk management in an or­ganizational context. The adopted methodology is focused on the presenta­tion of the research and obligations that the organizations have to take into account before the law and the regulatory authorities. International stand­ards and how they can be applied in the context of the organization un­der study were analyzed, and the main results reached, aim to raise aware­ness within the organizations assertively, for the existing vulnerabilities and threats. Risk management was based on asset management and profes­sional experience acquired over the years, as well as knowledge of internal procedures.
The Institutional Foundation of Economic Development

Institutions matter for economic development. Yet despite this accepted wisdom, new institutional economics has yet to provide a comprehensive look at what constitutes the institutional foundation of economic development (IFED). Bringing together findings from a range a fields, from development economics and development studies to political science and sociology, this book explores the precise mechanisms through which institutions affect growth. The book contends that institutions shape economic development through four “Big Things”: possibility, incentive, capability, and opportunity. From this perspective, IFED has six major dimensions: political hierarchy, property rights, social mobility, redistribution, innovation protection, and equal opportunity. The book further argues that IFED is only one pillar within the New Development Triangle: sustained economic development also requires strong state capacity and sound socioeconomic policies. Arguing for an evolutionary approach tied to a country's stage of development, the book advances an understanding of institutions and economic development through a holistic, interdisciplinary lens.
A Robust and Imperceptible Dark Frame Selection Using Advanced Squeezed Convoluted Ebola Neural Network-Based Video Watermarking Scheme with All Phase Discrete Cosine Biorthogonal Transform

Watermarking has been developed as a beneficial approach to resolve security problems such as copyright defense, legitimate ownership, and authenticity of digital data. Grayscale or binary images and color images are the most important types of embedded watermarks in the existing video watermarking schemes, while dark frame watermarking is barely used. A new robust blind video watermarking system with an Advanced Squeezed Convoluted Ebola Neural Network (ASCENN) approach is suggested to secure copyright, reduce noise, and authenticate the authenticity of dark frames. The proposed method selects the dark frames from the extracted input video. Then the selected dark frames are embedded and extracted using Finite Ridgelet Transform (FRT), All Phase Discrete Cosine Biorthogonal Transform (APDCBT), and Singular Value Decomposition (SVD) techniques. The robustness of the introduced approach is established by evaluating it through various image watermarking techniques and evaluating its performance against geometric, nongeometric, and combinational attacks. While comparing with the other existing video watermarking approaches, the proposed method has obtained a maximum correlation coefficient of 1, and the signal-to-noise ratio of 75[Formula: see text]dB, at minimum error rate of 0.0215, respectively.
EMPLOYEE RESPONSIBILITY AS A HUMAN RESOURCE MANAGEMENT OBJECT: COMPARATIVE ANALYSIS OF CHINESE AND RUSSIAN APPROACHES

The paper presents the results of a desk study of the responsibility at work, the purpose of which is to determine the degree of relevance of personnel responsibility issues, to identify the prevailing perception stereotypes of the responsibility by Chinese and Russian employees. The collection and processing of empirical data was carried out using the methods of quantitative and qualitative content analysis of text arrays in the Internet Chinese and Russian segments. An online survey of Russian enterprises’ employees involved in the poll using the spontaneous sampling method allowed the authors to identify the respondents attitude to the problem under study, as well as comparison of the Chinese and Russian employees’ narratives analysis results has practical importance for finding effective methods and tools for educating responsible employees, developed on the best practices basis. One of the key conclusions based on the study results is the disproportion between the degree of Russian specialists’ attention to the issues of personnel responsibility management and the significance of the influence of the employee responsibility factor on the organization’s performance indicators.
Adaptive Learning of Radial Basis Function Neural Networks Based on Traffic Sign Recognition using Principal Component Analysis

Using the PCA and RBF neural networks developed in this study, it was possible to develop a practical method for recognising traffic signs.PCA has been used in traffic sign recognition algorithms for several years.It is among an autonomous driving system's most prevalent image representation techniques.The picture is not only reduced in dimensionality, but some of the fluctuations in the digital image and the image data are retained.It is true that when PCA was completed, the RBF neural netts' hidden node neurones were modelled using the training images' intra-class discrimination qualities in the hidden layer neurone.RBF neural networks benefit from this because it allows them to acquire a wide range of changes observed in the lowdimensional feature space, increasing their generalisation capabilities.The suggested approach is tested on different template traffic signs, with positive results.Results from the experiments demonstrate that the suggested technique has a promising recognition performance.
An Efficient High-Dimensional Data Indexing Scheme Using Vector Approximation on Spark

최근 실시간 영상 처리 기술의 발달로 보안, 자율 주행 자동차, 의료 분야, 산업용 로봇 등 다양한 응용 분야에서 내용 기반 이미지 검색 기술이 연구되고 있다. 이러한 내용 기반 이미지 검색에서는 이미지로부터 추출한 특징이 고차원이며, 이에 따라 차원의 저주 문제를 갖는다. 본 논문에서는 기존의 벡터 근사화(vector approximation) 파일을 확장하여 고차원 데이터에서 차원의 저주 문제를 해결하는 효율적인 고차원 데이터 색인 기법을 제안한다. 제안하는 기법은 질의 처리 부하를 분배하기 위해 분산 처리 엔진인 아파치 스파크에서 구현된다. 제안하는 VA-File 기반의 분할 키 생성 방법인 VA-Key를 활용하여 각 VA-File마다의 유사도를 1차원 값으로 나타내어 보다 효과적인 데이터 분할을 수행할 수 있다. 마지막으로 다양한 성능 평가를 통해 제안하는 기법이 기존의 벡터 검색 기법인 KD-Tree, LSH(Locality Sensitive Hashing) 그리고 가장 인기있는 벡터 검색 도구인 FAISS 등과 실험을 수행했으며 실험 결과 질의 처리 시간과 정확도 관점에서 기존 기법에 비해 우수함을 보이며 특히, 고차원 데이터에서의 재현율과 처리 속도는 다른 기법보다 우수한 성능을 보인다. With the recent development of real-time image processing technology, content-based image retrieval has been studied in various applications such as security, autonomous vehicles, medical fields, and industrial robots. In content-based image retrieval, features extracted from images are high-dimensional, resulting in a dimensionality curse problem. In this paper, we propose an efficient high-dimensional data indexing scheme that solves the dimensionality curse problem of high-dimensional data by extending the existing vector approximation technique. Using VA-Key, a proposed VA-File-based split key generation method, it is possible to perform more effective data partition with the similarity of each VA-File as a one-dimensional value. It is shown through various performance evaluations with existing vector search techniques such as KD-Tree, Local Sensitive Hashing (LSH), and FAISS, the most popular vector search tool that the proposed scheme outperforms the existing scheme in terms of query processing time and accuracy.
The Effect of Social Capital on Different Innovations Under the Pandemic in Chinese Manufacturing Supply Chains: A Literature Review

In light of the unique social and cultural characteristics in China and the limited understanding of the relationship between social capital and innovation, this study aims to investigate the impact of social capital on product innovation and process innovation through a comprehensive review of relevant literature.The methodology employed for this study involves conducting a contextual analysis of existing literature from various databases.The results of the review confirm the positive influence of social capital, including its dimensions, in facilitating both product innovation and process innovation.The findings of this study are expected to contribute to the knowledge of scholars and manufacturers, providing them with a clearer understanding of the relationship between social capital and innovation, as well as guiding the establishment of competitive supply chains especially under the pandemic.Furthermore, this study also discusses the indicators and distinctions between product innovation and process innovation.
Artificial Level Language: A Library of Computing Engine for Natural Languages*

Programming with natural language is an area of research with different kinds of applications including introductory programming, robotics, etc. Factors like meaning preservation, ambiguity handling, etc, have to be considered while writing programs in natural language. Natural language programming is evolved over the past years through various developments. Here we introduce a system that can convert sentences written in natural language (we considered English as an example) to python statements. The main feature of our system is that the English sentences need not be grammatically correct. We named our system as Artificial Level Language (ALL) as it can be thought of as a new level, above the high-level language, in the programming language hierarchy. The main component of ALL is a tranSMachine comprised of two parts namely tranScripter and transCompiler. The tranScripter introduced here is named as JAVS-tranScripter which includes a set of protocols that are expected to be improved in the future. ALL take English statements as input and it generates Python code in the background to carry out the instructions and outputs the results.
How Federated Machine Learning Helps Increase the Mutual Benefit of Data-Sharing Ecosystems

Nowadays, data-sharing ecosystems are crucial for unlocking and realizing the maximum potential of data. Data spaces are an emergent concept that helps to overcome some of the challenges related to data sharing and supports the creation of innovative solutions in a trustful and mutually beneficial manner. This paper shows how competing companies in the mobility domain can collaborate toward optimizing the performance of a traffic prediction algorithm through implementing federated machine learning in a data space. The proposed method avoids sensitive data sharing by executing machine learning algorithms within the private environments of competing companies while only the trained model instances are shared. The approach has various applications beyond the one presented in the paper.
Special Quantum Steganalysis Algorithm for Quantum Secure Communications Based on Quantum Discriminator

The remarkable advancement of quantum steganography offers enhanced security for quantum communications.However, there is a significant concern regarding the potential misuse of this technology.Moreover, the current research on identifying malicious quantum steganography is insufficient.To address this gap in steganalysis research, this paper proposes a specialized quantum steganalysis algorithm.This algorithm utilizes quantum machine learning techniques to detect steganography in general quantum secure communication schemes that are based on pure states.The algorithm presented in this paper consists of two main steps: data preprocessing and automatic discrimination.The data preprocessing step involves extracting and amplifying abnormal signals, followed by the automatic detection of suspicious quantum carriers through training on steganographic and non-steganographic data.The numerical results demonstrate that a larger disparity between the probability distributions of steganographic and non-steganographic data leads to a higher steganographic detection indicator, making the presence of steganography easier to detect.By selecting an appropriate threshold value, the steganography detection rate can exceed 90%.
Surface structured light sensor with simultaneous color mapping

Abstract To obtain geometric information and color texture simultaneously, a surface structured light sensor consisting of a monochrome camera, a color camera, and a projector is proposed. The sensor uses a color camera to acquire surface color information while using it as a geometric measurement unit and matching with the monochrome camera to obtain geometric information. Due to the Bayer array and demosaicing algorithm of the color camera, pixel RGB components are always coupled with interference from other channels. However, existing color de-crosstalk in reconstruction is merely applied to the decoupling of color composite patterns, ignoring the intensity errors present in color fringe patterns under monochrome illumination. In our sensor, de-crosstalk of monochromatic patterns is considered to guarantee the reconstruction accuracy. The high-accuracy measurement of the sensor is validated by reconstructing standard steps, yielding a mean absolute error of 0.008 mm for distance measurements. In addition, the reconstruction experiment of a terracotta warrior verifies that the proposed sensor has potential application in the digital preservation of cultural relics.