Reviewerly/exHarmony huggingface dataset:
Stereoscopic Image Quality Evaluation Method for Visual Communication Design

With the development of computer vision technology, the demand for accurate recognition of stereoscopic image quality in the market is increasing. Accurate recognition of stereoscopic image quality is of great significance for providing high-value intelligent image services, which is also the motivation for this study. Accurately recognizing the quality of stereoscopic images is of great significance for image analysis and computer vision applications. In fields such as autonomous driving, medical image analysis, and industrial detection, accurate stereo image quality can provide reliable input for algorithms and improve the accuracy of analysis and recognition. The field or issue of this study is image quality evaluation, which aims to find higher performance stereoscopic image quality evaluation methods. Therefore, this study draws inspiration from the idea of ensemble learning and designs two Convolutional Neural Network (CNN) stereoscopic image quality evaluation methods based on the semantic features of stereoscopic images and the local detail perception module, and fuses them to form a mixed evaluation model. This study aims to solve the problem of image quality assessment, which is to accurately identify the quality of stereoscopic images and provide high-value intelligent image services. With the continuous development of computer vision technology, the demand for accurate recognition of stereoscopic image quality is increasing, which is also the motivation of this study. This study drew inspiration from the idea of ensemble learning and designed two hybrid evaluation models based on stereo image semantic features and local detail perception modules. Convolutional Neural Network (CNN) was used to achieve stereo image quality evaluation. This is one of the main contributions of this article. In order to evaluate the performance of the designed model, the LIVE 3D Phase I dataset was used for testing experiments. The expected results show that when the number of test samples is 500, the overall measurement values of the Spearman rank ordered correlation coefficient (SROCC) and Pearson linear correlation coefficient (PLCC) of the designed ICNN1 and ICNN2 stereo image quality evaluation models are 0.940, 0.949, and 0.940, 0.949, respectively. These results are significantly higher than the selected contrastive deep learning models and machine learning models. In addition, the designed model has relatively low computational time but high computational memory consumption, which is one of the main gaps compared to other studies. In summary, the model designed in this study has great application potential in improving the accuracy of stereo image quality recognition, and is particularly suitable for the Chinese visual design industry. Future research can further explore the market-oriented application of this recognition model.
A Big Data Stream-Driven Risk Recognition Approach for Hospital Accounting Management Systems

This work is confronted with hospital accounting management systems where business volume is usually large and trivial. While designing system prototype and processing algorithms, it is required to integrate realistic big data stream as the main factors for consideration. Because of such point, currently, there still lacks mature solutions for accounting risk recognition in such scenes. Combined with the micro service management technology of data flow, this paper puts forward the risk identification mode and cloud Data integrity verification algorithm for the purpose. Compared with traditional single user authentication techniques, this method has a significantly higher accuracy in hospital data analysis compared to comparative algorithms. At the same time, its error has been reduced. The multi-user parallel authentication algorithm further improves the computational efficiency of the authentication process while ensuring the integrity of data files and reducing the average time. Finally, we also make some empirical analysis on realistic data to testify performance of the proposed technical framework. The results show that the proposal is well suitable for digital risk recognition in hospital accounting management systems. And the recognition accuracy of the proposal can achieve 98%, and is about 22% higher than comparison methods.
Content Order-Controllable MR-to-Text

Content order is critical in natural language generation (NLG) for emphasizing the focus of a generated text passage. In this paper, we propose a novel MR (meaning representation)-to-text method that controls the order of the MR values in a generated text passage based on the given order constraints. We use an MR-text dataset with additional value order annotations to train our order-controllable MR-to-text model. We also use it to train a text-to-MR model to check whether the generated text passage correctly reflects the original MR. Furthermore, we augment the dataset with synthetic MR-text pairs to mitigate the discrepancy in the number of non-empty attributes between the training and test conditions and use it to train another order-controllable MR-to-text model. Our proposed methods demonstrate better NLG performance than the baseline methods without order constraints in automatic and subjective evaluations. In particular, the augmented dataset effectively reduces the number of deletion, insertion, and substitution errors in the generated text passages.
General analysis on essential mathematical principles of elliptic curve cryptography

Prevalent is the practical application of Elliptic Curve Cryptography (ECC) in the modern public-key cryptosystem, especially the implementation of ECC algorithm in Bitcoin source code. With the thorough introduction of discrete logarithm and Diffie-Hellman key exchange, ECC has gradually progressed to be sophisticated and efficient simultaneously. Therefore, it currently has been widely regarded as the successor of RSA algorithm in terms of inheritance for its shorter lengths of keys, faster speed and higher safety under the same encryption strength. Due to the potential safety and complexity of Elliptic Curve Cryptosystem, it is apparently noticed that there is included a large volume of Maths principles related to the establishment of ECC algorithm. As a consequence, this paper will mainly focus on qualitative research and exemplary analysis to specifically elucidate the general knowledge on essential mathematical principles of ECC, including the Law of Addition, the Elliptic Curve Discrete Logarithm Problems (ECDLP) and the Elliptic Curve ElGamal (EC ElGamal), together with the corresponding applications combined with their deprivation processes.
Influence of Career Advancement and Information Sharing on Employees' Engagement in Yemen Banking Sector

Employee engagement can be boosted by good HR practices. Complementary action can be gained by an organisation that provides training, compensation, career development, and equal and fair policies that meet the needs of its employees. Employees become more engaged. To address consumer needs, many service firms rely on information sharing techniques to enhance staff creativity. This study was carried out in Yemen’s banking sector to see if an information sharing system that recorded workers’ creative work improved the quality of their work, workplace engagement, and career development. This study underpinning theory, social exchange theory was used. Smart-PLS was used to evaluate the hypothesis using structural equation modelling using partial least squares. A five-point Likert scale was employed in the questionnaire to quantify all variables for data collection, and the structured questionnaire was utilised to measure the variables.
Design and Development of the Excel Devan Organizer Make-Up Private Course Training Application

In private course training, Makae-Up offers a variety of packagestraining courses so that customers can choose according to their wishes.training, namely non-formal education held outside of learningTraining school is to add insight or knowledgeto that learning where excel devan organizer is foundThe problem with the course training is the CEO (Chief Executive) scheduleOfficer) who clashed on the wedding schedule. from existing problemsbuilt Design and manufacture of information systems Private course trainingmake-up on excel devan organizer Android-based is expected to be able todeal with these problems. for the analysis method using Unfield Modeling Language (UML) and using the programming language Borland Delphi and Mysql as the database with the design and manufacture of a training information system for private make-up courses on Android-based excel devan organizer can be easier to use, accurate and integrated.
Promoting gender inclusion in education through ICT

The research was conducted with a descriptive quantitative approach. The main objective is to investigate the impact of digital tools on gender inclusion and student support in their school education to enhance academic quality. Additionally, the study aims to understand the frequency and level of utilization of these technologies in educational, social, and work-related activities. To achieve the research objective and address the stated question, female students from upper secondary education were specifically selected, representing first, second, and third-grade levels, respectively. Based on the provided data, it is evident that digital technologies play a crucial role in the current educational environment, particularly among younger students. The use of ICT in the learning process is highly valued by the respondents. Most consider that ICT provides them with relevant information, knowledge, and facilitates their learning process. ICT offers a powerful means to transform education and address gender inclusion in the classroom. Its capacity to improve access to education, provide innovative learning opportunities, and promote gender equality is invaluable.
Design and Development of Information System for Monitoring Files of Public Complaints at the Jakarta BPN Office

The National Land Agency (BPN) is a non-ministerial government agency which is under and responsible to the President and led by the Head. The National Land Agency has the task of carrying out governmental tasks in the field of land nationally, regionally and sectorally in accordance with statutory provisions. In the management of monitoring files that are not centralized, the file management process is still manual, searching for files takes about an hour, and physical files have the potential to be lost or damaged. From the existing problems, an information system for monitoring community complaint files was designed and built which is expected to be able to handle these problems. For analysis and design methods use the Unfield Modeling Language (UML). This information system for monitoring public complaint files uses the programming language PHP and Mysql as the database. With this web-based information system for monitoring community complaint files, the processes involved in terms of monitoring are easier to use, accurate and integrated.
The Role of Organizational Challenges and Technical Challenges on the Implementation of E-government Military Institutions

Purpose: This study aimed to find out the challenges and barriers to the implementation of the e-governance in the Yemeni military institutions. A comprehensive literature review was conducted to the&#x0D; study topic. A conceptual framework was proposed that contained two independent variables thatrepresent the challenges, which were (organizational challenges and technical challenges). Thedescriptive analytical approach was used in this study; it is one of the most used methods in the studyof social and human phenomena, and because it fits the phenomenon under study. It also examines anexisting phenomenon or issue from which information can be answered to answer study questions,without the intervention of the researcher. For this research, the instrument used to collect data fromthe research sample in Yemen in regards to the implementation of E-governance. The measurement ofthe direct impact of the independent variables (organizational challenges and technical challenges) onthe implementation of e-governance in military institutions in Yemen. Smart PLS has been used toconduct statistical analysis from the primary data. Several tests have been conducted for the collecteddata. The main resultsfound that there is a negative and significant relationship between organizationalchallenges and the Implementation of E-Governance in the Yemeni military institutions. Also, there is a&#x0D; negative and significant relationship between organizational challenges and the Implementation of E-Governance in the Yemeni military institutions. It is proposed that the organizational and technical&#x0D; challenges for the implementation of the Electronic Government, has the organizational factor, as themain barrier factor, it is recommended that the military institutions implement policies for thedissemination of online services to avoid that the members of the military have to move From theirhome, it is observed that the institutional factor is also important, this can be improved by assigning agreater economic budget to innovate in the development of Electronic
Hybrid Optimization Algorithm for Multi-level Image Thresholding Using Salp Swarm Optimization Algorithm and Ant Colony Optimization

The process of identifying optimal threshold for multi-level thresholding in image segmentation is a challenging process. An efficient optimization algorithm is required to find the optimal threshold and various nature inspired; evolutionary optimization algorithms are presented by the research community. However, to improve the performance in finding optimal threshold value and minimize the error, reduces the searching time a hybrid optimization algorithm is presented in this research work using salp swarm optimization and ant colony optimization algorithm. The ant colony optimization algorithm is used to enhance the exploration and exploitation characteristics of salp swarm optimization in finding optimal threshold for the given image. Experimentation using standard images validates the proposed model performance in comparison with traditional optimization algorithms like moth flame optimization, whale optimization algorithm, grey wolf optimization, artificial bee colony and bee foraging optimization algorithms. Proposed hybrid optimization outperformed in all parameters compared to traditional optimization algorithms and provides better optimal thresholds for the given input image.
HEALNet -- Hybrid Multi-Modal Fusion for Heterogeneous Biomedical Data

Technological advances in medical data collection such as high-resolution histopathology and high-throughput genomic sequencing have contributed to the rising requirement for multi-modal biomedical modelling, specifically for image, tabular, and graph data. Most multi-modal deep learning approaches use modality-specific architectures that are trained separately and cannot capture the crucial cross-modal information that motivates the integration of different data sources. This paper presents the Hybrid Early-fusion Attention Learning Network (HEALNet): a flexible multi-modal fusion architecture, which a) preserves modality-specific structural information, b) captures the cross-modal interactions and structural information in a shared latent space, c) can effectively handle missing modalities during training and inference, and d) enables intuitive model inspection by learning on the raw data input instead of opaque embeddings. We conduct multi-modal survival analysis on Whole Slide Images and Multi-omic data on four cancer cohorts of The Cancer Genome Atlas (TCGA). HEALNet achieves state-of-the-art performance, substantially improving over both uni-modal and recent multi-modal baselines, whilst being robust in scenarios with missing modalities.
Tales of Topological Qubits

Emulating the behavior of exotic quantum states may give quantum computing a better way of squeezing out troublesome noise and errors.
Annotators’ Selection Impact on the Creation of a Sentiment Corpus for the Cryptocurrency Financial Domain

Well labeled natural language corpus data is essential for most natural language processing techniques, especially in specialized fields. However, cohort biases remain a significant challenge in machine learning. The narrow origin of data sampling or human annotators in cohorts is a prevalent issue for machine learning researchers due to its potential to induce bias in the final product. During the development of the CryptoLin corpus for another research project, the authors became concerned about the potential influence of cohort bias on the selection of annotators. Therefore, this paper addresses the question of whether cohort diversity improves the labeling result through the implementation of a repeated annotator process, involving two annotator cohorts and a statistically robust comparison methodology. The utilization of statistical tests, such as the Chi-Square Independence test for absolute frequency tables, and the construction of confidence intervals for Kappa point estimates, facilitates a rigorous analysis of the differences between Kappa estimates. Furthermore, the application of a two-proportion z-test to compare the accuracy scores of UTAD and IE annotators for various pre-trained models, including Vader Sentiment Analysis, TextBlob Sentiment Analysis, Flair NLP library, and FinBERT Financial Sentiment Analysis with BERT, contributes to the advancement of knowledge in this field. The paper utilizes Cryptocurrency Linguo (CryptoLin), a corpus containing 2683 cryptocurrency-related news articles spanning more than three years,and compares two different selection criteria for the annotators. CryptoLin was annotated twice with discrete values representing negative, neutral, and positive news respectively. The first annotation was done by twenty-seven annotators from the same cohort. Each news title was randomly assigned and blindly annotated by three human annotators. The second annotation was carried out by eighty-three annotators from three cohorts. Each news title was randomly assigned and blindly annotated by three human annotators, one in each different cohort. In both annotations, a consensus mechanism using simple voting was applied. The first annotation used the same cohort with students from the same nationality and background. The second used three cohorts with students from a very diverse set of nationalities and educational backgrounds. The results demonstrate that manual labeling done by both groups was acceptable according to inter-rater reliability coefficients Fleiss’s Kappa, Krippendorff’s Alpha, and Gwet’s AC1. Preliminary analysis utilizing Vader, Textblob, Flair, and FinBERT confirmed the utility of the data set labeling for further refinement of sentiment analysis algorithms. Our results also highlight that the more diverse annotator pool performed better in all measured aspects.
Selected Practices Briefs on Application of Teaching Platform and Tool

With the help of the “Min Smart Homework” platform, Zhuanqiao Middle School in Minhang District, Shanghai explores the accurate teaching practice empowered by information technology, accurately grasps the changing trend of learning conditions through the analysis of the collected operation data, locates the weak points in the teaching process, and guides students’ learning and teachers’ teaching.
Explore the Project of Smart Education to Promote Teacher Professional Development

Since its establishment in 2012, Lantian Road Primary School in Bengbu has always been actively exploring smart education with “building smart campus as a paradise for teachers and students” as school management objective. The promotion of smart classroom construction is divided into three stages, including the stage of full staff training, classroom exploration, and comprehensive application. At different stages, the school creatively carries out and designs different measures to promote the application of smart classroom teaching in a targeted manner.
Selected Practices Briefs on Resource Construction and Sharing

With the “Yuncheng Smart Education Cloud Platform”, Yuncheng Haicang School encourages teachers to integrate courseware, micro-lessons, teaching plans, learning sheets and other resources needed in classroom teaching and continuously iterate and optimize resources in the process of resources application, aiming to promote the transformation of school teaching from traditional classrooms to flipped classrooms. The school takes school-based teaching and research innovation as the starting point, integrates, gathers and customizes resources through the innovative small teaching and research model. In other words, the teaching and research group is composed of teachers of the same grade and the same subject; they conduct regular teaching and research activities once a week. Generally, one teacher only prepares for one unit lesson or designated class hours, forming a mode of “individual lesson preparation + collective lesson preparation + individual [re]preparation”, which promotes the construction of normal teaching resources. Through the development of large-scale teaching and research activities, the school promotes the construction of excellent course resources and the reform of teaching and learning methods. That is to say, the teaching and research activities conducted by teachers are within the same disciplines but from different grades for the young teachers. Based on mechanism innovation, business integration and the generation of teaching and research culture, the school gives full play to the wisdom of the teacher group, breaks through the bottleneck of resource construction and allows every teacher to use digital resources to conduct modern teaching. Teachers generate high-quality resources by improving courseware, recording micro-classes and other means to realize the diversification and customization of school-based resources, which effectively solves teachers’ demand for resources. From the statistical table of resource types often used by school teachers, 87.18% of teachers will use multimedia material resources. At present, the application rate of the flipped classroom model in our school has reached 91%. Teachers will send learning sheets and micro-lessons to students before class, use the learning sheet to solve problems in a targeted manner during class and send precise exercises based on students’ classroom feedback after class. These refined resources provide strong support for teaching reform. A questionnaire survey was conducted on 320 students who used school-based resources, and the results showed that 44.38% of them liked the preview resources sent by teachers.
Let Every Child Grow Intelligently and Develop in an All-Round Way

In the construction of the smart campus, the digital portraits of Zongbei Middle School have passed qualitative evaluations in the application of technology, which makes education more targeted, guides, and helps students to “give full play of their strengths” (from good to excellent) + “improve their weaknesses” (from unqualified to qualified and good), and finally lays the foundation for the full development of the students, and promotes the comprehensive and individual growth of the students.
Selected Practices Briefs on New Teaching Model

Beijing First Normal Affiliated Primary School has explored an innovative teaching model that introduces digital stories into primary school mathematics teaching, aiming at the problems of poor learning initiative and low learning engagement of students, with the idea of knowledge re-creation. Before the teaching begins, the teacher first lets the students understand the digital story, clarifies the theme and tasks of this lesson, guides the students to carry out group inquiry learning, clarifies the personnel and division of labor of each group, and guides the students to put forward research ideas and overall planning and design.